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Visual Information Processing documents the Proceedings of the Eighth Annual Carnegie Symposium on Cognition, held at the Carnegie-Mellon University, Pittsburgh, Pennsylvania on May 19, 1972. This book compiles papers on the results from ongoing, programmatic research projects that aim to develop information processing models of cognition. The role of visual processes in cognition and language comprehension and explicit computer simulation of the memory-scanning task are discussed in detail. Other topics include the chronometrie studies of the rotation of mental images; function of visual imagery in elementary mathematics; and semantic prerequisites for comprehension. The production system for counting, subitizing, and adding; visual processes in linguistic comprehension; and models of control structures are also deliberated. This publication is a good source for students and researchers interested in images.
This text provides an introduction to computational aspects of early vision, in particular, color, stereo, and visual navigation. It integrates approaches from psychophysics and quantitative neurobiology, as well as theories and algorithms from machine vision and photogrammetry. When presenting mathematical material, it uses detailed verbal descriptions and illustrations to clarify complex points. The text is suitable for upper-level students in neuroscience, biology, and psychology who have basic mathematical skills and are interested in studying the mathematical modeling of perception.
Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions. 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. This MIT Press edition makes Marr's influential work available to a new generation of students and scientists. In Marr's framework, the process of vision constructs a set of representations, starting from a description of the input image and culminating with a description of three-dimensional objects in the surrounding environment. A central theme, and one that has had far-reaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis—in Marr's framework, the computational level, the algorithmic level, and the hardware implementation level. Now, thirty years later, the main problems that occupied Marr remain fundamental open problems in the study of perception. Vision provides inspiration for the continuing efforts to integrate knowledge from cognition and computation to understand vision and the brain.
Recent vision research has led to the emergence of new techniques that offer exciting potential for a more complete assessment of vision in clinical, industrial, and military settings. Emergent Techniques for Assessment of Visual Performance examines four areas of vision testing that offer potential for improved assessment of visual capability including: contrast sensitivity function, dark-focus of accommodation, dynamic visual acuity and dynamic depth tracking, and ambient and focal vision. In contrast to studies of accepted practices, this report focuses on emerging techniques that could help determine whether people have the vision necessary to do their jobs. In addition to examining some of these emerging techniques, the report identifies their usefulness in predicting performance on other visual and visual-motor tasks, and makes recommendations for future research. Emergent Techniques for Assessment of Visual Performance provides summary recommendations for research that will have significant value and policy implications for the next 5 to 10 years. The content and conclusions of this report can serve as a useful resource for those responsible for screening industrial and military visual function.
"Neurobiology of Cognition and Behavior" is a cognitive neuroscience that maps cognitive/behavioral units with anatomical regions in the human brain. The brain-behavioral associations are based on functional neuroimaging combined with lesion studies. The findings will be used to explain differences in clinical syndromes with videos of patients included.
Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.
This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.