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  • 151.
    Hjelm, Martin
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, Renaud
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kjellström, Hedvig
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Sparse Summarization of Robotic Grasping Data2013Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, s. 1082-1087Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.

  • 152.
    Hjelm, Martin
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Ek, Carl Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Detry, Renaud
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Learning Human Priors for Task-Constrained Grasping2015Ingår i: COMPUTER VISION SYSTEMS (ICVS 2015), Springer Berlin/Heidelberg, 2015, s. 207-217Konferensbidrag (Refereegranskat)
    Abstract [en]

    An autonomous agent using manmade objects must understand how task conditions the grasp placement. In this paper we formulate task based robotic grasping as a feature learning problem. Using a human demonstrator to provide examples of grasps associated with a specific task, we learn a representation, such that similarity in task is reflected by similarity in feature. The learned representation discards parts of the sensory input that is redundant for the task, allowing the agent to ground and reason about the relevant features for the task. Synthesized grasps for an observed task on previously unseen objects can then be filtered and ordered by matching to learned instances without the need of an analytically formulated metric. We show on a real robot how our approach is able to utilize the learned representation to synthesize and perform valid task specific grasps on novel objects.

  • 153.
    Hyttinen, Emil
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Adaptive Grasping Using Tactile Sensing2017Licentiatavhandling, monografi (Övrigt vetenskapligt)
    Abstract [sv]

    Att greppa nya föremål är utmanande, både eftersom roboten inte har fullständig information om objekten och på grund av den inneboende osäkerheten i verkliga tillämpningar. Återkoppling från känselsensorer är viktigt för att kunna greppa föremål som inte påträffats tidigare. I vår forskning så studerar vi hur information från känselsensorer kan användas för att förbättra greppandet av nya föremål. Eftersom det är svårt att extrahera relevanta egenskaper om föremål och härleda lämpliga åtgärder, baserat på känselsensorer, så har vi använt maskininlärning för att lära roboten lämpliga beteenden. Vi har visat att uppskattningar av stabiliteten av ett grepp baserat på känselsensorer kan förbättras genom att även använda en grov approximation av föremålets form. Vi har även konstruerat en metod som vägleder lokala justeringar av grepp, baserat på vår metod som uppskattar stabiliteten av ett grepp. Dess justeringar hittas genom att simulera känselsensordata för grepp i närheten av det nuvarande greppet. Vi presenterar flera experiment som demonstrerar tillämpbarheten av våra metoder. Avhandlingen avslutas med en diskussion om våra resultat och förslag på möjliga ämnen för fortsatt forskning.

  • 154.
    Irfan, Bahar
    et al.
    Univ Plymouth, Ctr Robot & Neural Syst, Plymouth, Devon, England..
    Ramachandran, Aditi
    Yale Univ, Social Robot Lab, New Haven, CT 06520 USA..
    Spaulding, Samuel
    MIT, Personal Robots Grp, Media Lab, Cambridge, MA 02139 USA..
    Glas, Dylan F.
    Huawei, Futurewei Technol, Santa Clara, CA USA..
    Leite, Iolanda
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Koay, Kheng Lee
    Univ Hertfordshire, Adapt Syst Res Grp, Hatfield, Herts, England..
    Personalization in Long-Term Human-Robot Interaction2019Ingår i: HRI '19: 2019 14TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION, IEEE , 2019, s. 685-686Konferensbidrag (Refereegranskat)
    Abstract [en]

    For practical reasons, most human-robot interaction (HRI) studies focus on short-term interactions between humans and robots. However, such studies do not capture the difficulty of sustaining engagement and interaction quality across long-term interactions. Many real-world robot applications will require repeated interactions and relationship-building over the long term, and personalization and adaptation to users will be necessary to maintain user engagement and to build rapport and trust between the user and the robot. This full-day workshop brings together perspectives from a variety of research areas, including companion robots, elderly care, and educational robots, in order to provide a forum for sharing and discussing innovations, experiences, works-in-progress, and best practices which address the challenges of personalization in long-term HRI.

  • 155.
    Jacobsson, Mattias
    SICS.
    Play, Belief and Stories about Robots: A Case Study of a Pleo Blogging Community2009Ingår i: Proceedings of RO-MAN 2009, NEW YORK: IEEE , 2009, s. 830-835Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    We present an analysis based on user-provided content collected from online blogs and forums about the robotic artifact Pleo. Our primary goal is to explore stories about how human-robot interaction would manifest themselves in actual real-world contexts. To be able to assess these types of communicative media we are using a method based on virtual ethnography that specifically addresses underlying issues in how the data is produced and should be interpreted. Results indicate that generally people are staging, performing and have a playful approach to the interaction. This is further emphasized by the way people communicate their stories through the blogging practice. Finally we argue that these resources are indeed essential for understanding and designing long-term human-robot relationships.

  • 156.
    Jacobsson, Mattias
    et al.
    SICS.
    Fernaeus, Ylva
    KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.
    Cramer, Henriette
    Ljungblad, Sara
    Crafting against robotic fakelore: on the critical practice of artbot artists2013Ingår i: Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery (ACM), 2013, Vol. 2013, s. 2019-2028Konferensbidrag (Refereegranskat)
    Abstract [en]

    We report on topics raised in encounters with a series of robotics oriented artworks, which to us were interpreted as a general critique to what could be framed as robotic fakelore, or mythology. We do this based on interviews held with artists within the community of ArtBots, and discuss how their approach relates to and contributes to the discourse of HCI. In our analysis we outline a rough overview of issues emerging in the interviews and reflect on the broader questions they may pose to our research community.

  • 157.
    Jansson, Ylva
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields2017Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This work presents a first evaluation of using spatiotemporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition.

    The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.

  • 158.
    Jansson, Ylva
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Lindeberg, Tony
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields2018Ingår i: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 60, nr 9, s. 1369-1398Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatiotemporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state of the art. In particular, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.

