Machine Vision : Theory, Algorithms, Practicalities

Rating:
Summary: Good structured reference, very useful
Review: A very clearly structured book which is useful as a reference. Covers a lot of subjects (filtering, detection of shapes [lines, circles, holes and more], pattern matching/recognition, motion, invariants, ...), including the implementation aspects (hard/software). The chapters sometimes do not go much into deep but provide further references. Recommended book!

Rating:
Summary: Excellent resource
Review: Covers many aspects of vision, from basic image processing through high level scene analysis. It doesn't always go down to the nitty-gritty source code level for every topic, but it does provide the direction to handle most every common machine vision problem. Of the ten or so general machine vision books on my easy-access shelf, this is the one I seem to pull down the most.

Rating:
Summary: Excellent resource
Review: Covers many aspects of vision, from basic image processing through high level scene analysis. It doesn't always go down to the nitty-gritty source code level for every topic, but it does provide the direction to handle most every common machine vision problem. Of the ten or so general machine vision books on my easy-access shelf, this is the one I seem to pull down the most.

Rating:
Summary: Solid Foundation to computer Vision
Review: First of all I like this book very much. This book provides a solid and concrete foundation to computer vision from engineering point of view. The basic issues are treated very well in the conceptual and practical levels (e.g. edge detection). I came from a photogrammetry background, which means that the geometric aspects are very dominant in my thinking, and this book emphasize many geometric concepts in computer vision specially the treatment of Hough Transform as a main theme in the book. I recommend this book to the practitioners in spatial sciences (GIS, Remote sensing, Photogrammetry, etc) as well as the general community of computer vision.