The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition.
These interconnected essays on three-dimensional visual object recognition present cutting-edge research by some of the most creative neuroscientific, cognitive, and computational scientists in the field.
Cassandra Moore and Patrick Cavanagh take a classic demonstration, the perception of “two-tone” images, and turn it into a method for understanding the nature of object representations in terms of surfaces and the interaction between bottom-up and top-down processes. Michael J. Tarr and Isabel Gauthier use computer graphics to study whether viewpoint-dependent recognition mechanisms can generalize between exemplars of perceptually defined classes. Melvyn A. Goodale and G. Keith Humphrey use innovative psychophysical techniques to investigate dissociable aspects of visual and spatial processing in brain-injured subjects. D.I. Perrett, M.W. Oram, and E. Ashbridge combine neurophysiological single-cell data from monkeys with computational analyses for a new way of thinking about the mechanisms that mediate viewpoint-dependent object recognition and mental rotation. Shimon Ullman also addresses possible mechanisms to account for viewpoint-dependent behavior, but from the perspective of machine vision. Finally, Philippe G. Schyns synthesizes work from many areas, to provide a coherent account of how stimulus class and recognition task interact.
The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition.
Embark on a groundbreaking exploration of visual object recognition with "Object Recognition in Man, Monkey, and Machine," a seminal work published by MIT Press. This First Edition paperback, delving into the fascinating intersection of neuroscience, cognitive science, and computer science, offers a comprehensive look at how humans, primates, and machines perceive and identify objects. Published in 1999, this collection of interconnected essays features cutting-edge research from leading figures in the field. The book tackles the complex problem of image-based object recognition, presenting diverse methodologies and perspectives. Dive into the depths of object representation with Cassandra Moore and Patrick Cavanagh as they revisit the classic "two-tone" image demonstration, transforming it into a powerful tool for understanding surface perception and the interplay between bottom-up and top-down cognitive processes. Explore how our brains integrate sensory input with prior knowledge to construct a coherent visual world. Michael J. Tarr and Isabel Gauthier harness the power of computer graphics to investigate the generalization capabilities of viewpoint-dependent recognition mechanisms. Can we extrapolate our understanding of objects from one angle to another? Their research illuminates the crucial role of experience and perceptual categorization in visual recognition. Gain insights into the dissociable aspects of visual and spatial processing with Melvyn A. Goodale and G. Keith Humphrey. Through innovative psychophysical techniques, they examine how brain injuries can selectively impair different facets of object recognition, revealing the intricate neural architecture that underlies our visual abilities. Their research provides a valuable contribution to our understanding of the brain's modularity. Delve into the neurophysiological underpinnings of viewpoint-dependent object recognition with D.I. Perrett, M.W. Oram, and E. Ashbridge. Combining single-cell data from monkeys with computational analyses, they propose a novel framework for understanding how the brain handles mental rotation and recognizes objects from varying perspectives. This groundbreaking work bridges the gap between neuroscience and computational modeling. Shimon Ullman approaches the challenge of viewpoint-dependent behavior from the perspective of machine vision, exploring potential algorithms and architectures that could enable machines to achieve human-like object recognition capabilities. His work highlights the computational complexities involved in visual perception. Finally, Philippe G. Schyns synthesizes findings from diverse areas to provide a unified account of how stimulus class and recognition task interact. He reveals the dynamic interplay between the characteristics of the object being viewed and the goals of the observer, demonstrating the flexibility and adaptability of our visual system. This book is ideal for researchers, students, and anyone interested in the cognitive and computational processes that underlie object recognition. It bridges the gap between different disciplines, offering a comprehensive and insightful overview of the field. "Object Recognition in Man, Monkey, and Machine" is a valuable resource for understanding the complexities of vision and its implications for both artificial intelligence and our understanding of the human mind. Its continued relevance stems from its comprehensive exploration of foundational questions that remain central to the fields of computer vision, cognitive science, and neuroscience. Don't miss the opportunity to own this important contribution to the study of visual perception.
