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  • 251.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Chaos in controlled generators1995Konferensbidrag (Refereegranskat)
  • 252.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Controlling chaotic oscillations in delayed phase-locked loop1994Ingår i: Nonlinear dynamics, ISSN 0924-090X, E-ISSN 1573-269X, Vol. 2, nr 2, s. 36-48Artikel i tidskrift (Refereegranskat)
  • 253.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Modeling transmission of information using signals with chaotic frequency modulation1996Konferensbidrag (Refereegranskat)
  • 254.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Processing information-bearing chaotic signal in a presence of noise using coupled oscillating systems1995Ingår i: Synchronization and Patterns, s. 47-52Artikel i tidskrift (Refereegranskat)
  • 255.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    selective suppression of deterministic chaotic signals1993Ingår i: Technical physics letters, ISSN 1063-7850, E-ISSN 1090-6533, Vol. 19, nr 12, s. 769-770Artikel i tidskrift (Refereegranskat)
  • 256.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Synchronization of adaptive chaotic systems1996Konferensbidrag (Refereegranskat)
  • 257.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Using feedback and directional coupling for signal processing with synchronized chaotic systems1996Konferensbidrag (Refereegranskat)
  • 258.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Shalfeev, V.D
    Chua, L.O
    Exact synchronization of mismatched chaotic systems1996Ingår i: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, ISSN 0218-1274, Vol. 6, nr 3, s. 569-580Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this letter we use adaptive parameter and state feedback control to synchronize two or more slightly mismatched chaotic systems. Chua's circuit with a smooth nonlinearity is used throughout to illustrate our approach. We specify the conditions under which the parameter of a slave system will automatically converge to the parameter of the master system. We also consider potential applications of the control system to problems of secure communications and synchronization of chaos in a chain of slightly different Chua's circuits.

  • 259.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sushchik, M.M
    Molkov, I
    Kuznetsov, A.S
    bistable phase synchronization and chaos in a system of coupled Van der Pol-Duffing oscillators1998Konferensbidrag (Refereegranskat)
    Abstract [en]

    Analysis of numerical solutions for a system of two van der Pol-Duffing oscillators with nonlinear coupling showed that there exist chaotic switchings (occurring at irregular time intervals) between two oscillatory regimes differing by nearly time-constant phase shifts between the coupled subsystems. The analysis includes the investigation of bifurcations of the periodic motions corresponding to synchronization of two subsystems, finding stability regions of synchronization regimes and scenarios of the transitions to chaos.

  • 260.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sushchik, M.M.
    Molkov, Ya. I.
    Kuznetsov, A.S.
    Bistable phase synchronization and chaos in a system of coupled van der Pol-Duffing oscillators1999Ingår i: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, ISSN 0218-1274, Vol. 9, nr 12, s. 2271-2278Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Analysis of numerical solutions for a system of two van der Pol-Duffing oscillators with nonlinear coupling showed that there exist chaotic switchings (occurring at irregular time intervals) between two oscillatory regimes differing by nearly time-constant phase shifts between the coupled subsystems. The analysis includes the investigation of bifurcations of the periodic motions corresponding to synchronization of two subsystems, finding stability regions of synchronization regimes and scenarios of the transitions to chaos.

