lecture3_part2_visual_search_correspondence_final.

文件名称: lecture3_part2_visual_search_correspondence_final.pdf

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详细说明：image matching and recognition with local features Correspondence Semi-local and global geometric relations Ransac and Hough TransformInstance-level recognition Last time Local invariant features(last lecture -C. Schmid) Today Camera geometry -review(J. Ponce) Correspondence, matching and recognition with loca features, efficient visual search(J. Sivic Next week: Very large scale visual indexing -(C. Schmid) Outline- the rest of the lecture Part 1. Image matching and recognition with local features Correspondence Semi-local and global geometric relations Robust estimation- RANSAC and Hough Transform Part 2. Going large-scale Approximate nearest neighbour matching Bag-of-visual-words representation Efficient visual search and extensions Applications Image matching and recognition with local features The goal: establish correspondence between two or more mages C Image points x and x are in correspondence if they are projections of the same 3D scene point X Images courtesy a Zisserman EXample I: Wide baseline matching. Establish correspondence between two(or more)images Useful in visual geometry: Camera calibration, 3D reconstruction Structure and motion estimation Scale/affine-invariant regions: SIFT, Harris-Laplace, etc Example ll: object recognition Establish correspondence between the target image and (multiple )images in the model database Model database Target 品管 image e [ Lowe, 1999 EXample l: Visual search Given a query image, find images depicting the same place object in a large unordered image collection H留时学一容 Find these landmarks in these images and 1M more Establish correspondence between the query image and all images from the database depicting the same object /scene Y fitts Query image Database image( s) Why is it difficult? Want to establish correspondence despite possibly large changes in scale, viewpoint, lighting and partial occlusion Scale Viewpoint Lighting Occlusion and the image collection can be very large(e.g. 1 M images

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