Article

OpenVIDIA: parallel GPU computer vision

Authors:

James Fung,

Steve MannAuthors Info & Claims

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

Pages 849 - 852

https://doi.org/10.1145/1101149.1101334

Published: 06 November 2005 Publication History

Get Access

Abstract

Graphics and vision are approximate inverses of each other: ordinarily Graphics Processing Units (GPUs) are used to convert "numbers into pictures" (i.e. computer graphics). In this paper, we propose using GPUs in approximately the reverse way: to assist in "converting pictures into numbers" (i.e. computer vision). The OpenVIDIA project uses single or multiple graphics cards to accelerate image analysis and computer vision. It is a library and API aimed at providing a graphics hardware accelerated processing framework for image processing and computer vision. OpenVIDIA explores the creation of a parallel computer architecture consisting of multiple Graphics Processing Units (GPUs) built entirely from commodity hardware. OpenVIDIA uses multiple Graphic.Processing Units in parallel to operate as a general-purpose parallel computer architecture. It provides a simple API which implements some common computer vision algorithms. Many components can be used immediately and because the project is Open Source, the code is intended to serve as templates and examples for how similar algorithms are mapped onto graphics hardware. Implemented are image processing techniques (Canny edge detection, filtering), image feature handling (identifying and matching features) and image registration, to name a few.

References

[1]

J. Cui, W. Wong, and S. Mann. Time-frequency analysis of visual evoked potentials using chirplet transform. IEEE Electronics Letters, 41(4):217--218, 2005.

Crossref

Google Scholar

[2]

J. Fung. Chapter 40: Computer vision on the gpu. In GPU Gems 2: Programming Techniques for High-Performance Graphics and General Purpose Computation, pages 649--665. Addison-Wesley, 2005.

Google Scholar

[3]

J. Fung and S. Mann. Computer vision signal processing on graphics processing units. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), pages 83--89, Montreal, Quebec, Canada, May 17-21 2004.

Crossref

Google Scholar

[4]

E. Lindholm, M. J. Kilgard, and H. Moreton. A user programmable vertex engine. In Computer Graphics, Proc. of SIGGRAPH 2001, pages 149--158, 2001.

Digital Library

Google Scholar

[5]

S. Mann and S. Haykin. The chirplet transform: A generalization of Gabor's logon transform. Vision Interface '91, pages 205--212, June 3-7 1991. ISSN 0843-803X.

Google Scholar

[6]

W. Mark, R. Glanville, K. Akeley, and M. Kilgard. Cg: A system for programming graphics hardware in a c-like language. In Proceedings of ACM SIGGRAPH. ACM Press, 2003, volume 22, July 2003.

Digital Library

Google Scholar

[7]

J. D. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krger, A. E. Lefohn, and T. J. Purcell. A survey of general-purpose computation on graphics hardware. In Eurographics 2005, State of the Art Reports, pages 21--51, Aug. 2005.

Google Scholar

Cited By

View all

Wang LJia HZhang YLi KWei C(2022)EgpuIP: An Embedded GPU Accelerated Library for Image Processing2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00147(914-921)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00147
Seelaboyina RVishwkarma R(2022)Feature Extraction for Image Processing and Computer Vision—A Comparative ApproachProceedings of the International Conference on Cognitive and Intelligent Computing10.1007/978-981-19-2350-0_20(205-210)Online publication date: 1-Nov-2022
https://doi.org/10.1007/978-981-19-2350-0_20
Venkatesan NShin DNam C(2021)Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU FrameworksApplied Sciences10.3390/app1106283811:6(2838)Online publication date: 22-Mar-2021
https://doi.org/10.3390/app11062838
Show More Cited By

Index Terms

OpenVIDIA: parallel GPU computer vision
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

Recommendations

Computer vision algorithms acceleration using graphic processors NVIDIA CUDA
Abstract
Using graphic processing units (GPUs) in parallel with central processing unit in order to accelerate algorithms and applications demanding extensive computational resources has been a new trend used for the last few years. In this paper, we ...
Andy Witkin: From Computer Vision to Computer Graphics

