{"product_id":"9780792394914","title":"The Springer International Series in Engineering and Computer Science","description":"\u003ch1\u003eThe Springer International Series in Engineering and Computer Science\u003c\/h1\u003e \u003ch2\u003eBhanu, Bir; Sungkee Lee\u003c\/h2\u003e \u003cp\u003eImage 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. \u003cbr\u003e  \u003cem\u003eGenetic Learning for Adaptive Image Segmentation\u003c\/em\u003e 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. \u003cbr\u003e  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. \u003cbr\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 1994-09-30\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9780792394914\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4615-2774-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 271\u003c\/p\u003e ","brand":"Springer US","offers":[{"title":"Default Title","offer_id":45578400399500,"sku":"9780792394914","price":152.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9780792394914.jpg?v=1771511075","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9780792394914","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}