Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 287)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Genetic Learning for Adaptive Image Segmentation presents the first closed-loop image segmentation system that incorporates genetic and other algorithms to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions, such as time of day, time of year, weather, etc. Image segmentation performance is evaluated using multiple measures of segmentation quality. These quality measures include global characteristics of the entire image as well as local features of individual object regions in the image.
This adaptive image segmentation system provides continuous adaptation to normal environmental variations, exhibits learning capabilities, and provides robust performance when interacting with a dynamic environment. This research is directed towards adapting the performance of a well known existing segmentation algorithm (Phoenix) across a wide variety of environmental conditions which cause changes in the image characteristics. The book presents a large number of experimental results and compares performance with standard techniques used in computer vision for both consistency and quality of segmentation results. These results demonstrate, (a) the ability to adapt the segmentation performance in both indoor and outdoor color imagery, and (b) that learning from experience can be used to improve the segmentation performance over time.
Authors and Affiliations
Bibliographic Information
Book Title: Genetic Learning for Adaptive Image Segmentation
Authors: Bir Bhanu, Sungkee Lee
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-2774-9
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1994
Hardcover ISBN: 978-0-7923-9491-4Published: 30 September 1994
Softcover ISBN: 978-1-4613-6198-5Published: 22 December 2012
eBook ISBN: 978-1-4615-2774-9Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XIX, 271
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence