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Dense image correspondences for comp...

Hassner, Tal.

Dense image correspondences for computer vision

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dense image correspondences for computer visionedited by Tal Hassner, Ce Liu.
other author:	Hassner, Tal.
Published:	Cham :Springer International Publishing :2016.
Description:	xii, 295 p. :ill., digital ;24 cm.
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:

454503
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.
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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.
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650 0 $a Computer monitors $x Design and construction. $3 737743
650 0 $a Image processing. $3 182627
650 1 4 $a Engineering. $3 210888
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
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