國立虎尾科技大學 |

Dense Image Correspondences for Computer Vision

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Dense Image Correspondences for Computer Vision/ edited by Tal Hassner, Ce Liu.
other author:	Hassner, Tal.
Description:	XII, 295 p. 152 illus., 146 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Signal processing. -
Online resource:	https://doi.org/10.1007/978-3-319-23048-1
ISBN:	9783319230481

Dense Image Correspondences for Computer Vision
Dense Image Correspondences for Computer Vision[electronic resource] /edited by Tal Hassner, Ce Liu. - 1st ed. 2016. - XII, 295 p. 152 illus., 146 illus. in color.online resource.

Introduction to Dense Optical Flow -- SIFT Flow: Dense Correspondence across Scenes and its Applications -- Dense, Scale-Less Descriptors -- Scale-Space SIFT Flow -- Dense Segmentation-aware Descriptors -- SIFTpack: A Compact Representation for Efficient SIFT Matching -- In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features -- From Images to Depths and Back -- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling -- Joint Inference in Image Datasets via Dense Correspondence -- Dense Correspondences and Ancient Texts.

This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code, and data necessary for expediting the development of effective correspondence-based computer vision systems. · Provides in-depth coverage of dense-correspondence estimation · Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications · Includes information for designing computer vision systems that rely on efficient and robust correspondence estimation .

ISBN: 9783319230481

Standard No.: 10.1007/978-3-319-23048-1doiSubjects--Topical Terms:

561459
Signal processing.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Dense Image Correspondences for Computer Vision
LDR:03305nam a22004215i 4500 001 976740
003 DE-He213
005 20200702004535.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319230481 $9 978-3-319-23048-1
024 7 $a 10.1007/978-3-319-23048-1 $2 doi
035 $a 978-3-319-23048-1
050 4 $a TK5102.9
050 4 $a TA1637-1638
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.382 $2 23
245 1 0 $a Dense Image Correspondences for Computer Vision $h [electronic resource] / $c edited by Tal Hassner, Ce Liu.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XII, 295 p. 152 illus., 146 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction to Dense Optical Flow -- SIFT Flow: Dense Correspondence across Scenes and its Applications -- Dense, Scale-Less Descriptors -- Scale-Space SIFT Flow -- Dense Segmentation-aware Descriptors -- SIFTpack: A Compact Representation for Efficient SIFT Matching -- In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features -- From Images to Depths and Back -- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling -- Joint Inference in Image Datasets via Dense Correspondence -- Dense Correspondences and Ancient Texts.
520 $a This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code, and data necessary for expediting the development of effective correspondence-based computer vision systems. · Provides in-depth coverage of dense-correspondence estimation · Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications · Includes information for designing computer vision systems that rely on efficient and robust correspondence estimation .
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Optical data processing. $3 639187
650 0 $a Artificial intelligence. $3 559380
650 0 $a Electrical engineering. $3 596380
650 1 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Communications Engineering, Networks. $3 669809
700 1 $a Hassner, Tal. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1102715
700 1 $a Liu, Ce. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1102716
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319230474
776 0 8 $i Printed edition: $z 9783319230498
776 0 8 $i Printed edition: $z 9783319359144
856 4 0 $u https://doi.org/10.1007/978-3-319-23048-1
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)