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Adam Finkelstein
Researcher at Princeton University
Publications - 128
Citations - 12897
Adam Finkelstein is an academic researcher from Princeton University. The author has contributed to research in topics: Rendering (computer graphics) & Animation. The author has an hindex of 50, co-authored 123 publications receiving 11392 citations. Previous affiliations of Adam Finkelstein include Adobe Systems & University of Washington.
Papers
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Journal ArticleDOI
PatchMatch: a randomized correspondence algorithm for structural image editing
TL;DR: This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches, and proposes additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods.
Proceedings ArticleDOI
Fast multiresolution image querying
TL;DR: An “image querying metric” is introduced that operates on how many significant wavelet coefficients the query has in common with potential targets, and includes parameters that can be tuned, using a statistical analysis, to accommodate the kinds of image distortions found in different types of image queries.
Book ChapterDOI
The generalized patchmatch correspondence algorithm
TL;DR: This paper generalizes PatchMatch in three ways: to find k nearest neighbors, as opposed to just one, to search across scales and rotations, in addition to just translations, and to match using arbitrary descriptors and distances, not just sum-of-squared-differences on patch colors.
Proceedings ArticleDOI
Suggestive contours for conveying shape
TL;DR: A non-photorealistic rendering system that conveys shape using lines, and shows that suggestive contours can be drawn consistently with true contours, because they anticipate and extend them.
Proceedings ArticleDOI
Lapped textures
TL;DR: This work optimize the parametrization of each surface patch such that the tangential vector field aligns everywhere with the standard frame of the texture patch, and shows that this optimization is solved efficiently as a sparse linear system.