Publication detail
Implementation of the "Local Rank Differences" Image Feature Using SIMD Instructions of CPU
HEROUT, A. ZEMČÍK, P. JURÁNEK, R. HRADIŠ, M.
Original Title
Implementation of the "Local Rank Differences" Image Feature Using SIMD Instructions of CPU
English Title
Implementation of the "Local Rank Differences" Image Feature Using SIMD Instructions of CPU
Type
conference paper
Language
en
Original Abstract
Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend . The computatiponal performance of these classifiers largely depends on low level image features they are using: both from the point of view of the amount of information the feature provides and the executional time of its evaluation. Local Rank Difference is an image feature that is alternative to commonly used Haar features. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware as well as graphics hardware (GPU). Additionally, as shown in this paper, it performs very well on common CPU. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD using the multimedia instruction set of current general-purpose processors, presents its empirical performance measures compared to alternative approaches, and suggests several notes on practical usage of LRD and proposes directions for future work.
English abstract
Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend . The computatiponal performance of these classifiers largely depends on low level image features they are using: both from the point of view of the amount of information the feature provides and the executional time of its evaluation. Local Rank Difference is an image feature that is alternative to commonly used Haar features. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware as well as graphics hardware (GPU). Additionally, as shown in this paper, it performs very well on common CPU. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD using the multimedia instruction set of current general-purpose processors, presents its empirical performance measures compared to alternative approaches, and suggests several notes on practical usage of LRD and proposes directions for future work.
Keywords
Local Rank Differences, LRD, AdaBoost, Object Detection
RIV year
2008
Released
15.10.2008
Publisher
IEEE Computer Society
Location
Bhubaneswar
ISBN
978-0-7695-3476-3
Book
Proceedings of Sixth Indian Conference on Computer Vision, Graphics and Image Processing
Edition
NEUVEDEN
Edition number
NEUVEDEN
Pages from
1
Pages to
9
Pages count
9
URL
Documents
BibTex
@inproceedings{BUT32119,
author="Adam {Herout} and Pavel {Zemčík} and Roman {Juránek} and Michal {Hradiš}",
title="Implementation of the "Local Rank Differences" Image Feature Using SIMD Instructions of CPU",
annote="Usage of statistical classifiers, namely AdaBoost and its modifications, in
object detection and pattern recognition is a contemporary and popular trend .
The computatiponal performance of these classifiers largely depends on low level
image features they are using: both from the point of view of the amount of
information the feature provides and the executional time of its evaluation.
Local Rank Difference is an image feature that is alternative to commonly used
Haar features. It is suitable for implementation in programmable (FPGA) or
specialized (ASIC) hardware as well as graphics hardware (GPU). Additionally, as
shown in this paper, it performs very well on common CPU. The paper discusses the
LRD features and their properties, describes an experimental implementation of
LRD using the multimedia instruction set of current general-purpose processors,
presents its empirical performance measures compared to alternative approaches,
and suggests several notes on practical usage of LRD and proposes directions for
future work.",
address="IEEE Computer Society",
booktitle="Proceedings of Sixth Indian Conference on Computer Vision, Graphics and Image Processing",
chapter="32119",
edition="NEUVEDEN",
howpublished="print",
institution="IEEE Computer Society",
year="2008",
month="october",
pages="1--9",
publisher="IEEE Computer Society",
type="conference paper"
}