  • 159.
    Jansson, Ylva
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
    Dynamic texture recognition using time-causal spatio-temporal scale-space filters2017Ingår i: Scale Space and Variational Methods in Computer Vision, Springer, 2017, Vol. 10302, s. 16-28Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents an evaluation of using time-causal scale-space filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatiotemporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain. We evaluate one member in this family, constituting a joint binary histogram, on two widely used dynamic texture databases. The experimental evaluation shows competitive performance compared to previous methods for dynamic texture recognition, especially on the more complex DynTex database. These results support the descriptive power of time-causal spatio-temporal scale-space filters as primitives for video analysis.

  • 160.
    Johansson, Johan
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Solli, Martin
    FLIR Systems AB.
    Maki, Atsuto
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    An Evaluation of Local Feature Detectors and Descriptors for Infrared Images2016Ingår i: Lecture Notes in Computer Science, Volume 9915, 2016, s. 711-723Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper provides a comparative performance evaluation of local features for infrared (IR) images across different combinations of common detectors and descriptors. Although numerous studies report comparisons of local features designed for ordinary visual images, their performance on IR images is far less charted. We perform a systematic investigation, thoroughly exploiting the established benchmark while also introducing a new IR image data set. The contribution is two-fold: we (i) evaluate the performance of both local float type and more recent binary type detectors and descriptors in their combinations under a variety (6 kinds) of image transformations, and (ii) make a new IR image data set publicly available. Through our investigation we gain novel and useful insights for applying state-of-the art local features to IR images with different properties.

  • 161.
    Joyce, Peter James
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Hållbar utveckling, miljövetenskap och teknik, Hållbarhet, utvärdering och styrning.
    Computer vision for LCA foreground modelling – an initial pipeline and proof of concept software, lcopt-cvManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Purpose

    The majority of LCA studies begin with the drawing of a process flow diagram, which then needs to be translated manually into an LCA model. This study presents an initial image processing pipeline, implemented in an open source software package, called lcopt-cv, which can be used to identify the boxes and links in a photograph of a hand-drawn process flow diagram and automatically create an LCA foreground model.

    Methods

    The computer vision pipeline consists of a total of fifteen steps, beginning with loading the image file and conversion to greyscale. The background is equalised, then the foreground of the image is extracted from the background using thresholding. The lines are then dilated and closed to account for drawing errors. Contours in the image are detected and simplified, and rectangles (contours with four corners) are identified from the simplified contours as ‘boxes’. Links between these boxes are identified using a flood-filling technique. Heuristic processing, based on knowledge of common practice in drawing of process flow diagrams is then performed to more accurately identify the typology of the identified boxes and the direction of the links between them.

    Results and Discussion

    The performance of the image processing pipeline was tested on four different flow charts of increasing difficulty: A simple flow chart drawn in MS PowerPoint and saved as an image, followed by three photographs of hand-drawn flow charts. These consisted of a simple flow chart, a complex flow chart and a deliberately difficult example with merged lines. A set of default parameters for the pipeline was developed through trial and error. With minor tweaks to the default parameters, possible through the user interface of lcopt-cv, all of the flow charts were capable of being correctly processed and turned into LCA models. These models were successfully exported to further open source LCA software packages (lcopt and Brightway) to be analysed.

    Conclusions

    This study demonstrates that it is possible to generate a fully functional LCA model from a picture of a flow chart. This has potentially important implications not only for LCA practitioners as a whole, but in particular for the teaching of LCA. Skipping the steep learning curve required by most LCA software packages allows teachers to focus on important LCA concepts, while participants maintain the benefits of experiential learning by doing a ‘real’ LCA.

  • 162.
    Kakooei, Mohammad
    et al.
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik. Babol Noshirvani Univ Technol, Babol Sar, Iran..
    Nascetti, Andrea
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik.
    Ban, Yifang
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geoinformatik.
    SENTINEL-1 GLOBAL COVERAGE FORESHORTENING MASK EXTRACTION: AN OPEN SOURCE IMPLEMENTATION BASED ON GOOGLE EARTH ENGINE2018Ingår i: IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IEEE , 2018, s. 6836-6839Konferensbidrag (Refereegranskat)
    Abstract [en]

    It is well known that SAR imagery is affected by SAR geometric distortions due to the SAR imaging process (i.e. Layover, Foreshortening and Shadows). Specially in mountainous areas these distortions affect large portions of the images and in some applications, these areas shouldn't be included in analysis. Using a foreshortening mask is a suitable solution, but finding the mask is challenging. The aim of this research is to exploit the fusion of Sentinel-1 multi-temporal images and SRTM DEM to produce a quasi-global foreshortening mask using the Google Earth Engine (GEE), cloud-based platform. The mean value of multi-temporal Sentinel-1 images is calculated. Then a local minimum algorithm finds probable foreshortening area. Aspect and slope information from SRTM DEM are used to refine Sentinel-1 derived foreshortening mask. The proposed method is tested in British Columbia (Canada), Everest Mountain (Nepal), and Mazandaran (Iran). The results demonstrate the reliability of proposed method to detect the foreshortening area.

  • 163.
    Karaoguz, Hakan
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bore, Nils
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Folkesson, John
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Jensfelt, Patric
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Human-Centric Partitioning of the Environment2017Ingår i: 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), IEEE, 2017, s. 844-850Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present an object based approach for human-centric partitioning of the environment. Our approach for determining the human-centric regionsis to detect the objects that are commonly associated withfrequent human presence. In order to detect these objects, we employ state of the art perception techniques. The detected objects are stored with their spatio-temporal information inthe robot’s memory to be later used for generating the regions.The advantages of our method is that it is autonomous, requires only a small set of perceptual data and does not even require people to be present while generating the regions.The generated regions are validated using a 1-month dataset collected in an indoor office environment. The experimental results show that although a small set of perceptual data isused, the regions are generated at densely occupied locations.

