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Dense image correspondences for comp...

Hassner, Tal.

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Dense image correspondences for computer vision

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dense image correspondences for computer vision/ edited by Tal Hassner, Ce Liu.
other author:	Hassner, Tal.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xii, 295 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Electrical engineering. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-23048-1
ISBN:	9783319230481$q(electronic bk.)

Dense image correspondences for computer vision
Dense image correspondences for computer vision[electronic resource] /edited by Tal Hassner, Ce Liu. - Cham :Springer International Publishing :2016. - xii, 295 p. :ill., digital ;24 cm.

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.

ISBN: 9783319230481$q(electronic bk.)

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

649834
Electrical engineering.

LC Class. No.: TK153

Dewey Class. No.: 621.3

Dense image correspondences for computer vision
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245 0 0 $a Dense image correspondences for computer vision $h [electronic resource] / $c edited by Tal Hassner, Ce Liu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xii, 295 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Electrical engineering. $3 649834
650 0 $a Computer monitors $x Design and construction. $3 2179939
650 0 $a Image processing. $3 621117
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Communications Engineering, Networks. $3 891094
700 1 $a Hassner, Tal. $3 2179937
700 1 $a Liu, Ce. $3 2179938
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23048-1
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