  • 261.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sushchik, M.M
    Molkov, Ya. I.
    Kuznetsov, A.S,
    Phase synchronization bistability and chaos in system of two identical Van der Pol-Duffing oscillators1999Ingår i: Izvestiya Vysshikh Uchebnykh Zavedenii. Fizika, ISSN 0021-3411, Vol. 7, nr 1, s. 68-80Artikel i tidskrift (Refereegranskat)
  • 262.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sushchik, M.M
    Molkov, Ya.I
    Kuznetsov, A.S
    Phase bistability and chaos in a system of two identical Van der Pol-Duffing oscillators1998Konferensbidrag (Refereegranskat)
  • 263.
    Kozlov, Alexander
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sushchik, M.M.
    Molkov, Ya.I.
    Kuznetsov, A.S.
    Symmetry breaking, multistability and chaos in a system of two identical van der Pol-Duffing oscillators1998Ingår i: Bulletin of university of Nizhny Novgorod. Radiofizika, nr 1, s. 89-104Artikel i tidskrift (Refereegranskat)
  • 264.
    Krishnamurthy, Pradeep
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Silberberg, G.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A Cortical Attractor Network with Martinotti Cells Driven by Facilitating Synapses2012Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, nr 4, s. e30752-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  • 265.
    Krishnamurthy, Pradeep
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Silberberg, Gilad
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model2015Ingår i: Frontiers in Neural Circuits, ISSN 1662-5110, E-ISSN 1662-5110, Vol. 9, artikel-id 60Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Although the importance of long-range connections for cortical information processing has been acknowledged for a long time, most studies focused on the long-range interactions between excitatory cortical neurons. Inhibitory interneurons play an important role in cortical computation and have thus far been studied mainly with respect to their local synaptic interactions within the cortical microcircuitry. A recent study showed that long-range excitatory connections onto Martinotti cells (MC) mediate surround suppression. Here we have extended our previously reported attractor network of pyramidal cells (PC) and MC by introducing long-range connections targeting MC. We have demonstrated how the network with Martinotti cell-mediated long-range inhibition gives rise to surround suppression and also promotes saliency of locations at which simple non-uniformities in the stimulus field are introduced. Furthermore, our analysis suggests that the presynaptic dynamics of MC is only ancillary to its orientation tuning property in enabling the network with saliency detection. Lastly, we have also implemented a disinhibitory pathway mediated by another interneuron type (VIP interneurons), which inhibits MC and abolishes surround suppression.

  • 266. Krishnamurthy, Supriya
    et al.
    Ardelius, John
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Dam, Mads
    KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.
    Stadler, Rolf
    KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsnät. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Wuhib, Fetahi Zebenigus
    KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsnät. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Brief Announcement: The Accuracy of Tree-based Counting in Dynamic Networks2010Ingår i: PODC 2010: PROCEEDINGS OF THE 2010 ACM SYMPOSIUM ON PRINCIPLES OF DISTRIBUTED COMPUTING, NEW YORK: ASSOC COMPUTING MACHINERY , 2010, s. 291-292Konferensbidrag (Refereegranskat)
    Abstract [en]

    We study a simple Bellman-Ford-like protocol which performs network size estimation over a tree-shaped overlay. A continuous time Markov model is constructed which allows key protocol characteristics to be estimated under churn, including the expected number of nodes at a given (perceived) distance to the root and, for each such node, the expected (perceived) size of the subnetwork rooted at that node. We validate the model by simulations, using a range of network sizes, node degrees, and churn-to-protocol rates, with convincing results.

  • 267.
    Krishnamurthy, Supriya
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    El-Ansary, Sameh
    Swedish Institute of Computer Science (SICS).
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Haridi, Seif
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Kommunikation: Infrastruktur och tjänster, Programvaru- och datorsystem, SCS.
    An analytical study of a structured overlay in the presence of dynamic membership2008Ingår i: IEEE/ACM Transactions on Networking, ISSN 1063-6692, E-ISSN 1558-2566, Vol. 16, nr 4, s. 814-825Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we present an analytical study of dynamic membership (aka churn) in structured peer-to-peer networks. We use a fluid model approach to describe steady-state or transient phenomena and apply it to the Chord system. For any rate of churn and stabilization rates and any system size, we accurately account for the functional form of the probability of network disconnection as well as the fraction of failed or incorrect successor and finger pointers. We show how we can use these quantities to predict both the performance and consistency of lookups under churn. All theoretical predictions match simulation results. The analysis includes both features that are generic to structured overlays deploying a ring as well as Chord-specific details and opens the door to a systematic comparative analysis of, at least, ring-based structured overlay systems under churn.

  • 268. Krishnamurthy, Supriya
    et al.
    El-Ansary, Sameh
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Haridi, Seif
    Comparing maintenance strategies for overlays2008Ingår i: Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing, PDP 2008, IEEE Computer Society, 2008, s. 473-482Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present an analytical tool for understanding the performance of structured overlay networks under chum based on the master-equation approach of physics. We motivate and derive an equation for the average number of hops taken by lookups during churn, for the Chord network. We analyse this equation in detail to understand the behaviour with and without churn. We then use this understanding to predict how lookups will scale for varying peer population as well as varying the sizes of the routing tables. We also consider a change in the maintenance algorithm of the overlay, from periodic stabilisation to a reactive one which corrects fingers only when a change is detected. We generalise our earlier analysis to understand how the reactive strategy compares with the periodic one.

