Visual Pattern Analyzers
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Visual Pattern Analyzers
Author: Norma Van Surdam Graham
language: en
Publisher: Oxford University Press
Release Date: 1989-09-21
The visual system must extract from the light that falls on the retina meaningful information about what is where in our environment. At an early stage it analyzes the incoming sensory data along many dimensions of pattern vision, e.g. spatial frequency, orientation, velocity, eye-of-origin. Visual Pattern Analyzers provides a definitive account of current knowledge about this stage of visual processing. Nowhere else can such a comprehensive summarty of the lower level pattern analyzers be found. The book's emphasis is on psychophysical experiments measuring the detection and identification of near-threshold patterns -- and the mathematical models, such as multidimensional signal-detection theory, used to draw inferences from such experimental results -- but neurophysiological evidence is presented and compared critically to the psychophysical evidence. Introductory material on psychophysical methods, signal detection theory, and the mathematics of Fourier analysis is given in order to make the book more accessible to all who are interested in the lower or higher levels of visual perception. This volume will be of great value to researchers and graduate students in the fields of vision and perception. Within the scientific community there is wide interest in the visual system, and the book will be of use to investigators in many fields, including psychophysics, neuroscience, ophthalmology and optics, computer science, and cognitive and experimental psychology.
Low-Rank Models in Visual Analysis
Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. - Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications - Provides a full and clear explanation of the theory behind the models - Includes detailed proofs in the appendices
Textual and Visual Information Retrieval using Query Refinement and Pattern Analysis
This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book’s overarching goal is to introduce readers to new ideas in an easy-to-follow manner.