Open AccessBook
Computer and Robot Vision
TLDR
This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach.Abstract:
From the Publisher:
This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach. The discussion in "Volume I" focuses on image in, and image out or feature set out. "Volume II" covers the higher level techniques of illumination, perspective projection, analytical photogrammetry, motion, image matching, consistent labeling, model matching, and knowledge-based vision systems.read more
Citations
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Journal ArticleDOI
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
Book ChapterDOI
Machine learning for high-speed corner detection
Edward Rosten,Tom Drummond +1 more
TL;DR: It is shown that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time.
Journal ArticleDOI
SUSAN—A New Approach to Low Level Image Processing
TL;DR: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction and the resulting methods are accurate, noise resistant and fast.
Journal ArticleDOI
A tutorial on visual servo control
TL;DR: This article provides a tutorial introduction to visual servo control of robotic manipulators by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process.
Proceedings ArticleDOI
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Yuri Boykov,Marie-Pierre Jolly +1 more
TL;DR: In this paper, the user marks certain pixels as "object" or "background" to provide hard constraints for segmentation, and additional soft constraints incorporate both boundary and region information.