Genetic Learning for Adaptive Image Segmentation

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June 30, 2019 | History

Genetic Learning for Adaptive Image Segmentation

Image segmentation is generally the first task in any automated image understanding application, such as autonomous vehicle navigation, object recognition, photointerpretation, etc. All subsequent tasks, such as feature extraction, object detection, and object recognition, rely heavily on the quality of segmentation. One of the fundamental weaknesses of current image segmentation algorithms is their inability to adapt the segmentation process as real-world changes are reflected in the image. Only after numerous modifications to an algorithm's control parameters can any current image segmentation technique be used to handle the diversity of images encountered in real-world applications. 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.

Publish Date
Publisher
Springer US
Language
English
Pages
271

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Edition Availability
Cover of: Genetic Learning for Adaptive Image Segmentation
Genetic Learning for Adaptive Image Segmentation
1994, Springer US
electronic resource / in English

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Book Details


Edition Notes

Online full text is restricted to subscribers.

Also available in print.

Mode of access: World Wide Web.

Published in
Boston, MA
Series
The Springer International Series in Engineering and Computer Science, Robotics: Vision, Manipulation and Sensors -- 287, Springer International Series in Engineering and Computer Science, Robotics: Vision, Manipulation and Sensors -- 287.

Classifications

Dewey Decimal Class
006.6
Library of Congress
T385, TA1637-1638, TK7882.P3, T385TA1637-1638TK788, TA1501-1820

The Physical Object

Format
[electronic resource] /
Pagination
1 online resource (xix, 271 pages).
Number of pages
271

Edition Identifiers

Open Library
OL27040874M
ISBN 10
1461361982, 1461527740
ISBN 13
9781461361985, 9781461527749
OCLC/WorldCat
852788198

Work Identifiers

Work ID
OL19852414W

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