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» Learning to localize detected objects
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ICIP
2006
IEEE
16 years 6 months ago
Local Discriminant Embedding with Tensor Representation
We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Jian Xia, Dit-Yan Yeung, Guang Dai
ACCV
2006
Springer
15 years 10 months ago
Minimal Weighted Local Variance as Edge Detector for Active Contour Models
Performing segmentation of narrow, elongated structures with low contrast boundaries is a challenging problem. Boundaries of these structures are difficult to be located when noise...
Max W. K. Law, Albert C. S. Chung
ICASSP
2010
IEEE
15 years 4 months ago
No-reference image sharpness assessment based on local phase coherence measurement
Sharpness is one of the most determining factors in the perceptual assessment of image quality. Objective image sharpness measures may play important roles in the design and optim...
Rania Hassen, Zhou Wang, Magdy Salama
AROBOTS
2008
104views more  AROBOTS 2008»
15 years 4 months ago
Modeling dynamic scenarios for local sensor-based motion planning
This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information with a sensor-based motion planning system. The contribution in th...
Luis Montesano, Javier Minguez, Luis Montano
ICCV
2005
IEEE
16 years 6 months ago
Efficient Learning of Relational Object Class Models
We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts' appearance...
Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall