The recognition of faces is a fundamental visual function with importance for social interaction and communication. Scientific interest in facial recognition has increased dramatically over the last decade. Researchers in such fields as psychology, neurophysiology, and functional imaging have published more than 10,000 studies on face processing.
David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr’s creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation.
Seeing has puzzled scientists and philosophers for centuries and it continues to do so. This new edition of a classic text offers an accessible but rigorous introduction to the computational approach to understanding biological visual systems.
Recent years have seen a burst of studies on the mouse eye and visual system, fueled in large part by the relatively recent ability to produce mice with precisely defined changes in gene sequence. Mouse models have contributed to a wide range of scientific breakthroughs for a number of ocular and neurological diseases and have allowed researchers to address fundamental issues that were difficult to approach with other experimental models.
The uniqueness of shape as a perceptual property lies in the fact that it is both complex and structured. Shapes are perceived veridically—perceived as they really are in the physical world, regardless of the orientation from which they are viewed. The constancy of the shape percept is the sine qua non of shape perception; you are not actually studying shape if constancy cannot be achieved with the stimulus you are using. Shape is the only perceptual attribute of an object that allows unambiguous identification.
In Things and Places, Zenon Pylyshyn argues that the process of incrementally constructing perceptual representations, solving the binding problem (determining which properties go together), and, more generally, grounding perceptual representations in experience arise from the nonconceptual capacity to pick out and keep track of a small number of sensory individuals.
This classic work in vision science, written by a leading figure in Germany's Gestalt movement in psychology and first published in 1936, addresses topics that remain of major interest to vision researchers today. Wolfgang Metzger's main argument, drawn from Gestalt theory, is that the objects we perceive in visual experience are not the objects themselves but perceptual effigies of those objects constructed by our brain according to natural rules.
This authoritative text is the only comprehensive reference available on electrophysiologic vision testing, offering both practical information on techniques and problems as well as basic physiology and anatomy, theoretical concepts, and clinical correlations. The second edition, of the widely used text, offers extensive new material and updated information: 65 of the 84 chapters are completely new, with the changes reflecting recent advances in the field. The book will continue to be an essential resource for practitioners and scholars from a range of disciplines within vision science.
This classic work on cyclopean perception has influenced a generation of vision researchers, cognitive scientists, and neuroscientists and has inspired artists, designers, and computer graphics pioneers. In Foundations of Cyclopean Perception (first published in 1971 and unavailable for years), Bela Julesz traced the visual information flow in the brain, analyzing how the brain combines separate images received from the two eyes to produce depth perception.
Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic.