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» Learning Statistical Structure for Object Detection
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DAGM
2009
Springer
13 years 12 months ago
Active Structured Learning for High-Speed Object Detection
High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system th...
Christoph H. Lampert, Jan Peters
CVPR
2010
IEEE
13 years 11 months ago
Latent Hierarchical Structural Learning for Object Detection
We present a latent hierarchical structural learning method for object detection. An object is represented by a mixture of hierarchical tree models where the nodes represent objec...
Leo Zhu, Yuanhao Chen, Antonio Torralba, Alan Yuil...
ICIP
2007
IEEE
13 years 11 months ago
Propagating Image-Level Part Statistics to Enhance Object Detection
The bag-of-words approach has become increasingly attractive in the fields of object category recognition and scene classification, witnessed by some successful applications [5, 7...
Sheng Gao, Joo-Hwee Lim, Qibin Sun
CVPR
2005
IEEE
14 years 7 months ago
Part-Based Statistical Models for Object Classification and Detection
We propose using simple mixture models to define a set of mid-level binary local features based on binary oriented edge input. The features capture natural local structures in the...
Elliot Joel Bernstein, Yali Amit
CVPR
2007
IEEE
14 years 7 months ago
Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts
This paper proposes a novel approach to constructing a hierarchical representation of visual input that aims to enable recognition and detection of a large number of object catego...
Sanja Fidler, Ales Leonardis