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» Automatic feature selection in neuroevolution
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CGF
2008
144views more  CGF 2008»
13 years 11 months ago
Deformation-Driven Shape Correspondence
Non-rigid 3D shape correspondence is a fundamental and difficult problem. Most applications which require a correspondence rely on manually selected markers. Without user assistan...
Hao Zhang 0002, Alla Sheffer, Daniel Cohen-Or, Qua...
SIGIR
2011
ACM
13 years 2 months ago
Pseudo test collections for learning web search ranking functions
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
ICCV
2007
IEEE
15 years 1 months ago
Scene Summarization for Online Image Collections
We formulate the problem of scene summarization as selecting a set of images that efficiently represents the visual content of a given scene. The ideal summary presents the most i...
Ian Simon, Noah Snavely, Steven M. Seitz
CVPR
2009
IEEE
14 years 3 months ago
Learning to associate: HybridBoosted multi-target tracker for crowded scene
We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragm...
Yuan Li, Chang Huang, Ram Nevatia
ICPR
2008
IEEE
15 years 11 days ago
A contrario matching of SIFT-like descriptors
In this paper, the matching of SIFT-like features [5] between images is studied. The goal is to decide which matches between descriptors of two datasets should be selected. This m...
Julie Delon, Julien Rabin, Yann Gousseau