Publication Details
GP-GPU Implementation of the "Local Rank Differences" Image Feature
Jošth Radovan, Ing., Ph.D.
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
pattern recognition, adaptive boosting, AdaBoost, WaldBoost, image features, LRD, Local Rank Differences, hardware acceleration
A currently popular trend in object detection and pattern recognition is usage of statistical classifiers, namely AdaBoost and its modifications. The speed performance of these classifiers largely depends on the low level image features they are using: both on the amount of information the feature provides and the processor time of its evaluation. Local Rank Dierences is an image feature that is alternative to commonly used haar wavelets. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware, but - as this paper shows - it performs very well on graphics hardware (GPU) used in general purpose manner (GPGPU, namely CUDA in this case) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of the LRD in graphics hardware using CUDA, presents its empirical performance measures compared to alternative approaches, suggests several notes on practical usage of LRD and proposes directions for future work.
@inproceedings{BUT29556,
author="Adam {Herout} and Radovan {Jošth} and Pavel {Zemčík} and Michal {Hradiš}",
title="GP-GPU Implementation of the {"}Local Rank Differences{"} Image Feature",
booktitle="Proceedings of International Conference on Computer Vision and Graphics 2008",
year="2008",
series="Lecture Notes in Computer Science",
pages="1--11",
publisher="Springer Verlag",
address="Heidelberg",
isbn="978-3-642-02344-6",
url="http://www.fit.vutbr.cz/research/groups/graph/publi/2008/2008-Herout-ICCVG-LRDonCUDA.pdf"
}