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» Learning Hierarchical Shape Models from Examples
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CVPR
2009
IEEE
15 years 3 months ago
Learning Rotational Features for Filament Detection
State-of-the-art approaches for detecting filament-like structures in noisy images rely on filters optimized for signals of a particular shape, such as an ideal edge or ridge. W...
François Fleuret, Germán Gonzá...
ECCV
2008
Springer
14 years 9 months ago
3D Face Recognition by Local Shape Difference Boosting
Abstract. A new approach, called Collective Shape Difference Classifier (CSDC), is proposed to improve the accuracy and computational efficiency of 3D face recognition. The CSDC le...
Yueming Wang, Xiaoou Tang, Jianzhuang Liu, Gang Pa...
CVPR
2005
IEEE
14 years 10 months ago
Estimating 3D Shape and Texture Using Pixel Intensity, Edges, Specular Highlights, Texture Constraints and a Prior
We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering th...
Sami Romdhani, Thomas Vetter
IVC
2002
148views more  IVC 2002»
13 years 7 months ago
Detecting lameness using 'Re-sampling Condensation' and 'multi-stream cyclic hidden Markov models'
A system for the tracking and classification of livestock movements is presented. The combined `tracker-classifier' scheme is based on a variant of Isard and Blakes `Condensa...
Derek R. Magee, Roger D. Boyle
EH
1999
IEEE
351views Hardware» more  EH 1999»
14 years 5 days ago
Evolvable Hardware or Learning Hardware? Induction of State Machines from Temporal Logic Constraints
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...