Titlebook: Dense Image Correspondences for Computer Vision; Tal Hassner,Ce Liu Book 2016 Springer International Publishing Switzerland 2016 Annotatio

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SIFTpack: A Compact Representation for Efficient SIFT Matchingn time, for both finding nearest neighbors and computing all distances between all descriptors. The usefulness of SIFTpack is demonstrated as an alternative implementation for .-means dictionaries of visual words and for image retrieval.

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Depth Transfer: Depth Extraction from Videos Using Nonparametric Samplingths, and this depth estimation technique outperforms the state-of-the-art on benchmark databases. This method can be used to automatically convert a monoscopic video into stereo for 3D visualization demonstrated through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film ..

专心

Book 2016ce estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challengi

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DOMAINS – A Taxonomy: External Qualitiesariant descriptor (SID). We thereby deliver dense descriptors that are invariant to background changes, rotation, and/or scaling. We explore the merit of our technique in conjunction with large displacement motion estimation and wide-baseline stereo, and demonstrate that exploiting segmentation information yields clear improvements.

讽刺

Dense Segmentation-Aware Descriptorsariant descriptor (SID). We thereby deliver dense descriptors that are invariant to background changes, rotation, and/or scaling. We explore the merit of our technique in conjunction with large displacement motion estimation and wide-baseline stereo, and demonstrate that exploiting segmentation information yields clear improvements.

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