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» Randomness and Geometric Features in Computer Vision
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ICPR
2006
IEEE
14 years 1 months ago
A Pixel-wise Object Tracking Algorithm with Target and Background Sample
In this paper, we present a clustering-based tracking algorithm for non-rigid object. Non-rigid object tracking is a challenging task because the target often appears as a concave...
Chunsheng Hua, Haiyuan Wu, Qian Chen, Toshikazu Wa...
CVPR
2010
IEEE
14 years 1 months ago
Face Recognition Based on Image Sets
We introduce a novel method for face recognition from image sets. In our setting each test and training example is a set of images of an individual’s face, not just a single ima...
Hakan Cevikalp, Bill Triggs
BMVC
2010
13 years 5 months ago
Deterministic Sample Consensus with Multiple Match Hypotheses
RANSAC (Random Sample Consensus) is a popular and effective technique for estimating model parameters in the presence of outliers. Efficient algorithms are necessary for both fram...
Paul McIlroy, Edward Rosten, Simon Taylor, Tom Dru...
ICCV
1999
IEEE
14 years 9 months ago
Dynamic Feature Ordering for Efficient Registration
Existing sequential feature-based registration algorithms involving search typically either select features randomly (eg. the RANSAC[8] approach) or assume a predefined, intuitive...
Tat-Jen Cham, James M. Rehg
CVBIA
2005
Springer
14 years 1 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a dis...
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi...