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Genetic Learning for Adaptive Image Segmentation

Name: Genetic Learning for Adaptive Image Segmentation
Brand: Springer-Verlag New York Inc.
SKU: 9781461361985
Price: 149.91 EUR
Availability: InStock

Autor: Bir Bhanu, Sungkee Lee

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer-Verlag New York Inc.

Dostupnosť: Skladom u dodávateľa

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149.91 €

Image segmentation is generally the first task in any automated image understanding application, suc...

Informácie o knihe

Autor

Bir Bhanu, Sungkee Lee

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2012

Stránok

271

EAN

9781461361985

ISBN

1461361982

Enbook ID

06796583

Vydavateľ

Springer-Verlag New York Inc.

Hmotnosť

456

Rozmery

155 x 235 x 17

Kompletný popis

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. §