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  • 1. Bayro-Corrochano, Eduardo
    et al.
    Eklundh, Jan-Olof
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Advances in theory and applications of pattern recognition, image processing and computer vision2011Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 32, nr 16, s. 2143-2144Artikel i tidskrift (Refereegranskat)
  • 2.
    Devarakota, Pandu Ranga Rao
    et al.
    KTH, Skolan för elektro- och systemteknik (EES), Signalbehandling. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Mirbach, Bruno
    IEE S.A., ZAE Weiergewan, 5326 Contern, Luxembourg.
    Ottersten, Björn
    KTH, Skolan för elektro- och systemteknik (EES), Signalbehandling. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Reliability estimation of a statistical classifier2008Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 29, nr 3, s. 243-253Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Statistical pattern classification techniques have been successfully applied to many practical classification problems. In real-world applications, the challenge is often to cope with patterns that lead to unreliable classification decisions. These situations occur either due to unexpected patterns, i.e., patterns which occur in the regions far from the training data or due to patterns which occur in the overlap region of classes. This paper proposes a method for estimating the reliability of a classifier to cope with these situations. While existing methods for quantifying the reliability are often solely based on the class membership probability estimated on global approximations, in this paper, the reliability is quantified in terms of a confidence interval on the class membership probability. The size of the confidence interval is calculated explicitly based on the local density of training data in the neighborhood of a test pattern. A synthetic example is given to illustrate the various aspects of the proposed approach. In addition, experimental evaluation on real data sets is conducted to demonstrate the effectiveness of the proposed approach to detect unexpected patterns. The lower bound of the confidence interval is used to detect the unexpected patterns. By comparing the performance with the state-of-the-art methods, we show our approach is well-founded.

  • 3.
    Henter, Gustav Eje
    et al.
    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).
    Picking up the pieces: Causal states in noisy data, and how to recover them2013Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 34, nr 5, s. 587-594Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Automatic structure discovery is desirable in many Markov model applications where a good topology (states and transitions) is not known a priori. CSSR is an established pattern discovery algorithm for stationary and ergodic stochastic symbol sequences that learns a predictively optimal Markov representation consisting of so-called causal states. By means of a novel algebraic criterion, we prove that the causal states of a simple process disturbed by random errors frequently are too complex to be learned fully, making CSSR diverge. In fact, the causal state representation of many hidden Markov models, representing simple but noise-disturbed data, has infinite cardinality. We also report that these problems can be solved by endowing CSSR with the ability to make approximations. The resulting algorithm, robust causal states (RCS), is able to recover the underlying causal structure from data corrupted by random substitutions, as is demonstrated both theoretically and in an experiment. The algorithm has potential applications in areas such as error correction and learning stochastic grammars.

  • 4. Kermit, M.
    et al.
    Eide, A. J.
    Lindblad, Thomas
    KTH, Tidigare Institutioner                               , Fysik.
    Waldemark, K.
    Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals2000Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, nr 3, s. 277-281Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The breathing patterns from sleeping persons suffering from sleep apnea have been measured. A method based on the neural network-like O-algorithm has been applied to capture the onset of sleep apnea. This method is suggested as an indicator for early on-line detection of obstructions in the upper airway. Results from the system tested with airflow signals recorded from five patients during sleep indicate acceptable performance and treatment for developing apnea is possible.

  • 5. Kondori, Farid Abedan
    et al.
    Yousefi, Shahrouz
    KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.
    Kouma, Jean-Paul
    Liu, Li
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.
    Direct hand pose estimation for immersive gestural interaction2015Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 66, s. 91-99Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a novel approach for performing intuitive gesture based interaction using depth data acquired by Kinect. The main challenge to enable immersive gestural interaction is dynamic gesture recognition. This problem can be formulated as a combination of two tasks; gesture recognition and gesture pose estimation. Incorporation of fast and robust pose estimation method would lessen the burden to a great extent. In this paper we propose a direct method for real-time hand pose estimation. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Extensive experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation On two different setups; desktop computing, and mobile platform. This reveals the system capability to accommodate different interaction procedures. In addition, a user study is conducted to evaluate learnability, user experience and interaction quality in 3D gestural interaction in comparison to 2D touchscreen interaction.

  • 6. Lidayová, K.
    et al.
    Frimmel, H.
    Wang, Chunliang
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering. Linköping University, Sweden.
    Bengtsson, E.
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering. Linköping University, Sweden.
    Fast vascular skeleton extraction algorithm2016Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, s. 67-75Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels - nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.