  • 269. Lan, Yueheng
    et al.
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. Aalto University, Finland.
    The stochastic thermodynamics of a rotating Brownian particle in a gradient flow2015Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, artikel-id 12266Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We compute the entropy production engendered in the environment from a single Brownian particle which moves in a gradient flow, and show that it corresponds in expectation to classical near-equilibrium entropy production in the surrounding fluid with specific mesoscopic transport coefficients. With temperature gradient, extra terms are found which result from the nonlinear interaction between the particle and the non-equilibrated environment. The calculations are based on the fluctuation relations which relate entropy production to the probabilities of stochastic paths and carried out in a multi-time formalism.

  • 270. Lansner, A
    et al.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ullström, M
    Grillner, S
    Local spinal modulation of the calcium dependent potassium channel underlying slow adaptation in a model of the lamprey CPG1998Konferensbidrag (Refereegranskat)
  • 271. Lansner, A
    et al.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ullström, M
    Grillner, S
    Modulating the calcium dependent potassium conductance in a model of the lamprey CPG1997Konferensbidrag (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

    The lampery is a primitive water-living vertebrate that moves by means of undulatory swimming. It is of particular interest as an experimental model for the neural generation of locomotin [Grillner et al..,,, 1995] a major advantage of this system is that the motor pattern underlying swimming can be elicited in an isolated piece of spinal cord. Being one of the best characterized vertebrate neuronal systems, the lampery spinal CPG has been the subject of a number of modelling and simulation studies.

  • 272.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations2009Ingår i: TINS - Trends in Neurosciences, ISSN 0166-2236, E-ISSN 1878-108X, Vol. 32, nr 3, s. 178-186Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The second half of the past century saw the emergence of a theory of cortical associative memory function originating in Donald Hebb's hypotheses on activity-dependent synaptic plasticity and cell-assembly formation and dynamics. This conceptual framework has today developed into a theory of attractor memory that brings together many experimental observations from different sources and levels of investigation into computational models displaying information-processing capabilities such as efficient associative memory and holistic perception. Here, we outline a development that might eventually lead to a neurobiologically grounded theory of cortical associative memory.

  • 273.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Neural network models2005Ingår i: Encyclopedia of Nonlinear Science, New York: Routledge, 2005, s. 614-616Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 274.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Benjaminsson, Simon
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Johansson, Christopher
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    From ANN to Biomimetic Information Processing2009Ingår i: BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING / [ed] Gutierrez A, Marco S, 2009, Vol. 188, s. 33-43Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial neural networks (ANN) are useful components in today's data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits.

  • 275.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Diesmann, Markus
    Forschungzentrum Jülich and Aachen University .
    Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers2012Ingår i: Computational Systems Neurobiology / [ed] Nicolas Le Novére, Springer, 2012, s. 283-315Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    The number of neurons and synapses in biological brains is very large, on the order of millions and billions respectively even in small animals like insects and mice. By comparison most neuronal network models developed and simulated up to now have been tiny, comprising many orders of magnitude less neurons than their real counterpart, with an even more dramatic difference when it comes to the number of synapses. In this chapter we discuss why and when it may be important to work with large-scale, if not full-scale, neuronal network and brain models and to run simulations on supercomputers. We describe the state-of-the-art in large-scale neural simulation technology and methodology as well as ways to analyze and visualize output from such simulations. Finally we discuss the challenges and future trends in this field.

  • 276.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hemani, Ahmed
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Farahini, Nasim
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Spiking brain models: Computation, memory and communication constraints for custom hardware implementation2014Ingår i: 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE , 2014, s. 556-562Konferensbidrag (Refereegranskat)
    Abstract [en]

    We estimate the computational capacity required to simulate in real time the neural information processing in the human brain. We show that the computational demands of a detailed implementation are beyond reach of current technology, but that some biologically plausible reductions of problem complexity can give performance gains between two and six orders of magnitude, which put implementations within reach of tomorrow's technology.

