Dense Image Correspondences for Computer Vision
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portes grátis
Dense Image Correspondences for Computer Vision
Liu, Ce; Hassner, Tal
Springer International Publishing AG
08/2016
295
Mole
Inglês
9783319359144
15 a 20 dias
4686
Descrição não disponível.
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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Annotation Propagation;Data Driven;Dense Correspondence Estimation;Dense Correspondences;Dense Pixel Matching;Dense SIFT;Depth-transfer;Example Based;Label-transfer;SIFT-Flow;Scale-less SIFT
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
