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» Object Class Recognition Using SIFT and Bayesian Networks
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PAMI
2010
249views more  PAMI 2010»
13 years 6 months ago
Fast Keypoint Recognition Using Random Ferns
While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle pe...
Mustafa Özuysal, Michael Calonder, Vincent Le...
ICCV
2011
IEEE
12 years 7 months ago
Informative Feature Selection for Object Recognition via Sparse PCA
Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to r...
Nikhil Naikal, Allen Y. Yang, S. Shankar Sastry
IJCV
2008
266views more  IJCV 2008»
13 years 7 months ago
Learning to Recognize Objects with Little Supervision
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
Peter Carbonetto, Gyuri Dorkó, Cordelia Sch...
CVIU
2010
429views more  CVIU 2010»
13 years 7 months ago
Cascade of descriptors to detect and track objects across any network of cameras
Most multi-camera systems assume a well structured environment to detect and track objects across cameras. Cameras need to be fixed and calibrated, or only objects within a traini...
Alexandre Alahi, Pierre Vandergheynst, Michel Bier...
IJON
2002
120views more  IJON 2002»
13 years 7 months ago
The recognition and analysis of animate objects using neural networks and active contour models
: In this paper we describe a method for tracking walking humans in the visual field. Active contour models are used to track moving objects in a sequence of images. The resulting ...
Ken Tabb, Neil Davey, Rod Adams, Stella J. George