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» Learning Statistical Structure for Object Detection
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DAGM
2009
Springer
15 years 4 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
15 years 3 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
15 years 3 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
15 years 11 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
15 years 11 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