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LUCID: Locally Uniform Comparison Image Descriptor
Andrew M Ziegler · Eric Christiansen · David Kriegman · Serge Belongie

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

Keypoint matching between pairs of images using popular descriptors like SIFT or a faster variant called SURF is at the heart of many computer vision algorithms including recognition, mosaicing, and structure from motion. For real-time mobile applications, very fast but less accurate descriptors like BRIEF and related methods use a random sampling of pairwise comparisons of pixel intensities in an image patch. Here, we introduce Locally Uniform Comparison Image Descriptor (LUCID), a simple description method based on permutation distances between the ordering of intensities of RGB values between two patches. LUCID is computable in linear time with respect to patch size and does not require floating point computation. An analysis reveals an underlying issue that limits the potential of BRIEF and related approaches compared to LUCID. Experiments demonstrate that LUCID is faster than BRIEF, and its accuracy is directly comparable to SURF while being more than an order of magnitude faster.

Author Information

Andrew M Ziegler (University of California, San Diego)
Eric Christiansen (University of California, San Diego)
David Kriegman (University of California, San Diego)
Serge Belongie (Cornell University)

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