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» Contour-Based Learning for Object Detection
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CVPR
2010
IEEE
14 years 3 months ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a...
Paul Schnitzspan, Stefan Roth, Bernt Schiele
CVPR
2009
IEEE
1119views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation
In this paper, a novel feature named Adaptive Contour Feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in...
Wei Gao (Tsinghua University), Haizhou Ai (Tsinghu...
PAMI
2000
113views more  PAMI 2000»
13 years 7 months ago
Hierarchical Discriminant Regression
This paper presents a new technique which incrementally builds a hierarchical discriminant regression (IHDR) tree for generation of motion based robot reactions. The robot learned...
Wey-Shiuan Hwang, Juyang Weng
CVPR
2008
IEEE
14 years 9 months ago
A hierarchical and contextual model for aerial image understanding
In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene an...
Jake Porway, Kristy Wang, Benjamin Yao, Song Chun ...
CVPR
2003
IEEE
14 years 9 months ago
Representation and Detection of Deformable Shapes
We describe some techniques that can be used to represent and detect deformable shapes in images. The main difficulty with deformable template models is the very large or infinite...
Pedro F. Felzenszwalb