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Multithreading AdaBoost framework for object recognition
2015
2015 IEEE International Conference on Image Processing (ICIP)
Our research focuses on the study of effective feature description and robust classifier technique, proposing a novel learning framework, which is capable of processing multiclass objects recognition simultaneously and accurately. The framework adopts rotation-invariant histograms of oriented gradients (Ri-HOG) as feature descriptors. Most of the existing HOG techniques are computed on a dense grid of uniformlyspaced cells and use overlapping local contrast of rectangular blocks for
doi:10.1109/icip.2015.7350997
dblp:conf/icip/ChenTA15
fatcat:hq4z5fjpmngvvorkgapwqjyqgq