Chapter 7: Object and Scene Perception Cover Sheet
暂无分享,去创建一个
[1] Tomaso Poggio,et al. Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. , 2004, Journal of neurophysiology.
[2] S Ullman,et al. Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.
[3] Geoffrey E. Hinton. Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.
[4] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[5] Jeremy M. Wolfe,et al. Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.
[6] Antonio Torralba,et al. Using the forest to see the trees: exploiting context for visual object detection and localization , 2010, CACM.
[7] Thomas Serre,et al. A neuromorphic approach to computer vision , 2010, Commun. ACM.
[8] Nando de Freitas,et al. Target-directed attention: Sequential decision-making for gaze planning , 2008, 2008 IEEE International Conference on Robotics and Automation.
[9] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[10] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[11] 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.
[12] 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.
[13] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[14] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[15] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] C. Koch,et al. A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.
[17] Antonio Torralba,et al. Exploiting hierarchical context on a large database of object categories , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[18] Trevor Darrell. Reinforcement Learning of Active Recognition Behaviors , 1997, NIPS 1997.
[19] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[20] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[21] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[22] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[23] Satyajit Rao,et al. Visual routines and attention , 1998 .
[24] Krista A. Ehinger,et al. SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[25] Keiji Tanaka,et al. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.
[26] Tomaso A. Poggio,et al. Biophysical Models of Neural Computation: Max and Tuning Circuits , 2006, WImBI.
[27] Antonio Torralba,et al. Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.
[28] Michael C. Mozer,et al. Experience-Guided Search: A Theory of Attentional Control , 2007, NIPS.
[29] Antonio Torralba,et al. Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.
[30] T. Poggio,et al. What and where: A Bayesian inference theory of attention , 2010, Vision Research.
[31] Laurent Itti,et al. An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[32] A. Oliva,et al. From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition , 1994 .
[33] tephen E. Palmer. The effects of contextual scenes on the identification of objects , 1975, Memory & cognition.
[34] Geoffrey E. Hinton,et al. Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[35] Antonio Torralba,et al. Recognizing indoor scenes , 2009, CVPR.
[36] Matthew H. Wilder,et al. An integrative, experience-based theory of attentional control. , 2011, Journal of vision.
[37] A. Torralba,et al. The role of context in object recognition , 2007, Trends in Cognitive Sciences.
[38] B. Fischhoff,et al. Journal of Experimental Psychology: Human Learning and Memory , 1980 .
[39] S. Thorpe,et al. Speed of processing in the human visual system , 1996, Nature.
[40] D H HUBEL,et al. RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.
[41] Christof Koch,et al. Comparison of feature combination strategies for saliency-based visual attention systems , 1999, Electronic Imaging.