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» On regularization algorithms in learning theory
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154
Voted
CVPR
2007
IEEE
16 years 6 months ago
Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing
We present a method for the simultaneous detection and segmentation of people from static images. The proposed technique requires no manual segmentation during training, and explo...
Vinay Sharma, James W. Davis
ICPR
2008
IEEE
15 years 11 months ago
Face super-resolution using 8-connected Markov Random Fields with embedded prior
In patch based face super-resolution method, the patch size is usually very small, and neighbor patches’ relationship via overlapped regions is only to keep smoothness of recons...
Kai Guo, Xiaokang Yang, Rui Zhang, Guangtao Zhai, ...
129
Voted
IPPS
2007
IEEE
15 years 11 months ago
Programming Distributed Memory Sytems Using OpenMP
OpenMP has emerged as an important model and language extension for shared-memory parallel programming. On shared-memory platforms, OpenMP offers an intuitive, incremental approac...
Ayon Basumallik, Seung-Jai Min, Rudolf Eigenmann
170
Voted
ISWC
1997
IEEE
15 years 8 months ago
Stochasticks: Augmenting the Billiards Experience with Probabilistic Vision and Wearable Computers
We propose a practical application of wearable computing and augmented reality which enhances the game of billiards. A vision algorithm is implemented which operates in interactiv...
Tony Jebara, Cyrus Eyster, Joshua Weaver, Thad Sta...
167
Voted
CVPR
2011
IEEE
14 years 8 months ago
Connecting Non-Quadratic Variational Models and MRFs
Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approaches are ubiquitous in low-level vision, including image restoration, segmentation, opti...
Kevin Schelten, Stefan Roth