Vision: are models of object recognition catching up with the brain?
暂无分享,去创建一个
[1] T. Poggio,et al. General conditions for predictivity in learning theory , 2004, Nature.
[2] R. von der Heydt,et al. Coding of Border Ownership in Monkey Visual Cortex , 2000, The Journal of Neuroscience.
[3] Kunihiko Fukushima,et al. Cognitron: A self-organizing multilayered neural network , 1975, Biological Cybernetics.
[4] R. Desimone,et al. Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. , 1981, Journal of neurophysiology.
[5] Gábor Lugosi,et al. Introduction to Statistical Learning Theory , 2004, Advanced Lectures on Machine Learning.
[6] R. Zemel,et al. Experience-Dependent Perceptual Grouping and Object-Based Attention , 2002 .
[7] Tomaso A. Poggio,et al. Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[8] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[9] H H Bülthoff,et al. How are three-dimensional objects represented in the brain? , 1994, Cerebral cortex.
[10] Eric T. Carlson,et al. A neural code for three-dimensional object shape in macaque inferotemporal cortex , 2008, Nature Neuroscience.
[11] Elie Bienenstock,et al. Compositionality, MDL Priors, and Object Recognition , 1996, NIPS.
[12] Trevor Hastie,et al. An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.
[13] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[14] David G. Lowe,et al. University of British Columbia. , 1945, Canadian Medical Association journal.
[15] Roberto Brunelli,et al. Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..
[16] Wayne D. Gray,et al. Basic objects in natural categories , 1976, Cognitive Psychology.
[17] Irving Biederman,et al. Human image understanding: Recent research and a theory , 1985, Comput. Vis. Graph. Image Process..
[18] Tomaso A. Poggio,et al. Trainable pedestrian detection , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).
[19] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[20] Shimon Ullman,et al. Combined Top-Down/Bottom-Up Segmentation , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[22] B. Schiele,et al. Interleaved Object Categorization and Segmentation , 2003, BMVC.
[23] G. Orban,et al. Selectivity for 3D shape that reveals distinct areas within macaque inferior temporal cortex. , 2000, Science.
[24] Dan Roth,et al. Learning a Sparse Representation for Object Detection , 2002, ECCV.
[25] Takeo Kanade,et al. Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[27] Curvature in Depth for Object Representation , 2000, Neuron.
[28] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[29] William Grimson,et al. Object recognition by computer - the role of geometric constraints , 1991 .
[30] Tomaso A. Poggio,et al. Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[31] Pietro Perona,et al. Towards automatic discovery of object categories , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[32] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[33] Paul A. Viola,et al. Robust Real-time Object Detection , 2001 .
[34] T. Poggio,et al. A Bayesian inference theory of attention: neuroscience and algorithms , 2009 .
[35] Tai Sing Lee,et al. Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.
[36] G. Wahba. Spline models for observational data , 1990 .
[37] Y. Amit,et al. An integrated network for invariant visual detection and recognition , 2003, Vision Research.
[38] R. Shepard,et al. Mental Rotation of Three-Dimensional Objects , 1971, Science.
[39] E. Rolls,et al. INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.
[40] Charles Kemp,et al. The discovery of structural form , 2008, Proceedings of the National Academy of Sciences.
[41] Cordelia Schmid,et al. Toward Category-Level Object Recognition , 2006, Toward Category-Level Object Recognition.
[42] Tomaso A. Poggio,et al. A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[43] Felipe Cucker,et al. On the mathematical foundations of learning , 2001 .
[44] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[45] Shimon Ullman,et al. Image interpretation by a single bottom-up top-down cycle , 2008, Proceedings of the National Academy of Sciences.
[46] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[47] David A. McAllester,et al. A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[48] David G. Lowe,et al. Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..
[49] Ronen Basri,et al. Hierarchy and adaptivity in segmenting visual scenes , 2006, Nature.
[50] Thomas Serre,et al. A quantitative theory of immediate visual recognition. , 2007, Progress in brain research.
[51] Shimon Ullman,et al. Combining Class-Specific Fragments for Object Classification , 1999, BMVC.
[52] Keiji Tanaka,et al. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.
[53] M. Turk,et al. Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.
[54] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[55] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[56] Denis Fize,et al. Speed of processing in the human visual system , 1996, Nature.
[57] J. Hawkins,et al. On Intelligence , 2004 .
[58] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[59] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[60] T. Poggio. THE COMPUTATIONAL MAGIC OF THE VENTRAL STREAM: TOWARDS A THEORY , 2011 .
[61] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[62] Stephen Grossberg,et al. A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..
[63] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[64] Edmund T. Rolls,et al. A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures , 2000, Neural Computation.
[65] Heinrich H Bülthoff,et al. Image-based object recognition in man, monkey and machine , 1998, Cognition.
[66] 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.
[67] N. Logothetis,et al. View-dependent object recognition by monkeys , 1994, Current Biology.
[68] Simon J. Thorpe,et al. Ultra-Rapid Scene Categorization with a Wave of Spikes , 2002, Biologically Motivated Computer Vision.
[69] Lorenzo Rosasco,et al. Publisher Accessed Terms of Use Detailed Terms Mathematics of the Neural Response , 2022 .
[70] Andrew Zisserman,et al. Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[71] Shimon Ullman,et al. From simple innate biases to complex visual concepts , 2012, Proceedings of the National Academy of Sciences.
[72] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[73] Michael I. Jordan,et al. Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes , 2008, NIPS.
[74] Pietro Perona,et al. A Bayesian approach to unsupervised one-shot learning of object categories , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[75] Heiko Wersing,et al. Learning Optimized Features for Hierarchical Models of Invariant Object Recognition , 2003, Neural Computation.
[76] David D. Cox,et al. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..
[77] H. Barlow. Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .
[78] Michel Vidal-Naquet,et al. Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.
[79] Ronen Basri,et al. Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[80] T. Poggio,et al. The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.
[81] Michael I. Jordan. Graphical Models , 1998 .
[82] Cordelia Schmid,et al. Dataset Issues in Object Recognition , 2006, Toward Category-Level Object Recognition.
[83] Thomas Serre,et al. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .
[84] Mikhail Belkin,et al. Semi-Supervised Learning on Riemannian Manifolds , 2004, Machine Learning.
[85] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[86] M. Tarr,et al. Visual Object Recognition , 1996, ISTCS.
[87] 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).
[88] Jitendra Malik,et al. Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.