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Dense image correspondences for comp...

Hassner, Tal.

Dense image correspondences for computer vision[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	621.3
書名/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.
Contained By:	Springer eBooks
標題:	Electrical engineering.
標題:	Computer monitors - Design and construction.
標題:	Image processing.
標題:	Engineering.
標題:	Signal, Image and Speech Processing.
標題:	Image Processing and Computer Vision.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Communications Engineering, Networks.
ISBN:	9783319230481
ISBN:	9783319230474
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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-23048-1

Dense image correspondences for computer vision[electronic resource] /
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

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

183930
Electrical engineering.

LC Class. No.: TK153

Dewey Class. No.: 621.3

Dense image correspondences for computer vision[electronic resource] /
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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 Image processing. $3 342096
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650 2 4 $a Signal, Image and Speech Processing. $3 463860
650 2 4 $a Image Processing and Computer Vision. $3 463967
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
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700 1 $a Liu, Ce. $3 652842
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