LEADER 03281nam 2200433 u 450 001 9911019714703321 005 20230705080239.0 010 $a9781119859024 010 $a1119859026 010 $a9781119859031 010 $a1119859034 010 $a9781119859048 010 $a1119859042 035 $a(CKB)27378058800041 035 $a(NjHacI)9927378058800041 035 $a(Perlego)3739427 035 $a(EXLCZ)9927378058800041 100 $a20230705d2023 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aImage segmentation $eprinciples, techniques, and applications /$fTao Lei 210 1$aHoboken, NJ :$cWiley-Blackwell,$d2023. 215 $a1 online resource 330 8 $aImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors-such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression-to assist graduate students and researchers apply and improve image segmentation in their work. * Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. * Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. * Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. * Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods. 606 $aImage segmentation 606 $aImage segmentation$xMathematical models 615 0$aImage segmentation. 615 0$aImage segmentation$xMathematical models. 676 $a006.6 700 $aLei$b Tao$01842009 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9911019714703321 996 $aImage segmentation$94421940 997 $aUNINA