Wide-base line matching propose : SIFT 、 GLOH 、 SURF… (histogram based descriptor) – Good performance and robustness to image transformations. – High computational cost and sensitivity to occlusions. Purpose – Design a descriptor that is as robust as SIFT or GLOH but can be computed much more effectively and handle occlusions. Introduction
No velty – introduces DAISY local image descriptor Introduction (cont.)
No velty – introduces DAISY local image descriptor Introduction (cont.) SIFT descriptor is a 3–D histogram in which two dimensions correspond to image spatial dimensions and the additional dimension to the image gradient direction (normally discrete into 8 bins)
No velty – introduces DAISY local image descriptor Introduction (cont.) * S. Winder and M. Brown. Learning Local Image Descriptors in CVPR’07 Improved performance : + Precise localization + Rotational Robustness
No velty – introduces DAISY local image descriptor Introduction (cont.) Replacing weighted sums by convolutions
Results Using low-resolution of the Brussels images 768x510 (2048x1360 origin)  Combined Depth and Outlier Estimation in Multi-View Stereo, CVPR’06
Results Using low-resolution of the Rathaus images 768x512 (3072x2048 origin) The holes are caused by the fact that a lot of the texture is not visible.  Dense Matching of Multiple Wide-Baseline Views, ICCV’03
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