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Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision
Deepti Pachauri · Risi Kondor · Gautam Sargur · Vikas Singh

Wed Dec 10 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

Consistently matching keypoints across images, and the related problem of finding clusters of nearby images, are critical components of various tasks in Computer Vision, including Structure from Motion (SfM). Unfortunately, occlusion and large repetitive structures tend to mislead most currently used matching algorithms, leading to characteristic pathologies in the final output. In this paper we introduce a new method, Permutations Diffusion Maps (PDM), to solve the matching problem, as well as a related new affinity measure, derived using ideas from harmonic analysis on the symmetric group. We show that just by using it as a preprocessing step to existing SfM pipelines, PDM can greatly improve reconstruction quality on difficult datasets.

Author Information

Deepti Pachauri (3M)
Risi Kondor (The University of Chicago)
Gautam Sargur (University of Wisconsin Madison)
Vikas Singh (UW-Madison)

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