  • 164.
    Karayiannidis, Yiannis
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Smith, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Vina, Francisco
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Online Contact Point Estimation for Uncalibrated Tool Use2014Ingår i: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, s. 2488-2493Konferensbidrag (Refereegranskat)
    Abstract [en]

    One of the big challenges for robots working outside of traditional industrial settings is the ability to robustly and flexibly grasp and manipulate tools for various tasks. When a tool is interacting with another object during task execution, several problems arise: a tool can be partially or completely occluded from the robot's view, it can slip or shift in the robot's hand - thus, the robot may lose the information about the exact position of the tool in the hand. Thus, there is a need for online calibration and/or recalibration of the tool. In this paper, we present a model-free online tool-tip calibration method that uses force/torque measurements and an adaptive estimation scheme to estimate the point of contact between a tool and the environment. An adaptive force control component guarantees that interaction forces are limited even before the contact point estimate has converged. We also show how to simultaneously estimate the location and normal direction of the surface being touched by the tool-tip as the contact point is estimated. The stability of the the overall scheme and the convergence of the estimated parameters are theoretically proven and the performance is evaluated in experiments on a real robot.

  • 165.
    Kazemi, Vahid
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Correspondence Estimation in Human Face and Posture Images2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Many computer vision tasks such as object detection, pose estimation,and alignment are directly related to the estimation of correspondences overinstances of an object class. Other tasks such as image classification andverification if not completely solved can largely benefit from correspondenceestimation. This thesis presents practical approaches for tackling the corre-spondence estimation problem with an emphasis on deformable objects.Different methods presented in this thesis greatly vary in details but theyall use a combination of generative and discriminative modeling to estimatethe correspondences from input images in an efficient manner. While themethods described in this work are generic and can be applied to any object,two classes of objects of high importance namely human body and faces arethe subjects of our experimentations.When dealing with human body, we are mostly interested in estimating asparse set of landmarks – specifically we are interested in locating the bodyjoints. We use pictorial structures to model the articulation of the body partsgeneratively and learn efficient discriminative models to localize the parts inthe image. This is a common approach explored by many previous works. Wefurther extend this hybrid approach by introducing higher order terms to dealwith the double-counting problem and provide an algorithm for solving theresulting non-convex problem efficiently. In another work we explore the areaof multi-view pose estimation where we have multiple calibrated cameras andwe are interested in determining the pose of a person in 3D by aggregating2D information. This is done efficiently by discretizing the 3D search spaceand use the 3D pictorial structures model to perform the inference.In contrast to the human body, faces have a much more rigid structureand it is relatively easy to detect the major parts of the face such as eyes,nose and mouth, but performing dense correspondence estimation on facesunder various poses and lighting conditions is still challenging. In a first workwe deal with this variation by partitioning the face into multiple parts andlearning separate regressors for each part. In another work we take a fullydiscriminative approach and learn a global regressor from image to landmarksbut to deal with insufficiency of training data we augment it by a large numberof synthetic images. While we have shown great performance on the standardface datasets for performing correspondence estimation, in many scenariosthe RGB signal gets distorted as a result of poor lighting conditions andbecomes almost unusable. This problem is addressed in another work wherewe explore use of depth signal for dense correspondence estimation. Hereagain a hybrid generative/discriminative approach is used to perform accuratecorrespondence estimation in real-time.

  • 166.
    Kazemi, Vahid
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Burenius, Magnus
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Azizpour, Hossein
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Sullivan, Josephine
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Multi-view body part recognition with random forests2013Ingår i: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, Bristol, England: British Machine Vision Association , 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper addresses the problem of human pose estimation, given images taken from multiple dynamic but calibrated cameras. We consider solving this task using a part-based model and focus on the part appearance component of such a model. We use a random forest classifier to capture the variation in appearance of body parts in 2D images. The result of these 2D part detectors are then aggregated across views to produce consistent 3D hypotheses for parts. We solve correspondences across views for mirror symmetric parts by introducing a latent variable. We evaluate our part detectors qualitatively and quantitatively on a dataset gathered from a professional football game.

  • 167.
    Kazemi, Vahid
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Josephine, Sullivan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    One Millisecond Face Alignment with an Ensemble of Regression Trees2014Ingår i: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2014, s. 1867-1874Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.

  • 168.
    Kazemi, Vahid
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. Microsoft Research, United States.
    Keskin, Cem
    Microsoft Research, United States.
    Taylor, Jonathan
    Microsoft Research, United States.
    Kholi, Pushmeet
    Microsoft Research, United States.
    Izadi, Shahram
    Microsoft Research, United States.
    Real-time Face Reconstruction from a Single Depth Image2014Ingår i: Proceedings - 2014 International Conference on 3D Vision, 3DV 2014, IEEE conference proceedings, 2014, s. 369-376Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper contributes a real time method for recovering facial shape and expression from a single depth image. The method also estimates an accurate and dense correspondence field between the input depth image and a generic face model. Both outputs are a result of minimizing the error in reconstructing the depth image, achieved by applying a set of identity and expression blend shapes to the model. Traditionally, such a generative approach has shown to be computationally expensive and non-robust because of the non-linear nature of the reconstruction error. To overcome this problem, we use a discriminatively trained prediction pipeline that employs random forests to generate an initial dense but noisy correspondence field. Our method then exploits a fast ICP-like approximation to update these correspondences, allowing us to quickly obtain a robust initial fit of our model. The model parameters are then fine tuned to minimize the true reconstruction error using a stochastic optimization technique. The correspondence field resulting from our hybrid generative-discriminative pipeline is accurate and useful for a variety of applications such as mesh deformation and retexturing. Our method works in real-time on a single depth image i.e. without temporal tracking, is free from per-user calibration, and works in low-light conditions.