  • 277.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Johansson, Christopher
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A Mean Field Approximation of BCPNN2005Rapport (Övrigt vetenskapligt)
    Abstract [en]

    In this report we study a mean field (MF) approximation of the Bayesian Confidence Propagating Neural Network (BCPNN) for which we derive the energy function. This MF approximation is compared with the original formulation of the network in a number of different tasks in order to establish the similarities and dissimilarities. We investigate the effect of different updating strategies on the storage capacity. Three different ways of modulating the attractor size are experimentally tested. We apply the networks to prototype extraction. Finally, we investigate how the networks cluster data. These experiments show that there are some differences between BCPNN and its MF approximation. Furthermore, the experiments provide some new knowledge on the clustering of memories in a BCPNN.

  • 278.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Modeling Coordination in the Neocortex at the Microcircuit and Global Network Level2010Ingår i: Dynamic Coordination in the Brain: From Neurons to Mind / [ed] von der Malsburg, C., Phillips W. A., Singer W., MIT Press, 2010, s. 83-99Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 279.
    Lansner, Anders
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Marklund, Petter
    Sikström, Sverker
    Nilsson, Lars-Göran
    Reactivation in Working Memory: An Attractor Network Model of Free Recall2013Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 8, s. e73776-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The dynamic nature of human working memory, the general-purpose system for processing continuous input, while keeping no longer externally available information active in the background, is well captured in immediate free recall of supraspan word-lists. Free recall tasks produce several benchmark memory phenomena, like the U-shaped serial position curve, reflecting enhanced memory for early and late list items. To account for empirical data, including primacy and recency as well as contiguity effects, we propose here a neurobiologically based neural network model that unifies short- and long-term forms of memory and challenges both the standard view of working memory as persistent activity and dual-store accounts of free recall. Rapidly expressed and volatile synaptic plasticity, modulated intrinsic excitability, and spike-frequency adaptation are suggested as key cellular mechanisms underlying working memory encoding, reactivation and recall. Recent findings on the synaptic and molecular mechanisms behind early LTP and on spiking activity during delayed-match-to-sample tasks support this view.

  • 280.
    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.

  • 281.
    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.

  • 282.
    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.

  • 283.
    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.

  • 284.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    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 features2001Ingår i: Scale-Space and Morphology in Computer Vision: Third International Conference, Scale-Space 2001 Vancouver, Canada, July 7–8, 2001 Proceedings, Springer Berlin/Heidelberg, 2001, Vol. 2106, s. 63-74Konferensbidrag (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.

  • 285.
    Laptev, Ivan
    et al.
    KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
    Mayer, H.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Eckstein, W.
    Steger, C.
    Baumgartner, A.
    Automatic extraction of roads from aerial images based on scale space and snakes2000Ingår i: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 12, nr 1, s. 23-31Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the first time a bridging of shadows and partially occluded areas using the heavily disturbed evidence in the image. Additionally, it has only few parameters to be adjusted. The road network is constructed after extracting crossings with varying shape and topology. We show the feasibility of the approach not only by presenting reasonable results but also by evaluating them quantitatively based on ground truth.

  • 286. Lemoy, Remi
    et al.
    Alava, Mikko
    Aurell, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.
    Local search methods based on variable focusing for random K-satisfiability2015Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, nr 1, s. 013305-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We introduce variable focused local search algorithms for satisfiabiliity problems. Usual approaches focus uniformly on unsatisfied clauses. The methods described here work by focusing on random variables in unsatisfied clauses. Variants are considered where variables are selected uniformly and randomly or by introducing a bias towards picking variables participating in several unsatistified clauses. These are studied in the case of the random 3-SAT problem, together with an alternative energy definition, the number of variables in unsatisfied constraints. The variable-based focused Metropolis search (V-FMS) is found to be quite close in performance to the standard clause-based FMS at optimal noise. At infinite noise, instead, the threshold for the linearity of solution times with instance size is improved by picking preferably variables in several UNSAT clauses. Consequences for algorithmic design are discussed.

