Learning Generic Invariances in Object Recognition: Translation and Scale
暂无分享,去创建一个
[1] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[2] D. M. Green,et al. Signal detection theory and psychophysics , 1966 .
[3] D. B. Bender,et al. Visual Receptive Fields of Neurons in Inferotemporal Cortex of the Monkey , 1969, Science.
[4] David H. Foster,et al. Visual Comparison of Rotated and Reflected Random-Dot Patterns as a Function of Their Positional Symmetry and Separation in the Field* , 1981 .
[5] R. Desimone,et al. Visual properties of neurons in area V4 of the macaque: sensitivity to stimulus form. , 1987, Journal of neurophysiology.
[6] J. O'Regan,et al. Some results on translation invariance in the human visual system. , 1990, Spatial vision.
[7] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[8] T Poggio,et al. Fast perceptual learning in visual hyperacuity. , 1991, Science.
[9] David I. Perrett,et al. Neurophysiology of shape processing , 1993, Image Vis. Comput..
[10] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[11] Peter Földiák,et al. Learning generalisation and localisation: Competition for stimulus type and receptive field , 1996, Neurocomputing.
[12] H. Bülthoff,et al. Face recognition under varying poses: The role of texture and shape , 1996, Vision Research.
[13] M. Fahle,et al. The role of visual field position in pattern–discrimination learning , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[14] E. Rolls,et al. INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.
[15] D. Johnston,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .
[16] M. Fahle,et al. Limited translation invariance of human visual pattern recognition , 1998, Perception & psychophysics.
[17] H. Barrett,et al. Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.
[18] G. Bi,et al. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.
[19] Shimon Ullman,et al. Computation of pattern invariance in brain-like structures , 1999, Neural Networks.
[20] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[21] Konrad P. Körding,et al. Extracting Slow Subspaces from Natural Videos Leads to Complex Cells , 2001, ICANN.
[22] 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.
[23] S. Edelman,et al. Imperfect Invariance to Object Translation in the Discrimination of Complex Shapes , 2001, Perception.
[24] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[25] Edmund T. Rolls,et al. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects , 2002, Neural Computation.
[26] J. Maunsell,et al. Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. , 2003, Journal of neurophysiology.
[27] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[28] 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.
[29] Michael W. Spratling. Learning viewpoint invariant perceptual representations from cluttered images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] J. DiCarlo,et al. 'Breaking' position-invariant object recognition , 2005, Nature Neuroscience.
[31] Matthew A. Kupinski,et al. Objective Assessment of Image Quality , 2005 .
[32] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[33] 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 .
[34] Stuart Geman,et al. Invariance and selectivity in the ventral visual pathway , 2006, Journal of Physiology-Paris.
[35] David G. Lowe,et al. University of British Columbia. , 1945, Canadian Medical Association journal.
[36] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[37] Thomas Serre,et al. Learning complex cell invariance from natural videos: A plausibility proof , 2007 .
[38] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Tomaso Poggio,et al. Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex , 2007, The Journal of Neuroscience.
[40] C. Koch,et al. Decoding visual inputs from multiple neurons in the human temporal lobe. , 2007, Journal of neurophysiology.
[41] Timothée Masquelier,et al. Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..
[42] David J. Freedman,et al. Dynamic population coding of category information in inferior temporal and prefrontal cortex. , 2008, Journal of neurophysiology.
[43] S. Gerber,et al. Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex , 2008 .
[44] Niko Wilbert,et al. Invariant Object Recognition with Slow Feature Analysis , 2008, ICANN.
[45] G. Kreiman,et al. Timing, Timing, Timing: Fast Decoding of Object Information from Intracranial Field Potentials in Human Visual Cortex , 2009, Neuron.
[46] Robbe L. T. Goris,et al. Frontiers in Computational Neuroscience Computational Neuroscience Neural Representations That Support Invariant Object Recognition , 2022 .
[47] David D. Cox,et al. What response properties do individual neurons need to underlie position and clutter "invariant" object recognition? , 2009, Journal of neurophysiology.
[48] J. DiCarlo,et al. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex , 2010, Neuron.