  • 169.
    Kazemi, Vahid
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Sullivan, Josephine
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Face Alignment with Part-Based Modeling2011Ingår i: BMVC 2011 - Proceedings of the British Machine Vision Conference 2011 / [ed] Hoey, Jesse and McKenna, Stephen and Trucco, Emanuele, UK: British Machine Vision Association, BMVA , 2011, s. 27.1-27.10Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose a new method for face alignment with part-based modeling. This method is competitive in terms of precision with existing methods such as Active Appearance Models, but is more robust and has a superior generalization ability due to its part-based nature. A variation of the Histogram of Oriented Gradients descriptor is used to model the appearance of each part and the shape information is represented with a set of landmark points around the major facial features. Multiple linear regression models are learnt to estimate the position of the landmarks from the appearance of each part. We verify our algorithm with a set of experiments on human faces and these show the competitive performance of our method compared to existing methods.

  • 170.
    Kazemi, Vahid
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Sullivan, Josephine
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Using Richer Models for Articulated Pose Estimation of Footballers2012Ingår i: Proceedings British Machine Vision Conference 2012., 2012, s. 6.1-6.10Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a fully automatic procedure for reconstructing the pose of a person in 3Dfrom images taken from multiple views. We demonstrate a novel approach for learningmore complex models using SVM-Rank, to reorder a set of high scoring configurations.The new model in many cases can resolve the problem of double counting of limbswhich happens often in the pictorial structure based models. We address the problemof flipping ambiguity to find the correct correspondences of 2D predictions across allviews. We obtain improvements for 2D prediction over the state of art methods on ourdataset. We show that the results in many cases are good enough for a fully automatic3D reconstruction with uncalibrated cameras.

  • 171. Khan, M. S. L.
    et al.
    ur Réhman, S.
    Mi, Y.
    Naeem, U.
    Beskow, Jonas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH.
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.
    Moveable facial features in a social mediator2017Ingår i: 17th International Conference on Intelligent Virtual Agents, IVA 2017, Springer, 2017, Vol. 10498, s. 205-208Konferensbidrag (Refereegranskat)
    Abstract [en]

    Human face and facial features based behavior has a major impact in human-human communications. Creating face based personality traits and its representations in a social robot is a challenging task. In this paper, we propose an approach for a robotic face presentation based on moveable 2D facial features and present a comparative study when a synthesized face is projected using three setups; 1) 3D mask, 2) 2D screen, and 3) our 2D moveable facial feature based visualization. We found that robot’s personality and character is highly influenced by the projected face quality as well as the motion of facial features.

  • 172. Khong, S. Z.
    et al.
    Cantoni, M.
    Jönsson, Ulf
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Optimeringslära och systemteori.
    Robust stability properties of the v-gap metric for time-varying systems2011Ingår i: Proc IEEE Conf Decis Control, 2011, s. 2028-2033Konferensbidrag (Refereegranskat)
    Abstract [en]

    The stability of uncertain feedback interconnections of causal time-varying linear systems is studied in terms of a recently established generalisation of the v-gap metric. In particular, a number of robustness results from the well-known linear time-invariant theory are extended. The time-varying generalisations include: sufficient conditions for robust stability; a bound on robust performance; and two-sided bounds on the induced norm of the variation in a closed-loop mapping as an open-loop component of the feedback interconnection is perturbed. Underlying assumptions are verified for causal systems that exhibit linear periodically time-varying behaviour. This includes a class of sampled-data systems as a special case. Within the periodic context considered, it can be shown that a robust stability condition is also necessary.

  • 173. King, Jennifer
    et al.
    Haustein, Joshua Alexander
    High Performance Humanoid Technologies, Karlsruhe Insitute of Technology.
    Srinivasa, Siddhartha S.
    Asfour, Tamim
    High Performance Humanoid Technologies, Karlsruhe Institute of Technology.
    Nonprehensile whole arm rearrangement planning on physics manifolds2015Ingår i: 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015, s. 2508-2515Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a randomized kinodynamic planner that solves rearrangement planning problems. We embed a physics model into the planner to allow reasoning about interaction with objects in the environment. By carefully selecting this model, we are able to reduce our state and action space, gaining tractability in the search. The result is a planner capable of generating trajectories for full arm manipulation and simultaneous object interaction. We demonstrate the ability to solve more rearrangement by pushing tasks than existing primitive based solutions. Finally, we show the plans we generate are feasible for execution on a real robot.

  • 174.
    Klasson, Marcus
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Zhang, C.
    Kjellström, Hedvig
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    A hierarchical grocery store image dataset with visual and semantic labels2019Ingår i: Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, s. 491-500, artikel-id 8658240Konferensbidrag (Refereegranskat)
    Abstract [en]

    Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application – classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.

  • 175.
    Kootstra, Geert
    et al.
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    de Jong, Sjoerd
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Schomaker, Lambert R. B.
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Using local symmetry for landmark selection2009Ingår i: Computer Vision Systems, Springer , 2009, Vol. 5815, s. 94-103Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Most visual Simultaneous Localization And Mapping (SLAM) methods use interest points as landmarks in their maps of the environment. Often the interest points are detected using contrast features, for instance those of the Scale Invariant Feature Transform (SIFT). The SIFT interest points, however, have problems with stability, and noise robustness. Taking our inspiration from human vision, we therefore propose the use of local symmetry to select interest points. Our method, the MUlti-scale Symmetry Transform (MUST), was tested on a robot-generated database including ground-truth information to quantify SLAM performance. We show that interest points selected using symmetry are more robust to noise and contrast manipulations, have a slightly better repeatability, and above all, result in better overall SLAM performance.