  • 287. Leski, Szymon
    et al.
    Lindén, Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Tetzlaff, Tom
    Pettersen, Klas H.
    Einevoll, Gaute T.
    Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential2013Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 9, nr 7, s. e1003137-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Despite its century-old use, the interpretation of local field potentials (LFPs), the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the 'locality' of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (<50%) in the spatial reach is observed for higher frequencies (>100 Hz) compared to the near-DC (similar to 0Hz) value of about 200 mu m. Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency (similar to 0Hz) LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive simplified model for the generation of population LFP.

  • 288.
    Lindahl, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Action selection in the basal ganglia - a computational investigation2010Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    The classical explanation for how basal ganglia could perform action selection relies on the direct and indirect pathway. In this computational study we instead build on earlier studies viewing the part performing selection as consisting of the direct and hyperdirect pathways, with the indirect pathway playing the role of controlling the balance between these two [1]. The present model is a further development of a previous spiking neuron model [2], reimplemented in the software NEST [3] using conductance based integrate and fire neurons. We explore the functional consequences of including additional connections known from experimental studies, such as the collaterals from the direct pathway striatal medium spiny neurons to globus pallidus externa (GPe) as well as a dual indirect pathway through GPe. Simulation experiments suggest that this new model can perform selection with an improved sensitivity and over a larger range of dopamine modulation.

  • 289.
    Lindahl, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hellgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Short term plasticity within the basal ganglia - a systems level computational investigation2011Ingår i: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 12, nr Suppl 1, s. P145-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Striatal direct pathway medium spiny neurons (MSNs) converge, with inhibitory synapses onto output nuclei substantia nigra reticulata (SNr), which keep neurons in the thalamus, superior colliculus and pendunculopontine nuclei under tonic inhibition[1]. Recent experimental findings[2] have found short term facilitation in MSN synapses onto SNr neurons. We investigate the functional consequences of these findings using a basal ganglia system level model, with spiking MSNs modeled according to Izhikevich’s simple model[3] and with facilitating synapses[4] fitted to data in[2]. The model is implemented in the NEST[5] simulator. We quantify how striatal populations of MSNs can control activity in SNr neurons, and to what extent this depends on having weak static, strong static and facilitating synapses between MSNs and SNr neurons.

    Our simulation experiments predict that facilitating synapses allow baseline firing of presynaptic MSNs without suppressing target SNr neurons, while burst activation of only a few of these presynaptic striatal neurons can suppress the activity of one SNr neuron. This is in accordance with extracellular recordings in awake animals[6], where task dependent activity is transferred from a broad striatal population to a smaller subpopulation, responding increasingly stronger during learning of a task dependent behavior.

  • 290.
    Lindahl, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Sarvestani, Iman Kamali
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Hällgren Kotaleski, Jeanette
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways2013Ingår i: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 7, s. UNSP 76-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many of the synapses in the basal ganglia display short-term plasticity. Still, computational models have not yet been used to investigate how this affects signaling. Here we use a model of the basal ganglia network, constrained by available data, to quantitatively investigate how synaptic short-term plasticity affects the substantia nigra reticulata (SNr), the basal ganglia output nucleus. We find that SNr becomes particularly responsive to the characteristic burst-like activity seen in both direct and indirect pathway striatal medium spiny neurons (MSN). As expected by the standard model, direct pathway MSNs are responsible for decreasing the activity in SNr. In particular, our simulations indicate that bursting in only a few percent of the direct pathway MSNs is sufficient for completely inhibiting SNr neuron activity. The standard model also suggests that SNr activity in the indirect pathway is controlled by MSNs disinhibiting the subthalamic nucleus (STN) via the globus pallidus externa (GPe). Our model rather indicates that SNr activity is controlled by the direct GPe-SNr projections. This is partly because GPe strongly inhibits SNr but also due to depressing STN-SNr synapses. Furthermore, depressing GPe-SNr synapses allow the system to become sensitive to irregularly firing GPe subpopulations, as seen in dopamine depleted conditions, even when the GPe mean firing rate does not change. Similar to the direct pathway, simulations indicate that only a few percent of bursting indirect pathway MSNs can significantly increase the activity in SNr. Finally, the model predicts depressing STN-SNr synapses, since such an assumption explains experiments showing that a brief transient activation of the hyperdirect pathway generates a tri-phasic response in SNr, while a sustained STN activation has minor effects. This can be explained if STN-SNr synapses are depressing such that their effects are counteracted by the (known) depressing GPe-SNr inputs.

