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» Object detection with vector quantized binary features
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CVPR
2009
IEEE
16 years 4 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...
CVPR
2010
IEEE
15 years 6 months ago
Learning Mid-Level Features For Recognition
Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of interme...
Y-Lan Boureau, Francis Bach, Yann LeCun, Jean Ponc...
MLDM
2005
Springer
15 years 3 months ago
Activity and Motion Detection Based on Measuring Texture Change
We estimate the speed of texture change by measuring the spread of texture vectors in their feature space. This method allows us to robustly detect even very slow moving objects. B...
Longin Jan Latecki, Roland Miezianko, Dragoljub Po...
IJON
2002
185views more  IJON 2002»
14 years 9 months ago
An image retrieval system by impression words and specific object names - IRIS
Image content analysis has become an important issue in multimedia processing. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-l...
Kazuhiro Kuroda, Masafumi Hagiwara
CVPR
2007
IEEE
15 years 11 months ago
A boosting regression approach to medical anatomy detection
The state-of-the-art object detection algorithm learns a binary classifier to differentiate the foreground object from the background. Since the detection algorithm exhaustively s...
Shaohua Kevin Zhou, Jinghao Zhou, Dorin Comaniciu