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» A Discriminative Framework for Modelling Object Classes
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ICDAR
2009
IEEE
14 years 3 months ago
Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models for Handwritten Digit Recognition
The problem of determining the appropriate number of components is important in finite mixture modeling for pattern classification. This paper considers the application of an unsu...
Xuefeng Chen, Xiabi Liu, Yunde Jia
CVPR
2008
IEEE
14 years 11 months ago
Discriminative learned dictionaries for local image analysis
Sparse signal models have been the focus of much recent research, leading to (or improving upon) state-of-the-art results in signal, image, and video restoration. This article ext...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
CVPR
2009
IEEE
15 years 4 months ago
A Multi-View Probabilistic Model for 3D Object Classes
We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints. Objects are represen...
Fei-Fei Li 0002, Hao Su, Min Sun, Silvio Savarese
3DOR
2010
13 years 4 months ago
Semantics-Driven Approach for Automatic Selection of Best Views of 3D Shapes
We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes ...
Hamid Laga
ECCV
2006
Springer
14 years 11 months ago
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection
This paper introduces the Located Hidden Random Field (LHRF), a conditional model for simultaneous part-based detection and segmentation of objects of a given class. Given a traini...
Ashish Kapoor, John M. Winn