  • 291.
    Linde, Oskar
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Composed Complex-Cue Histograms: An Investigation of the Information Content in Receptive Field Based Image Descriptors for Object Recognition2012Ingår i: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 116, nr 4, s. 538-560Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations.

    This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories.

    It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complex-cue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive experimental evaluations on two mutually independent data sets.

    For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatio-chromatic) differential invariants up to order two lead to very high recognition rates.

    For the task of category classification, primary information is contained in both first- and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue.

    Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations.

    An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.

  • 292.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A computational theory of visual receptive fields2013Ingår i: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 107, nr 6, s. 589-635Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world.

    These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space–time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system. Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales.

    It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators. Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation. Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations.

    There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision. Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space–time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space–time tilted receptive fields in V1, all within the same unified theory. In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli.

    This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision. It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms.

  • 293.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A framework for invariant visual operations based on receptive field responses2013Ingår i: SSVM 2013: Fourth International Conference on Scale Space and Variational Methods in Computer Vision, June 2-6, Schloss Seggau, Graz region, Austria: Invited keynote address / [ed] Arjan Kuijper, 2013Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    The brain is able to maintain a stable perception although the visual stimuli vary substantially on the retina due to geometric transformations and lighting variations in the environment. This talk presents a unified theory for achieving basic invariance properties of visual operations already at the level of receptive fields.

    This generalized framework for invariant receptive field responses comprises:

    • local scaling transformations caused by objects of different size and at different distances to the observer,
    • locally linearized image deformations caused by variations in the viewing direction in relation to the object,
    • locally linearized relative motions between the object and the observer and
    • local multiplicative intensity transformations caused by illumination variations.

    The receptive field model can be derived by necessity from symmetry properties of the environment and leads to predictions about receptive field profiles in good agreement with receptive field profiles measured by cell recordings in mammalian vision. Indeed, the receptive field profiles in the retina, LGN and V1 can be seen as close to ideal to what is motivated by the idealized requirements.

    By complementing receptive field measurements with selection mechanisms over the parameters in the receptive field families, it is shown how true invariance of receptive field responses can be obtained under scaling transformations, affine transformations and Galilean transformations. Thereby, the framework provides a mathematically well-founded and biologically plausible model for how basic invariance properties can be achieved already at the level of receptive fields and support invariant recognition of objects and events under variations in viewpoint, retinal size, object motion and illumination.

    The theory can explain the different shapes of receptive field profiles found in biological vision, which are tuned to different sizes and orientations in the image domain as well as to different image velocities in space-time, from a requirement that the visual system should be invariant to the natural types of image transformations that occur in its environment.

    References:

    • T. Lindeberg (2011) "Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space". Journal of Mathematical Imaging and Vision, volume 40, number 1, pages 36-81, May 2011.
    • T. Lindeberg (2013) “Invariance of visual operations at the level of receptive fields”, PLoS ONE 8(7): e66990, doi:10.1371/journal.pone.0066990, preprint available from arXiv:1210.0754.
    • T. Lindeberg (2013) "Generalized axiomatic scale-space theory", Advances in Imaging and Electron Physics, (P. Hawkes, ed.), Elsevier, volume 178, pages 1-96, Academic Press: Elsevier Inc., doi: 10.1016/B978-0-12-407701-0.00001-7
  • 294.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    A scale selection principle for estimating image deformations1998Ingår i: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 16, s. 961-977Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A basic functionality of a vision system concerns the ability to compute deformation fields between different images of the same physical structure. This article advocates the need for incorporating explicit mechanisms for scale selection in this context, in algorithms for computing descriptors such as optic flow and for performing stereo matching. A basic reason why such a mechanism is essential is the fact that in a coarse-to-fine propagation of disparity or flow information, it is not necessarily the case that the most accurate estimates are obtained at the finest scales. The existence of interfering structures at fine scales may make it impossible to accurately match the image data at fine scales. selecting deformation estimates from the scales that minimize the (suitably normalized) uncertainty over scales. A specific implementation of this idea is presented for a region based differential flow estimation scheme. It is shown that the integrated scale selection and flow estimation algorithm has the qualitative properties of leading to the selection of coarser scales for larger size image structures and increasing noise level, whereas it leads to the selection of finer scales in the neighbourhood of flow field discontinuities

