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About this book
Computer vision seeks a process that starts with a noisy,
ambiguous signal from a TV camera and ends with a high-level
description of discrete objects located in 3-dimensional
space and identified in a human classification.
This book addresses the process at several levels. First to
be treated are the low-level image-processing issues of
noise removaland smoothing while preserving important lines
and singularities in an image. At a slightly higher level, a
robust contour tracing algorithm is described that produces
a cartoon of the important lines in the image. Thirdis the
high-level task of reconstructing the geometry of objects in
the scene.
The book has two aims: to give the computer vision community
a new approach to early visual processing, in the form of
image segmentation that incorporates occlusion at a low
level, and to introduce real computer algorithms that do a
better job than what most vision programmers use currently.
The algorithms are:
- a nonlinear filter that reduces noise and enhances edges,
- an edge detector that also finds corners and produces
smoothed contours rather than bitmaps,
- an algorithm for filling gaps in contours.
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Table of contents (6 chapters)
Bibliographic Information
Book Title: Filtering, Segmentation and Depth
Authors: Mark Nitzberg, David Mumford, Takahiro Shiota
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-56484-5
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1993
Softcover ISBN: 978-3-540-56484-3Published: 30 March 1993
eBook ISBN: 978-3-540-47570-5Published: 30 June 2005
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: VIII, 152
Topics: Image Processing and Computer Vision, Artificial Intelligence, Software Engineering/Programming and Operating Systems
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