Computational object recognition: a biologically motivated approach
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[1] Ming-Kuei Hu,et al. Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.
[2] D. Scott. Perceptual learning. , 1974, Queen's nursing journal.
[3] D. Marr,et al. Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[4] Irving Biederman,et al. Human image understanding: Recent research and a theory , 1985, Comput. Vis. Graph. Image Process..
[5] R. Nosofsky. Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.
[6] D. W. Thompson,et al. Three-dimensional model matching from an unconstrained viewpoint , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.
[7] A. J. Mistlin,et al. Visual neurones responsive to faces , 1987, Trends in Neurosciences.
[8] Y. Miyashita. Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.
[9] M. Tarr,et al. Mental rotation and orientation-dependence in shape recognition , 1989, Cognitive Psychology.
[10] T. Poggio,et al. A network that learns to recognize three-dimensional objects , 1990, Nature.
[11] Y. Miyashita,et al. Neural organization for the long-term memory of paired associates , 1991, Nature.
[12] Ronen Basri,et al. Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[13] Allen M. Waxman,et al. Adaptive 3-D Object Recognition from Multiple Views , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[14] D I Perrett,et al. Organization and functions of cells responsive to faces in the temporal cortex. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[15] M. Young,et al. Sparse population coding of faces in the inferotemporal cortex. , 1992, Science.
[16] Keiji Tanaka. Inferotemporal cortex and higher visual functions , 1992, Current Opinion in Neurobiology.
[17] H H Bülthoff,et al. Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[18] M. Goodale,et al. Visual pathways to perception and action. , 1993, Progress in brain research.
[19] M. Goodale. Visual pathways supporting perception and action in the primate cerebral cortex , 1993, Current Opinion in Neurobiology.
[20] Y. Miyashita. Inferior temporal cortex: where visual perception meets memory. , 1993, Annual review of neuroscience.
[21] M. Goodale,et al. Chapter 28 Visual pathways to perception and action , 1993 .
[22] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[23] Keiji Tanaka,et al. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.
[24] Rolf Adams,et al. Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[25] M J Tarr,et al. Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993). , 1995, Journal of experimental psychology. Human perception and performance.
[26] Rolf P. Würtz,et al. Multilayer dynamic link networks for establishing image point correspondences and visual object recognition , 1995 .
[27] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[28] Stephen Grossberg,et al. Fast-learning VIEWNET architectures for recognizing three-dimensional objects from multiple two-dimensional views , 1995, Neural Networks.
[29] M. Goodale,et al. The visual brain in action , 1995 .
[30] G. Wallis. How neurons learn to associate 2D-views in invariant object recognition , 1996 .
[31] Arbeitsgruppe Bülthoff. How Neurons Learn to Associate 2d-views in Invariant Object Recognition , 1996 .
[32] N. P. Bichot,et al. Visual feature selectivity in frontal eye fields induced by experience in mature macaques , 1996, Nature.
[33] M. Tovée,et al. Representational capacity of face coding in monkeys. , 1996, Cerebral cortex.
[34] Sameer A. Nene,et al. Columbia Object Image Library (COIL100) , 1996 .
[35] Keiji Tanaka,et al. Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.
[36] Peter A. Frensch,et al. The Role of Information Reduction in Skill Acquisition , 1996, Cognitive Psychology.
[37] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[38] Rajesh P. N. Rao. Dynamic appearance-based recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[39] Guy Wallis,et al. Temporal Order in Human Object Recognition Learning , 1998 .
[40] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[41] Keiji Tanaka,et al. Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. , 1998, Journal of neurophysiology.
[42] D. Perrett,et al. Evidence accumulation in cell populations responsive to faces: an account of generalisation of recognition without mental transformations , 1998, Cognition.
[43] E. Rolls,et al. View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. , 1998, Cerebral cortex.
[44] Heinrich H Bülthoff,et al. Image-based object recognition in man, monkey and machine , 1998, Cognition.
[45] Steffen Schmalz,et al. Combining Multiple Views and Temporal Associations for 3-D object Recognition , 1998, ECCV.
[46] Ali Shokoufandeh,et al. View-based object recognition using saliency maps , 1999, Image Vis. Comput..
[47] M.M. Van Hulle,et al. View-based 3D object recognition with support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[48] M. Hulle,et al. VIEW-BASED 3 D OBJECT RECOGNITION WITH SUPPORT VECTOR MACHINES , 1999 .
[49] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[50] Luc Van Gool,et al. Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.
[51] R. Desimone,et al. Responses of Macaque Perirhinal Neurons during and after Visual Stimulus Association Learning , 1999, The Journal of Neuroscience.
[52] D. Mareschal,et al. A computational and neuropsychological account of object‐oriented behaviours in infancy , 1999 .