  • 176.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bergström, Niklas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Fast and Automatic Detection and Segmentation of Unknown Objects2010Ingår i: Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), IEEE , 2010, s. 442-447Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper focuses on the fast and automatic detection and segmentation of unknown objects in unknown environments. Many existing object detection and segmentation methods assume prior knowledge about the object or human interference. However, an autonomous system operating in the real world will often be confronted with previously unseen objects. To solve this problem, we propose a segmentation approach named Automatic Detection And Segmentation (ADAS). For the detection of objects, we use symmetry, one of the Gestalt principles for figure-ground segregation to detect salient objects in a scene. From the initial seed, the object is segmented by iteratively applying graph cuts. We base the segmentation on both 2D and 3D cues: color, depth, and plane information. Instead of using a standard grid-based representation of the image, we use super pixels. Besides being a more natural representation, the use of super pixels greatly improves the processing time of the graph cuts, and provides more noise-robust color and depth information. The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods.

  • 177.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bergström, Niklas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Gestalt Principles for Attention and Segmentation in Natural and Artificial Vision Systems2011Ingår i: Semantic Perception, Mapping and Exploration (SPME), ICRA 2011 Workshop, eSMCs , 2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    Gestalt psychology studies how the human visual system organizes the complex visual input into unitary elements. In this paper we show how the Gestalt principles for perceptual grouping and for figure-ground segregation can be used in computer vision. A number of studies will be shown that demonstrate the applicability of Gestalt principles for the prediction of human visual attention and for the automatic detection and segmentation of unknown objects by a robotic system.

  • 178.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Bergström, Niklas
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Using Symmetry to Select Fixation Points for Segmentation2010Ingår i: Proceedings of the 20th International Conference on Pattern Recognition, IEEE , 2010, s. 3894-3897Konferensbidrag (Refereegranskat)
    Abstract [en]

    For the interpretation of a visual scene, it is important for a robotic system to pay attention to the objects in the scene and segment them from their background. We focus on the segmentation of previously unseen objects in unknown scenes. The attention model therefore needs to be bottom-up and context-free. In this paper, we propose the use of symmetry, one of the Gestalt principles for figure-ground segregation, to guide the robot’s attention. We show that our symmetry-saliency model outperforms the contrast-saliency model, proposed in. The symmetry model performs better in finding the objects of interest and selects a fixation point closer to the center of the object. Moreover, the objects are better segmented from the background when the initial points are selected on the basis of symmetry.

  • 179.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Fast and Bottom-Up Object Detection and Segmentation using Gestalt Principles2011Ingår i: Proceedings of the International Conference on Robotics and Automation (ICRA), IEEE , 2011, s. 3423-3428Konferensbidrag (Refereegranskat)
    Abstract [en]

    In many scenarios, domestic robot will regularly encounter unknown objects. In such cases, top-down knowledge about the object for detection, recognition, and classification cannot be used. To learn about the object, or to be able to grasp it, bottom-up object segmentation is an important competence for the robot. Also when there is top-down knowledge, prior segmentation of the object can improve recognition and classification. In this paper, we focus on the problem of bottom-up detection and segmentation of unknown objects. Gestalt psychology studies the same phenomenon in human vision. We propose the utilization of a number of Gestalt principles. Our method starts by generating a set of hypotheses about the location of objects using symmetry. These hypotheses are then used to initialize the segmentation process. The main focus of the paper is on the evaluation of the resulting object segments using Gestalt principles to select segments with high figural goodness. The results show that the Gestalt principles can be successfully used for detection and segmentation of unknown objects. The results furthermore indicate that the Gestalt measures for the goodness of a segment correspond well with the objective quality of the segment. We exploit this to improve the overall segmentation performance.

  • 180.
    Kootstra, Gert
    et al.
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Nederveen, Arco
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    de Boer, Bart
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Paying Attention to Symmetry2008Ingår i: Proceedings of the British Machine Vision Conference (BMVC2008), The British Machine Vision Association and Society for Pattern Recognition , 2008, s. 1115-1125Konferensbidrag (Refereegranskat)
    Abstract [en]

    Humans are very sensitive to symmetry in visual patterns. Symmetry is detected and recognized very rapidly. While viewing symmetrical patterns eye fixations are concentrated along the axis of symmetry or the symmetrical center of the patterns. This suggests that symmetry is a highly salient feature. Existing computational models of saliency, however, have mainly focused on contrast as a measure of saliency. These models do not take symmetry into account. In this paper, we discuss local symmetry as measure of saliency. We developed a number of symmetry models an performed an eye tracking study with human participants viewing photographic images to test the models. The performance of our symmetry models is compared with the contrast saliency model of Itti et al. [1]. The results show that the symmetry models better match the human data than the contrast model. This indicates that symmetry is a salient structural feature for humans, a finding which can be exploited in computer vision.

  • 181.
    Kootstra, Gert
    et al.
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Nederveen, Arco
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    de Boer, Bart
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Paying Attention to Symmetry2008Ingår i: Proceedings of the 20th Belgium-Netherlands Artificial Intelligence Conference (BNAIC 2008), University of Twente Publications , 2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    Humans are very sensitive to symmetry in visual patterns. Symmetry is detected and recognized very rapidly, and eye fixations are concentrated along the axis of symmetry or the symmetrical center of the patterns. This suggests that symmetry is a highly salient feature. Existing computational models of saliency, however, have mainly focused on contrast as a measure of saliency. These models do not take symmetry into account. In this paper, we discuss local symmetry as a measure of saliency. We developed a number of symmetry models and performed an eye-tracking study with human participants viewing photographic images to test the models. The results show that the symmetry models better match the human data than the contrast saliency model of Itti, Koch and Niebur [1]. This indicates that symmetry is a salient structural feature for humans, a finding which can be exploited in computer vision.

  • 182.
    Kootstra, Gert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Popovic, Mila
    Jorgensen, Jimmy Alison
    Kuklinski, Kamil
    Miatliuk, Konstantsin
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Krueger, Norbert
    Enabling grasping of unknown objects through a synergistic use of edge and surface information2012Ingår i: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 31, nr 10, s. 1190-1213Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Grasping unknown objects based on visual input, where no a priori knowledge about the objects is used, is a challenging problem. In this paper, we present an Early Cognitive Vision system that builds a hierarchical representation based on edge and texture information which provides a sparse but powerful description of the scene. Based on this representation, we generate contour-based and surface-based grasps. We test our method in two real-world scenarios, as well as on a vision-based grasping benchmark providing a hybrid scenario using real-world stereo images as input and a simulator for extensive and repetitive evaluation of the grasps. The results show that the proposed method is able to generate successful grasps, and in particular that the contour and surface information are complementary for the task of grasping unknown objects. This allows for dealing with rather complex scenes.

