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Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, “natural images” are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.
Vision science has grown hugely in the past decades, but there have been few books showing readers how to adopt a computional approach to understanding visual perception, along with the underlying mechanisms in the brain. This book explains the computational principles and models of biological visual processing, and in particular, primate vision.
This edited volume is the first of its kind to bridge the epistemological gap between primate ethologists and primate neurobiologists. Leading experts in several fields review work ranging from primate foraging behavior to the neurophysiology of motor control, from vocal communication to the functions of the auditory cortex.
An essential reference book for visual science.
This stimulating volume on vision extends well beyond the traditional areas of vision research and places the subject in a much broader philosophical context. The emphasis throughout is to integrate and illuminate the visual process. The first three parts of the volume provide authoritative overviews on computational vision and neural networks, on the neurophysiology of visual cortex processing, and on eye-movement research. Each of these parts illustrates how different research perspectives may jointly solve fundamental problems related to the efficiency of visual perception, to the relationship between vision and eye-movements and to the neurophysiological 'codes' underlying our visual perceptions. In the fourth part, leading vision scientists introduce the reader to some major philosophical problems in vision research such as the nature of 'ultimate' codes for perceptual events, the duality of psycho-physics, the bases of visual recognition and the paradigmatic foundations of computer-vision research.
Designed for students, scientists and engineers interested in learning about the core ideas of vision science, this volume brings together the broad range of data and theory accumulated in this field.
The proceedings of the 2000 Neural Information Processing Systems (NIPS) Conference.The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference.
A unique multidisciplinary perspective on the problem of visual object categorization.
This book constitutes the refereed conference proceedings of the 24rd Iberoamerican Congress on Pattern Recognition, CIARP 2019, held in Havana, Cuba, in October 2019. The 70 papers presented were carefully reviewed and selected from 128 submissions. The papers are organized in topical sections named: Data Mining: Natural Language Processing and Text Mining; Image Analysis and Retrieval; Machine Learning and Neural Networks; Mathematical Theory of Pattern Recognition; Pattern Recognition and Applications; Signals Analysis and Processing; Speech Recognition; Video Analysis.