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Dense image correspondences for comp...

Hassner, Tal.

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Dense image correspondences for computer vision

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dense image correspondences for computer vision/ edited by Tal Hassner, Ce Liu.
其他作者:	Hassner, Tal.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xii, 295 p. :ill., digital ;24 cm.
內容註:	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
標題:	Electrical engineering. -
電子資源:	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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