  • 183.
    Kootstra, Gert
    et al.
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Schomaker, Lambert R. B.
    Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands.
    Prediction of Human Eye Fixations using Symmetry2009Ingår i: Proceedings of the 31st Annual Conference of the Cognitive Science Society (CogSci09), Cognitive Science Society , 2009, s. 56-61Konferensbidrag (Refereegranskat)
    Abstract [en]

    Humans are very sensitive to symmetry in visual patterns. Reaction time experiments show that symmetry is detected and recognized very rapidly. This suggests that symmetry is a highly salient feature. Existing computational models of saliency, however, have mainly focused on contrast as a measure of saliency. In this paper, we discuss local symmetry as a measure of saliency. We propose a number of symmetry models and perform an eye-tracking study with human participants viewing photographic images to test the models. The performance of our symmetry models is compared with the contrast-saliency model of Itti, Koch and Niebur (1998). The results show that the symmetry models better match the human data than the contrast model, which indicates that symmetry can be regarded as a salient feature.

  • 184.
    Kostavelis, Ioannis
    et al.
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Boukas, Evangelos
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Nalpantidis, Lazaros
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Aviles, Marcos
    SPARTAN system: Towards a low-cost and high-performance vision architecture for space exploratory rovers2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    The “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) activity of the European Space Agency (ESA) aims to develop an efficient, low-cost and accurate vision system for the future Martian exploratory rovers. The interest on vision systems for space robots has been steadily growing during the last years. The SPARTAN system considers an optimal implementation of computer vision algorithms for space rover navigation and is desig- nated for application to a space exploratory robotic rover, such as the ExoMars. The goal of the present work is the development of an appropriate architecture for the vision system. Thus, the arrangement and characteristics of the rover’s vision sensors will be defined and the required com- puter vision modules will be presented. The analysis will be performed taking into consideration the constraints defined by ESA about the SPARTAN system.

  • 185.
    Kostavelis, Ioannis
    et al.
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Nalpantidis, Lazaros
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Comparative presentation of real-time obstacle avoidance algorithms using solely stereo vision2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents a comparison between vision-based obstacle avoidance algorithms for mobile robot navigation. The issue of obstacle avoidance in robotics demands a reliable solution since mobile platforms often have to maneuver in arbitrary environments with high level of risk. The most significant advantage of the presented work is the use of only one sensor, i.e. a stereo camera, which significantly diminishes the computational cost. Three different versions of the proposed method have been developed. The implementation of these algorithms consists of a stereo vision module, which is common for all the versions, and a decision making module, which is different in each version and proposes an efficient method of processing stereo information in order to navigate a robotic platform. The algorithms have been implemented in C++ and the produced frame rate ensures that the robot will be able to accomplish the proposed decisions in real time. The presented algorithms have been tested on various different input images and their results are shown and discussed.

  • 186.
    Kostavelis, Ioannis
    et al.
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Nalpantidis, Lazaros
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Real-time algorithm for obstacle avoidance2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents a vision-based obstacle detection and avoidance method for autonomous mobile robots. The implementation of an algorithm able to navigate a robot in arbitrary environments usually demands of the synergy of several sensors. This work presents an algorithm employing only one sensor, i.e. a stereo camera, thus significantly diminishing the system’s complexity. The implementation of this algorithm can be divided into two separate and independent modules. First, the stereo vision module retrieves information from the environment and produces disparity maps and then the decision making module analyses the data of the disparity maps and governs the robot’s direction. The achieved frame rate ensures that the robot will have enough time to accomplish the proposed decisions in real time. Both of the modules have been implemented in C++. The complete algorithm has been examined by being applied on an extensive set of pre-captured stereo images.

  • 187.
    Kostavelis, Ioannis
    et al.
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Nalpantidis, Lazaros
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Gasteratos, Antonios
    Production and Management Engineering Dept., Democritus University of Thrace, Greece.
    Supervised traversability learning for robot navigation2011Ingår i: 12th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2011, Springer Berlin/Heidelberg, 2011, Vol. 6856 LNAI, s. 289-298Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents a machine learning method for terrain's traversability classification. Stereo vision is used to provide the depth map of the scene. Then, a v-disparity image calculation and processing step extracts suitable features about the scene's characteristics. The resulting data are used as input for the training of a support vector machine (SVM). The evaluation of the traversability classification is performed with a leave-one-out cross validation procedure applied on a test image data set. This data set includes manually labeled traversable and non-traversable scenes. The proposed method is able to classify the scene of further stereo image pairs as traversable or non-traversable, which is often the first step towards more advanced autonomous robot navigation behaviours.

  • 188.
    Kozica, Ermin
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Ramchandran, Kannan
    Kleijn, W. Bastiaan
    KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    COMPLEXITY-OUTSOURCED LOW-LATENCY VIDEO ENCODING THROUGH FEEDBACK UNDER A SUM-RATE CONSTRAINT2010Ingår i: 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING , 2010, s. 973-976Konferensbidrag (Refereegranskat)
    Abstract [en]

    In live video communication, the quality of the video that is encoded on a mobile device is more often than not constrained by the available computational resources. We address this problem by allowing the encoder to "outsource" the encoding complexity to the decoder through the use of feedback, under a sum-rate constraint comprising the weighted sum of the forward and feedback rates. Analysis of such a complexity-outsourced framework using an analytically tractable video model reveals that the feedback rate should be optimally adapted to the video motion characteristics. Application of our framework to real-world video sequences reveals the efficacy of our proposed architecture, with experimental results validating that substantial gains in PSNR are achievable over state-of-the-art fast-search algorithms at a comparable level of complexity. These gains are significant even for mobile live video communication applications over the Internet.