The article is a retrospective on Andy Witkin, who helped merge the fields of computer vision and computer graphics through his work at Schlumberger, Carnegie Mellon University, and Pixar.
Three-dimensional representations for computer graphics and computer vision

Representing complex three-dimensional objects in a computer involves more than just evaluating its display capabilities. Other factors are the uses and costs of the representation, what operations can be performed on it and, ultimately, how useful it ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

November 2005

1110 pages

ISBN:1595930442

DOI:10.1145/1101149

General Chairs:
Hongjiang Zhang
Microsoft Research Asia, China
,
Tat-Seng Chua
National University of Singapore, Singapore
,
Program Chairs:
Ralf Steinmetz
Technische Universitat Darmstadt, Germany
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Lynn Wilcox
FXPAL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

MM05

Sponsor:

MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

Acceptance Rates

MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

107
Total Citations
View Citations
1,940
Total Downloads

Downloads (Last 12 months)57
Downloads (Last 6 weeks)3

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wang LJia HZhang YLi KWei C(2022)EgpuIP: An Embedded GPU Accelerated Library for Image Processing2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00147(914-921)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00147
Seelaboyina RVishwkarma R(2022)Feature Extraction for Image Processing and Computer Vision—A Comparative ApproachProceedings of the International Conference on Cognitive and Intelligent Computing10.1007/978-981-19-2350-0_20(205-210)Online publication date: 1-Nov-2022
https://doi.org/10.1007/978-981-19-2350-0_20
Venkatesan NShin DNam C(2021)Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU FrameworksApplied Sciences10.3390/app1106283811:6(2838)Online publication date: 22-Mar-2021
https://doi.org/10.3390/app11062838
Dantas DDanilo Passos Leal HSousa D(2021)Fast 2D and 3D image processing with OpenCL2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7351730(4858-4862)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/ICIP.2015.7351730
Moolchandani DGupta SKumar ASarangi S(2020)Performance Prediction for Multi-Application Concurrency on GPUs2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS48437.2020.00050(306-315)Online publication date: Aug-2020
https://doi.org/10.1109/ISPASS48437.2020.00050
(2020)ReferencesOpenVX Programming Guide10.1016/B978-0-12-816425-9.00024-3(343-346)Online publication date: 2020
https://doi.org/10.1016/B978-0-12-816425-9.00024-3
Brill FErukhimov VGiduthuri RRamm S(2020)IntroductionOpenVX Programming Guide10.1016/B978-0-12-816425-9.00007-3(1-13)Online publication date: 2020
https://doi.org/10.1016/B978-0-12-816425-9.00007-3
Bettaieb AFilali NFilali TBen Aissia H(2019)GPU acceleration of edge detection algorithm based on local variance and integral image: application to air bubbles boundaries extractionComputer Optics10.18287/2412-6179-2019-43-3-446-45443:3Online publication date: Jun-2019
https://doi.org/10.18287/2412-6179-2019-43-3-446-454
Scharf DLe Viet BLe TRechenberg JTschierschke SVogl EVandone AGiardini M(2019)Hardware Accelerated Image Processing on an FPGA-SoC Based Vision System for Closed Loop Monitoring and Additive Manufacturing Process ControlComputer Vision Systems10.1007/978-3-030-34995-0_1(3-12)Online publication date: 23-Nov-2019
https://doi.org/10.1007/978-3-030-34995-0_1
Emrani ZBateni SRabbani H(2017)A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection AlgorithmsJournal of Medical Signals & Sensors10.4103/2228-7477.1991557:1(33)Online publication date: 2017
https://doi.org/10.4103/2228-7477.199155
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Computer vision algorithms acceleration using graphic processors NVIDIA CUDA

Andy Witkin: From Computer Vision to Computer Graphics

Three-dimensional representations for computer graphics and computer vision