  • 295.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Automatic scale selection as a pre-processing stage for interpreting the visual world1999Ingår i: Proc. Fundamental StructuralProperties in Image and Pattern Analysis FSPIPA'99 , (Budapest, Hungary), September 6-7, 1999, Österreichischen Computer Gesellschaft , 1999, Vol. 130, s. 9-23Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper reviews a systematic methodology for formulating mechanisms for automatic scale selection when performing feature detection in scale-space. An important property of the proposed approach is that the notion of scale is included already in the definition of image features

  • 296.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Automatic Scale Selection as a Pre-Processing Stage to Interpreting Real-World Data1996Ingår i: Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence (Toulouse, France): Invited keynote address, 1996, s. 490-490Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    e perceive objects in the world as meaningful entities only over certain ranges of scale. A simple example is the concept of a branch of a tree, which makes sense only at a scale from, say, a few centimeters to at most a few meters, It is meaningless to discuss the tree concept at the nanometer or kilometer level. At those scales, it is more relevant to talk about the molecules that form the leaves of the tree, and the forest in which the tree grows, respectively.

    This fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis.

    After a brief review of the main ideas behind a scale-space representation, I will in this talk describe a recently developed systematic methodology for generating hypotheses about interesting scale levels in image data---based on a general principle stating that local extrema over scales of different combinations of normalized derivatives are likely candidates to correspond to interesting image structures. Specifically, it will be shown how this idea can be used for formulating feature detectors which automatically adapt their local scales of processing to the local image structure.

    Support for the proposed methodology will be presented in terms of general study of the scale selection method under rescalings of the input data, as well as more detailed analysis of how the scale selection method performs when integrated with various types of feature detection modules and then applied to characteristic image patterns. Moreover, it will be illustrated by a rich set of experiments how this scale selection approach applies to various types of feature detection problems in early vision.

    In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottle-neck. It will be argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to analyse complex unknown environments.

  • 297.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Corner detection2001Ingår i: Encyclopaedia of Mathematics / [ed] Michiel Hazewinkel, Springer , 2001Kapitel i bok, del av antologi (Refereegranskat)
  • 298.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Discrete Derivative Approximations with Scale-Space Properties: A Basis for Low-Level Feature Extraction1993Ingår i: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 3, nr 4, s. 349-376Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article shows how discrete derivative approximations can be defined so thatscale-space properties hold exactly also in the discrete domain. Starting from a set of natural requirements on the first processing stages of a visual system,the visual front end, it gives an axiomatic derivation of how a multiscale representation of derivative approximations can be constructed from a discrete signal, so that it possesses analgebraic structure similar to that possessed by the derivatives of the traditional scale-space representation in the continuous domain. A family of kernels is derived that constitutediscrete analogues to the continuous Gaussian derivatives.The representation has theoretical advantages over other discretizations of the scale-space theory in the sense that operators that commute before discretizationcommute after discretization. Some computational implications of this are that derivative approximations can be computeddirectly from smoothed data and that this will giveexactly the same result as convolution with the corresponding derivative approximation kernel. Moreover, a number ofnormalization conditions are automatically satisfied.The proposed methodology leads to a scheme of computations of multiscale low-level feature extraction that is conceptually very simple and consists of four basic steps: (i)large support convolution smoothing, (ii)small support difference computations, (iii)point operations for computing differential geometric entities, and (iv)nearest-neighbour operations for feature detection.Applications demonstrate how the proposed scheme can be used for edge detection and junction detection based on derivatives up to order three.

  • 299.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Edge detection2001Ingår i: Encyclopaedia of Mathematics / [ed] Michiel Hazewinkel, Springer , 2001Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Edge detection

    An early processing stage in image processing and computer vision, aimed at detecting and characterizing discontinuities in the image domain.