  • 189.
    Kozica, Ermin
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Ljud- och bildbehandling (Stängd 130101).
    Zachariah, Dave
    KTH, Skolan för elektro- och systemteknik (EES), Ljud- och bildbehandling (Stängd 130101).
    Kleijn, W. Bastiaan
    KTH, Skolan för elektro- och systemteknik (EES), Ljud- och bildbehandling (Stängd 130101).
    Interlacing intraframes in multiple-description video coding2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    We introduce a method to improve performance of multiple-description coding based on legacy video coders with pre- and postprocessing. The pre- and post-processing setup is general, making the method applicable to most legacy coders. For the case of two coders, a relative displacement of the intra-coding mode between the coders is shown to give improved robustness to packet loss. The optimal displacement of the intra-coding mode is found analytically, using a distortion minimization formulation where two independent Gilbert channels are assumed. The analytical results are confirmed by simulations. Tests with an H.263 coder show significant improvement in YPSNR over equivalent systems with no relative displacement of the intra-coding operation.

  • 190.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Robot Visions, Robot Vision2013Ingår i: Twelfth Scandinavian Conference on Artificial Intelligence, 2013, s. 11-11Konferensbidrag (Refereegranskat)
  • 191.
    Kragic, Danica
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Christensen, Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Robust Visual Servoing2014Ingår i: Household Service Robotics, Elsevier, 2014, s. 397-427Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    For service robots operating in domestic environments, it is not enough to consider only control level robustness; it is equally important to consider how image information that serves as input to the control process can be used so as to achieve robust and efficient control. In this chapter we present an effort toward the development of robust visual techniques used to guide robots in various tasks. Given a task at hand, we argue that different levels of complexity should be considered; this also defines the choice of the visual technique used to provide the necessary feedback information. We concentrate on visual feedback estimation where we investigate both two- and three-dimensional techniques. In the former case, we are interested in providing coarse information about the object position/velocity in the image plane. In particular, a set of simple visual features (cues) is employed in an integrated framework where voting is used for fusing the responses from individual cues. The experimental evaluation shows the system performance for three different cases of camera-robot configurations most common for robotic systems. For cases where the robot is supposed to grasp the object, a two-dimensional position estimate is often not enough. Complete pose (position and orientation) of the object may be required. Therefore, we present a model-based system where a wire-frame model of the object is used to estimate its pose. Since a number of similar systems have been proposed in literature, we concentrate on the particular part of the system usually neglected-automatic pose initialization. Finally, we show how a number of existing approaches can successfully be integrated in a system that is able to recognize and grasp fairly textured, everyday objects. One of the examples presented in the experimental section shows a mobile robot performing tasks in a real-word environment-a living room.

  • 192. Kyrki, V.
    et al.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Recent trends in computational and robot vision2008Ingår i: Unifying perspectives in computational and robot vision / [ed] Danica Kragic, Ville Kyrki, New York: Springer Science+Business Media B.V., 2008, s. 1-10Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    There are many characteristics in common in computer vision research and vision research in robotics. For example, the Structure-and-Motion problem in vision has its analog of SLAM (Simultaneous Localization and Mapping) in robotics, visual SLAM being one of the current hot topics. Tracking is another area seeing great interest in both communities, in its many variations, such as 2-D and 3-D tracking, single and multi-object tracking, rigid and deformable object tracking. Other topics of interest for both communities are object and action recognition. Despite having these common interests, however, "pure" computer vision has seen significant theoretical and methodological advances during the last decade which many of the robotics researchers are not fully aware of. On the other hand, the manipulation and control capabilities of robots as well as the range of application areas have developed greatly. In robotics, vision can not be considered an isolated component, but it is instead a part of a system resulting in an action. Thus, in robotics the vision research should include consideration of the control of the system, in other words, the entire perception-action loop. A holistic system approach would then be useful and could provide significant advances in this application domain.

  • 193. Laptev, I.
    et al.
    Lindeberg, Tony
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    A distance measure and a feature likelihood map concept for scale-invariant model matching2003Rapport (Refereegranskat)
    Abstract [en]

    This paper presents two approaches for evaluating multi-scale feature-based object models. Within the first approach, a scale-invariant distance measure is proposed for comparing two image representations in terms of multi-scale features. Based on this measure, the maximisation of the likelihood of parameterised feature models allows for simultaneous model selection and parameter estimation. The idea of the second approach is to avoid an explicit feature extraction step and to evaluate models using a function defined directly from the image data. For this purpose, we propose the concept of a feature likelihood map, which is a function normalised to the interval [0, 1], and that approximates the likelihood of image features at all points in scale-space. To illustrate the applicability of both methods, we consider the area of hand gesture analysis and show how the proposed evaluation schemes can be integrated within a particle filtering approach for performing simultaneous tracking and recognition of hand models under variations in the position, orientation, size and posture of the hand. The experiments demonstrate the feasibility of the approach, and that real time performance can be obtained by pyramid implementations of the proposed concepts.

  • 194.
    Laptev, Ivan
    et al.
    IRISA/INRIA.
    Caputo, Barbara
    Schüldt, Christian
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Local velocity-adapted motion events for spatio-temporal recognition2007Ingår i: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 108, nr 3, s. 207-229Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we address the problem of motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate the problem of motion recognition as a matching of corresponding events in image sequences. To enable the matching, we present and evaluate a set of motion descriptors that exploit the spatial and the temporal coherence of motion measurements between corresponding events in image sequences. As the motion measurements may depend on the relative motion of the camera, we also present a mechanism for local velocity adaptation of events and evaluate its influence when recognizing image sequences subjected to different camera motions. When recognizing motion patterns, we compare the performance of a nearest neighbor (NN) classifier with the performance of a support vector machine (SVM). We also compare event-based motion representations to motion representations in terms of global histograms. A systematic experimental evaluation on a large video database with human actions demonstrates that (i) local spatio-temporal image descriptors can be defined to carry important information of space-time events for subsequent recognition, and that (ii) local velocity adaptation is an important mechanism in situations when the relative motion between the camera and the interesting events in the scene is unknown. The particular advantage of event-based representations and velocity adaptation is further emphasized when recognizing human actions in unconstrained scenes with complex and non-stationary backgrounds.