  • 7.
    Mahbod, Amirreza
    et al.
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Chowdhury, Manish
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Smedby, Örjan
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Wang, Chunliang
    KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
    Automatic brain segmentation using artificial neural networks with shape context2018Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 101, s. 74-79Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Segmenting brain tissue from MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Many automatic or semi-automatic methods have been proposed in the literature in order to reduce the requirement of user intervention, but the level of accuracy in most cases is still inferior to that of manual segmentation. We propose a new brain segmentation method that integrates volumetric shape models into a supervised artificial neural network (ANN) framework. This is done by running a preliminary level-set based statistical shape fitting process guided by the image intensity and then passing the signed distance maps of several key structures to the ANN as feature channels, in addition to the conventional spatial-based and intensity-based image features. The so-called shape context information is expected to help the ANN to learn local adaptive classification rules instead of applying universal rules directly on the local appearance features. The proposed method was tested on a public datasets available within the open MICCAI grand challenge (MRBrainS13). The obtained average Dice coefficient were 84.78%, 88.47%, 82.76%, 95.37% and 97.73% for gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), brain (WM + GM) and intracranial volume respectively. Compared with other methods tested on the same dataset, the proposed method achieved competitive results with comparatively shorter training time.

  • 8. Svedberg, D.
    et al.
    Carlsson, Stefan
    KTH, Tidigare Institutioner                               , Numerisk analys och datalogi, NADA.
    Calibration, pose and novel views from single images of constrained scenes2000Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, nr 13-14, s. 1125-1133Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We exploit the common constraint of having a right-angle corner of two rectangular planes in the scene in order to calibrate a perspective projection camera and compute its pose relative to the coordinate system defined by the corner. No metric information about the corner is assumed. The camera is constrained to have its image x- and y-axes to be orthogonal with the same scale factor, which is valid for most real-world cameras. We then reproject the image of the corner to an arbitrary viewpoint. We can also compute the metric properties of the scene to scale. We report experimental results with subjectively acceptable quality. The approach shows the power of exploiting constraints that are abundant in typical architectural scenes.

  • 9. Waldemark, J.
    et al.
    Millberg, M.
    Lindblad, Thomas
    KTH, Tidigare Institutioner                               , Fysik.
    Waldemark, K.
    Image analysis for airborne reconnaissance and missile applications2000Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, nr 3, s. 239-251Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper describes how the pulse coupled neural network (PCNN) can be used in various image analysis applications. We especially focus on two time-critical applications, in particular, airborne reconnaissance and missile navigation. Today, biologically inspired sensor analysis systems such as the PCNN can be used in many different applications related to these two major applications. New ideas are shown on how to use PCNN in combination with other image processing transforms, e.g. the Radon transform and foveation point detection to solve image interpretation and missile navigation problems. This includes solving tasks such as image segmentation, object detection and target identification. Finally, a VHDL implementation of the PCNN targeting FPGA is presented.

  • 10. Waldemark, K.
    et al.
    Lindblad, Thomas
    KTH, Tidigare Institutioner                               , Fysik.
    Becanovic, V.
    Guillen, J. L. L.
    Klingner, P. L.
    Patterns from the sky - Satellite image analysis using pulse coupled neural networks for pre-processing, segmentation and edge detection2000Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, nr 3, s. 227-237Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this work we attempt to distinguish land from water in satellite images, specifically images taken by the FORTE satellite. First, we successfully approximate areas hidden by stationary artefacts in the image. We then segment regions of land from water. Finally, we determine the boundaries of the surrounding landmasses.

  • 11.
    Yousefi, Shahrouz
    et al.
    Digital Media Lab., Department of Applied Physics and Electronics, Umeå University.
    Kondori, Farid Abedan
    Digital Media Lab., Department of Applied Physics and Electronics, Umeå University.
    Li, Haibo
    KTH, Skolan för datavetenskap och kommunikation (CSC). Digital Media Lab., Department of Applied Physics and Electronics, Umeå University.
    Experiencing real 3D gestural interaction with mobile devices2013Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 34, nr 8, s. 912-921Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Number of mobile devices such as smart phones or Tablet PCs has been dramatically increased over the recent years. New mobile devices are equipped with integrated cameras and large displays which make the interaction with the device more efficient. Although most of the previous works on interaction between humans and mobile devices are based on 2D touch-screen displays, camera-based interaction opens a new way to manipulate in 3D space behind the device, in the camera's field of view. In this paper, our gestural interaction heavily relies on particular patterns from local orientation of the image called Rotational Symmetries. This approach is based on finding the most suitable pattern from a large set of rotational symmetries of different orders that ensures a reliable detector for hand gesture. Consequently, gesture detection and tracking can be hired as an efficient tool for 3D manipulation in various applications in computer vision and augmented reality. The final output will be rendered into color anaglyphs for 3D visualization. Depending on the coding technology, different low cost 3D glasses can be used for the viewers.

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