LEADER 04688nam 22005895 450 001 9910337571303321 005 20240326002759.0 010 $a3-030-04831-4 024 7 $a10.1007/978-3-030-04831-0 035 $a(CKB)4100000007522638 035 $a(MiAaPQ)EBC5642612 035 $a(DE-He213)978-3-030-04831-0 035 $a(PPN)233802134 035 $a(EXLCZ)994100000007522638 100 $a20190121d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aVisual Saliency: From Pixel-Level to Object-Level Analysis /$fby Jianming Zhang, Filip Malmberg, Stan Sclaroff 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (138 pages) 311 0 $a3-030-04830-6 327 $a1 Overview -- 2 Boolean Map Saliency: A Surprisingly Simple Method -- 3 A Distance Transform Perspective -- 4 Efficient Distance Transform for Salient Region Detection -- 5 Salient Object Subitizing -- 6 Unconstrained Salient Object Detection -- 7 Conclusion and Future Work. 330 $aThis book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc. In this book, the authors present methods for both traditional and emerging saliency computation tasks, ranging from classical low-level tasks like pixel-level saliency detection to object-level tasks such as subitizing and salient object detection. For low-level tasks, the authors focus on pixel-level image processing approaches based on efficient distance transform. For object-level tasks, the authors propose data-driven methods using deep convolutional neural networks. The book includes both empirical and theoretical studies, together with implementation details of the proposed methods. Below are the key features for different types of readers. For computer vision and image processing practitioners: Efficient algorithms based on image distance transforms for two pixel-level saliency tasks; Promising deep learning techniques for two novel object-level saliency tasks; Deep neural network model pre-training with synthetic data; Thorough deep model analysis including useful visualization techniques and generalization tests; Fully reproducible with code, models and datasets available. For researchers interested in the intersection between digital topological theories and computer vision problems: Summary of theoretic findings and analysis of Boolean map distance; Theoretic algorithmic analysis; Applications in salient object detection and eye fixation prediction. Students majoring in image processing, machine learning and computer vision: This book provides up-to-date supplementary reading material for course topics like connectivity based image processing, deep learning for image processing; Some easy-to-implement algorithms for course projects with data provided (as links in the book); Hands-on programming exercises in digital topology and deep learning. 606 $aOptical data processing 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aComputer science$xMathematics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aMathematics of Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I17001 615 0$aOptical data processing. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aComputer science$xMathematics. 615 14$aImage Processing and Computer Vision. 615 24$aSignal, Image and Speech Processing. 615 24$aMathematics of Computing. 676 $a621.367 676 $a006.42 700 $aZhang$b Jianming$c(Research scientist).$4aut$4http://id.loc.gov/vocabulary/relators/aut$01745171 702 $aMalmberg$b Filip$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSclaroff$b Stan$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337571303321 996 $aVisual Saliency: From Pixel-Level to Object-Level Analysis$94175671 997 $aUNINA