  • 195.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lindeberg, Tony
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    A Distance Measure and a Feature Likelihood Map Concept for Scale-Invariant Model Matching2003Ingår i: International Journal of Computer Vision, ISSN 0920-5691, E-ISSN 1573-1405, Vol. 52, nr 2, s. 97-120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents two approaches for evaluating multi-scale feature-based object models. Within the first approach, a scale-invariant distance measure is proposed for comparing two image representations in terms of multi-scale features. Based on this measure, the maximisation of the likelihood of parameterised feature models allows for simultaneous model selection and parameter estimation.

    The idea of the second approach is to avoid an explicit feature extraction step and to evaluate models using a function defined directly from the image data. For this purpose, we propose the concept of a feature likelihood map, which is a function normalised to the interval [0, 1], and that approximates the likelihood of image features at all points in scale-space.

    To illustrate the applicability of both methods, we consider the area of hand gesture analysis and show how the proposed evaluation schemes can be integrated within a particle filtering approach for performing simultaneous tracking and recognition of hand models under variations in the position, orientation, size and posture of the hand. The experiments demonstrate the feasibility of the approach, and that real time performance can be obtained by pyramid implementations of the proposed concepts.

  • 196.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner (före 2005), Numerisk analys och datalogi, NADA.
    Lindeberg, Tony
    KTH, Tidigare Institutioner (före 2005), Numerisk analys och datalogi, NADA.
    A multi-scale feature likelihood map for direct evaluation of object hypotheses2001Ingår i: Proc Scale-Space and Morphology in Computer Vision, Springer Berlin/Heidelberg, 2001, Vol. 2106, s. 98-110Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper develops and investigates a new approach for evaluating feature based object hypotheses in a direct way. The idea is to compute a feature likelihood map (FLM), which is a function normalized to the interval [0, 1], and which approximates the likelihood of image features at all points in scale-space. In our case, the FLM is defined from Gaussian derivative operators and in such a way that it assumes its strongest responses near the centers of symmetric blob-like or elongated ridge-like structures and at scales that reflect the size of these structures in the image domain. While the FLM inherits several advantages of feature based image representations, it also (i) avoids the need for explicit search when matching features in object models to image data, and (ii) eliminates the need for thresholds present in most traditional feature based approaches. In an application presented in this paper, the FLM is applied to simultaneous tracking and recognition of hand models based on particle filtering. The experiments demonstrate the feasibility of the approach, and that real time performance can be obtained by a pyramid implementation of the proposed concept.

  • 197.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Interest point detection and scale selection in space-time2003Ingår i: Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings, Springer Berlin/Heidelberg, 2003, Vol. 2695, s. 372-387Konferensbidrag (Refereegranskat)
    Abstract [en]

    Several types of interest point detectors have been proposed for spatial images. This paper investigates how this notion can be generalised to the detection of interesting events in space-time data. Moreover, we develop a mechanism for spatio-temporal scale selection and detect events at scales corresponding to their extent in both space and time. To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect regions in space-time where the image structures have significant local variations in both space and time. In this way, events that correspond to curved space-time structures are emphasised, while structures with locally constant motion are disregarded. To construct this operator, we start from a multi-scale windowed second moment matrix in space-time, and combine the determinant and the trace in a similar way as for the spatial Harris operator. All space-time maxima of this operator are then adapted to characteristic scales by maximising a scale-normalised space-time Laplacian operator over both spatial scales and temporal scales. The motivation for performing temporal scale selection as a complement to previous approaches of spatial scale selection is to be able to robustly capture spatio-temporal events of different temporal extent. It is shown that the resulting approach is truly scale invariant with respect to both spatial scales and temporal scales. The proposed concept is tested on synthetic and real image sequences. It is shown that the operator responds to distinct and stable points in space-time that often correspond to interesting events. The potential applications of the method are discussed.

  • 198.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    On Space-Time Interest Points2003Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features capture interesting events in video and can be used for a compact representation and for interpretation of video data.

    To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.

  • 199.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Space-time interest points2003Ingår i: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003: ICCV'03, IEEE conference proceedings, 2003, s. 432-439Konferensbidrag (Refereegranskat)
    Abstract [en]

    Local image features or interest points provide compact and abstract representations of patterns in an image. We propose to extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for its interpretation. To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We then estimate the spatio-temporal extents of the detected events and compute their scale-invariant spatio-temporal descriptors. Using such descriptors, we classify events and construct video representation in terms of labeled space-time points. For the problem of human motion analysis, we illustrate how the proposed method allows for detection of walking people in scenes with occlusions and dynamic backgrounds.

  • 200.
    Laptev, Ivan
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Tracking of multi-state hand models using particle filtering and a hierarchy of multi-scale image features2001Rapport (Refereegranskat)
    Abstract [en]

    This paper presents an approach for simultaneous tracking and recognition of hierarchical object representations in terms of multiscale image features. A scale-invariant dissimilarity measure is proposed for comparing scale-space features at different positions and scales. Based on this measure, the likelihood of hierarchical, parameterized models can be evaluated in such a way that maximization of the measure over different models and their parameters allows for both model selection and parameter estimation. Then, within the framework of particle filtering, we consider the area of hand gesture analysis, and present a method for simultaneous tracking and recognition of hand models under variations in the position, orientation, size and posture of the hand. In this way, qualitative hand states and quantitative hand motions can be captured, and be used for controlling different types of computerised equipment.

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