by Dana H. Ballard, Christopher M. Brown
Publisher: Prentice Hall 1982
Number of pages: 539
Computer vision is the construction of explicit, meaningful descriptions of physical objects from images. Image understanding is very different from image processing, which studies image-to-image transformations, not explicit description building. Descriptions are a prerequisite for recognizing, manipulating, and thinking about objects. Parts of the book assume some mathematical and computing background (calculus, linear algebra, data structures, numerical methods). However, throughout the book mathematical rigor takes a backseat to concepts. Our intent is to transmit a set of ideas about a new field to the widest possible audience.
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by Dilip K. Prasad - arXiv
We propose a new object detection/recognition method, which improves over the existing methods in every stage of the object detection/recognition process. In addition to the usual features, we propose to use geometric shapes as additional features.
by David Vernon - Prentice Hall
This book is a comprehensive introduction to machine vision, it will allow the reader to quickly comprehend the essentials of this topic. Emphasis is on a range of the tools and techniques for image acquisition, processing, and analysis.
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Introductory textbook and a research monograph on modelling the statistical structure of natural images. The statistical structure of natural images is described using a number of statistical models whose parameters are estimated from image samples.
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This book reports recent advances in the use of pattern recognition techniques for computer and robot vision. The areas of low level vision such as segmentation, edge detection, and region identification, are the focus of this book.