GP-GPU Implementation of the "Local Rank Differences" Image Feature

Authors:
Adam Herout;Radovan Josth;Pavel Zemcik;Michal Hradis
Affiliations:
Graph@FIT, Brno University of Technology, Brno, CZ;Graph@FIT, Brno University of Technology, Brno, CZ;Graph@FIT, Brno University of Technology, Brno, CZ;Graph@FIT, Brno University of Technology, Brno, CZ
Venue:
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Year:
2008

Citing 5
Cited 2

Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
WaldBoost " Learning for Time Constrained Sequential Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
"Local Rank Differences" Image Feature Implemented on GPU

ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Learning a fast emulator of a binary decision process

ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II

Real-time object detection on CUDA

Journal of Real-Time Image Processing
Fast and energy efficient AdaBoost classifier

Proceedings of the 10th FPGAworld Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 Differences 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 alter native approaches, suggests several notes on practical usage of LRD and proposes directions for future work.