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Genetic Learning for Adaptive Image Segmentation
Bhanu, Bir; Lee, Sungkee
€ 194.08
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Description for Genetic Learning for Adaptive Image Segmentation
Paperback. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 271 pages, biography. BIC Classification: UYQ; UYQV. Category: (G) General (US: Trade). Dimension: 235 x 155 x 16. Weight in Grams: 456.
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 ... Read more
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 ... Read more
Product Details
Format
Paperback
Publication date
2012
Publisher
Springer-Verlag New York Inc. United States
Number of pages
271
Condition
New
Series
The Springer International Series in Engineering and Computer Science
Number of Pages
271
Place of Publication
New York, NY, United States
ISBN
9781461361985
SKU
V9781461361985
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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