{"product_id":"9783030048303","title":"Visual Saliency: From Pixel-Level to Object-Level Analysis","description":"\u003ch1\u003eVisual Saliency: From Pixel-Level to Object-Level Analysis\u003c\/h1\u003e \u003ch2\u003eZhang, Jianming; Malmberg, Filip; Sclaroff, Stan\u003c\/h2\u003e \u003cp\u003eThis 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.\u003c\/p\u003e\u003cdiv\u003e\n\u003cp\u003eIn  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 fordifferent types of readers.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003eFor computer vision and image processing practitioners:\u003cp\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003eEfficient algorithms based on image distance transforms for two pixel-level saliency tasks;\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003ePromising deep learning techniques for two novel object-level saliency tasks;\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003eDeep neural network model pre-training with synthetic data;\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eThorough deep model analysis including useful visualization techniques and generalization tests;\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eFully reproducible with code, models and datasets available.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eFor researchers interested in the intersection between digital topological theories and computer vision problems:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003eSummary of theoretic findings and analysis of Boolean map distance;\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eTheoretic algorithmic analysis;\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003eApplications in salient object detection and eye fixation prediction.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eStudents majoring in image processing, machine learning and computer vision:\u003c\/p\u003e\n\u003cp\u003eThis book provides up-to-date supplementary reading material for course topics like connectivity based image processing, deep learning for image processing;\u003c\/p\u003e\n\u003cp\u003eSome easy-to-implement algorithms for course projects with data provided (as links in the book);\u003c\/p\u003e\n\u003cp\u003eHands-on programming exercises in digital topology and deep learning.\u003c\/p\u003e\n\u003c\/div\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2019-02-02\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783030048303\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-030-04831-0\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 138\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":47708650832012,"sku":"9783030048303","price":49.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783030048303.jpg?v=1776782017","url":"https:\/\/lateknightbooks.com\/products\/9783030048303","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}