000			04337nam a22005295i 4500
001			978-3-030-04831-0
003			DE-He213
005			20240423125021.0
007			cr nn 008mamaa
008			190121s2019 sz | s |||| 0|eng d
020			_a9783030048310 _9978-3-030-04831-0
024	7		_a10.1007/978-3-030-04831-0 _2doi
050		4	_aTA1634
072		7	_aUYQV _2bicssc
072		7	_aCOM016000 _2bisacsh
072		7	_aUYQV _2thema
082	0	4	_a006.37 _223
100	1		_aZhang, Jianming. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aVisual Saliency: From Pixel-Level to Object-Level Analysis _h[electronic resource] / _cby Jianming Zhang, Filip Malmberg, Stan Sclaroff.
250			_a1st ed. 2019.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019.
300			_aVII, 138 p. 47 illus., 44 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_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.
520			_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 fordifferent 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.
650		0	_aComputer vision.
650		0	_aSignal processing.
650		0	_aComputer science _xMathematics.
650	1	4	_aComputer Vision.
650	2	4	_aSignal, Speech and Image Processing.
650	2	4	_aMathematics of Computing.
700	1		_aMalmberg, Filip. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSclaroff, Stan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030048303
776	0	8	_iPrinted edition: _z9783030048327
856	4	0	_uhttps://doi.org/10.1007/978-3-030-04831-0
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c173282 _d173282