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The Springer International Series in Engineering and Computer Science

Genetic Learning for Adaptive Image Segmentation

Authors: Bhanu, Bir, Sungkee Lee

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eBook $209.00
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  • ISBN 978-1-4615-2774-9
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Hardcover $269.00
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Softcover $269.00
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About this book

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.

Table of contents (10 chapters)

  • Introduction

    Bhanu, Bir (et al.)

    Pages 1-13

  • Image segmentation Techniques

    Bhanu, Bir (et al.)

    Pages 15-24

  • Segmentation as an Optimization Problem

    Bhanu, Bir (et al.)

    Pages 25-38

  • Baseline Adaptive Image Segmentation Using a Genetic Algorithm

    Bhanu, Bir (et al.)

    Pages 39-59

  • Basic Experimental Results – Indoor Imagery

    Bhanu, Bir (et al.)

    Pages 61-108

Buy this book

eBook $209.00
price for USA (gross)
  • ISBN 978-1-4615-2774-9
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $269.00
price for USA
  • ISBN 978-0-7923-9491-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $269.00
price for USA
  • ISBN 978-1-4613-6198-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Genetic Learning for Adaptive Image Segmentation
Authors
Series Title
The Springer International Series in Engineering and Computer Science
Series Volume
287
Copyright
1994
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4615-2774-9
DOI
10.1007/978-1-4615-2774-9
Hardcover ISBN
978-0-7923-9491-4
Softcover ISBN
978-1-4613-6198-5
Series ISSN
0893-3405
Edition Number
1
Number of Pages
XIX, 271
Topics