Logo - springer
Slogan - springer

Computer Science - Image Processing | Genetic Learning for Adaptive Image Segmentation

Genetic Learning for Adaptive Image Segmentation

Bhanu, Bir, Sungkee Lee

1994, XIX, 271 p.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$209.00

(net) price for USA

ISBN 978-1-4615-2774-9

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$269.00

(net) price for USA

ISBN 978-0-7923-9491-4

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$269.00

(net) price for USA

ISBN 978-1-4613-6198-5

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • 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.

Content Level » Research

Keywords » Navigation - algorithms - cognition - computer vision - control - genetic algorithms - image segmentation - learning - object recognition - optimization - performance

Related subjects » Artificial Intelligence - Image Processing

Table of contents 

List of Figures. Preface. 1. Introduction. 2. Image Segmentation Techniques. 3. Segmentation as an Optimization Problem. 4. Baseline Adaptive Image Segmentation Using a Genetic Algorithm. 5. Basic Experimental Results -- Indoor Imagery. 6. Basic Experimental Results -- Outdoor Imagery. 7. Evaluating the Effectiveness of the Baseline Technique -- Further Experiments. 8. Hybrid Search Scheme for Adaptive Image Segmentation. 9. Simultaneous Optimization of Global and Local Evaluation Measures. 10. Summary. References. Index.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Computer Imaging, Vision, Pattern Recognition and Graphics.