    The importance of edge detection for early machine vision is usually motivated from the observation that under rather general assumptions about the image formation process, a discontinuity in image brightness can be assumed to correspond to a discontinuity in either depth, surface orientation, reflectance, or illumination. In this respect, edges in the image domain constitute a strong link to physical properties of the world. A representation of image information in terms of edges is also compact in the sense that the two-dimensional image pattern is represented by a set of one-dimensional curves. For these reasons, edges have been used as main features in a large number of computer vision algorithms.

    A non-trivial aspect of edge-based analysis of image data, however, concerns what should be meant by a discontinuity in image brightness. Real-world image data are inherently discrete, and for a function defined on a discrete domain, there is no natural notion of "discontinuity" , and there is no inherent way to judge what are the edges in a given discrete image.

    An early approach to edge detection involved the convolution of the image  by a Gaussian kernel , followed by the detection of zero-crossings in the Laplacian response [a1] (cf. also Scale-space theory). However, such edge curves satisfying

    give rise to false edges and have poor localization at curved edges.

    A more refined approach is the notion of non-maximum suppression [a2], [a3], [a4], where edges are defined as points at which the gradient magnitude assumes a local maximum in the gradient direction. In differential-geometric terms, such edge points can be characterized as points at which [a5]:

    i) the second-order directional derivative in the gradient direction is zero; and

    ii) the third-order directional derivative in the gradient direction is negative.

    In terms of partial derivatives, for a two-dimensional image  this edge definition can be written as

    Again, the computation of discrete derivative approximations is preceded by smoothing the image  with a Gaussian kernel, and the choice of different standard deviations of the Gaussian kernel gives rise to edges at different scales (see Scale-space theory or [a5]). While other choices of linear smoothing kernels have also been advocated, their shapes can often be well approximated by Gaussians [a3], [a6], [a7].

    Other approaches to edge detection involve the thresholding of edge strength measures, the computation of intensity derivatives from local least squares fitting, and functional minimization (see also [a8]).

    A subject which has been given large attention during the 1990s is the replacement of the linear smoothing operation by a non-linear smoothing step, with the goal of avoiding smoothing across object boundaries [a9], [a10].

    References:

    [a1] D. Marr, E. Hildreth, "Theory of edge detection" Proc. R. Soc. London , 207 (1980) pp. 187–217

    [a2] R.M. Haralick, "Digital step edges from zero-crossings of second directional derivatives" IEEE Trans. Pattern Anal. Machine Intell. , 6 (1984)

    [a3] J. Canny, "A computational approach to edge detection" IEEE Trans. Pattern Anal. Machine Intell. , 8 : 6 (1986) pp. 679–698

    [a4] A.F. Korn, "Toward a symbolic representation of intensity changes in images" IEEE Trans. Pattern Anal. Machine Intell. , 10 : 5 (1988) pp. 610–625

    [a5] T. Lindeberg, "Edge detection and ridge detection with automatic scale selection" Internat. J. Computer Vision , 30 : 2 (1998) pp. 117–154

    [a6] V. Torre, T.A. Poggio, "On edge detection" IEEE Trans. Pattern Anal. Machine Intell. , 8 : 2 (1980) pp. 147–163

    [a7] R. Deriche, "Using Canny's criteria to derive a recursively implemented optimal edge detector" Internat. J. Computer Vision , 1 (1987) pp. 167–187

    [a8] R. Jain, et al., "Machine vision" , McGraw-Hill (1995)

    [a9] P. Perona, J. Malik, "Scale-space and edge detection using anisotropic diffusion" IEEE Trans. Pattern Anal. Machine Intell. , 12 : 7 (1990) pp. 629–639

    [a10] "Geometry-driven diffusion in computer vision" B.M. ter Haar Romeny (ed.) , Kluwer Acad. Publ. (1994)

  • 300.
    Lindeberg, Tony
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Feature detection with automatic scale selection1998Ingår i: International Journal of Computer Vision, Vol. 30, nr 2, s. 79-116Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. Whereas scale-space representation provides a well-founded framework for dealing with this issue by representing image structures at different scales, traditional scale-space theory does not address the problem of how to selectlocal appropriate scales for further analysis.

    This article proposes a systematic approach for dealing with this problem---a heuristic principle is presented stating that local extrema over scales of different combinations of gamma-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is proposed that this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure.

    Support is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation.

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