[53] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[54] Gregory Dudek,et al. Local appearance for robust object recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[55] David G. Lowe,et al. Towards a Computational Model for Object Recognition in IT Cortex , 2000, Biologically Motivated Computer Vision.
[56] Tomaso Poggio,et al. Computational Models of Object Recognition in Cortex: A Review , 2000 .
[57] Narendra Ahuja,et al. Learning to recognize objects , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[58] Lucas Paletta,et al. Active object recognition by view integration and reinforcement learning , 2000, Robotics Auton. Syst..
[59] Tomaso Poggio,et al. Models of object recognition , 2000, Nature Neuroscience.
[60] Barbara Hammer,et al. Generalized Relevance LVQ for Time Series , 2001, ICANN.
[61] Heinrich H. Bülthoff,et al. Acquiring Robust Representations for Recognition from Image Sequences , 2001, DAGM-Symposium.
[62] Heinrich H. Bülthoff,et al. View-based recognition under illumination changes using local features , 2001, CVPR 2001.
[63] H. Bülthoff,et al. Effects of temporal association on recognition memory , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[64] M. Mishkin,et al. Learning increases stimulus salience in anterior inferior temporal cortex of the macaque. , 2001, Journal of neurophysiology.
[65] Heinrich H. Bülthoff,et al. Automatic acquisition of exemplar-based representations for recognition from image sequences , 2001, CVPR 2001.
[66] Stepán Obdrzálek,et al. Object Recognition using Local Affine Frames on Distinguished Regions , 2002, BMVC.
[67] T. Poggio,et al. How Visual Cortex Recognizes Objects: The Tale of the Standard Model , 2002 .
[68] Heiko Wersing,et al. Unsupervised Learning of Combination Features for Hierarchical Recognition Models , 2002, ICANN.
[69] R. Henson,et al. Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming , 2002, Nature Neuroscience.
[70] Michel Vidal-Naquet,et al. Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.
[71] M. Chun,et al. The dark side of visual attention , 2002, Current Opinion in Neurobiology.
[72] B. Hammer,et al. Monitoring technical systems with prototype based clustering , 2003 .
[73] Bernt Schiele,et al. Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[74] M. Bar. A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition , 2003, Journal of Cognitive Neuroscience.
[75] S. Hochstein,et al. The reverse hierarchy theory of visual perceptual learning , 2004, Trends in Cognitive Sciences.
[76] J. Koenderink,et al. The internal representation of solid shape with respect to vision , 1979, Biological Cybernetics.
[77] E. Wojciulik,et al. Attention increases neural selectivity in the human lateral occipital complex , 2004, Nature Neuroscience.
[78] Koji Okamoto,et al. Quantitative visualization of micro-tube flow using micro-PIV , 2004, J. Vis..
[79] Daphna Weinshall,et al. A self-organizing multiple-view representation of 3D objects , 2004, Biological Cybernetics.
[80] M. Harries,et al. Viewer-centred and object-centred coding of heads in the macaque temporal cortex , 2004, Experimental Brain Research.
[81] Thomas Serre,et al. Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[82] Jochen Triesch,et al. Analysis of a Biologically-Inspired System for Real-time Object Recognition , 2005 .
[83] Heiko Wersing,et al. Rapid Online Learning of Objects in a Biologically Motivated Recognition Architecture , 2005, DAGM-Symposium.
[84] Abel G. Oliva,et al. Gist of a scene , 2005 .
[85] Julian Eggert,et al. Learning viewpoint invariant object representations using a temporal coherence principle , 2005, Biological Cybernetics.
[86] Martin A. Riedmiller,et al. Appearance-Based Robot Discrimination Using Eigenimages , 2006, RoboCup.
[87] Alain Rakotomamonjy,et al. Object Categorization Using Kernels Combining Graphs and Histograms of Gradients , 2006, ICIAR.
[88] J. Maunsell,et al. Feature-based attention in visual cortex , 2006, Trends in Neurosciences.
[89] Esa Rahtu,et al. Properties of Patch Based Approaches for the Recognition of Visual Object Classes , 2006, DAGM-Symposium.
[90] David G. Lowe,et al. Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[91] David G. Lowe,et al. University of British Columbia. , 1945, Canadian Medical Association journal.
[92] Martin Lauer,et al. Real-time 3D Ball Recognition using Perspective and Catadioptric Cameras , 2007, EMCR.
[93] Dirk B. Walther,et al. Task-set switching with natural scenes: measuring the cost of deploying top-down attention. , 2007, Journal of vision.
[94] B. Schölkopf,et al. Generalization and similarity in exemplar models of categorization: Insights from machine learning , 2008, Psychonomic bulletin & review.
[95] Martin A. Riedmiller,et al. Incremental GRLVQ: Learning relevant features for 3D object recognition , 2008, Neurocomputing.
[96] B. Sendhoff,et al. Evolution of Hierarchical Features for Visual Object Recognition , 2022 .