CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Fuzzy Logic Approach for Stereo Matching Suited for Real-Time Processing

by M. Perez-patricio, A. Aguilar-gonzalez, J.l. Camas-anzueto, M. Arias-estrada
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 2
Year of Publication: 2015
Authors: M. Perez-patricio, A. Aguilar-gonzalez, J.l. Camas-anzueto, M. Arias-estrada
10.5120/19795-1572

M. Perez-patricio, A. Aguilar-gonzalez, J.l. Camas-anzueto, M. Arias-estrada . A Fuzzy Logic Approach for Stereo Matching Suited for Real-Time Processing. International Journal of Computer Applications. 113, 2 ( March 2015), 1-8. DOI=10.5120/19795-1572

@article{ 10.5120/19795-1572,
author = { M. Perez-patricio, A. Aguilar-gonzalez, J.l. Camas-anzueto, M. Arias-estrada },
title = { A Fuzzy Logic Approach for Stereo Matching Suited for Real-Time Processing },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 2 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number2/19795-1572/ },
doi = { 10.5120/19795-1572 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:53.008491+05:30
%A M. Perez-patricio
%A A. Aguilar-gonzalez
%A J.l. Camas-anzueto
%A M. Arias-estrada
%T A Fuzzy Logic Approach for Stereo Matching Suited for Real-Time Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 2
%P 1-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a novel method that uses both area and feature based information as similarity measures for stereo matching is proposed. Area-based information is suited for non-homogeneous regions while feature information helps in homogeneous areas. In order to define a conjugate pair, a fuzzy logic approach that combines the similarity information is used. The proposed method preserves discontinuities while reducing matching errors in homogeneous regions. This proposal is suited for real-time processing using dedicated hardware. We demonstrate and discuss performance using synthetic stereo pairs.

References
  1. T. Kanade and M. Okutomi (1991) A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment. Proceedings of the 1991 IEEE International Conference on Robotics and Automation. Sacramento, CA, USA
  2. S. Scherer andW. Andexer and A. Pinz (1998) Robust adaptive window matching by homogeneity constraint and integration of descriptions. Proceedings of the 14th International Conference on Pattern Recognition. Brisbane, Australia
  3. J. Lotti and G. Giraudon (1994) Correlation algorithm with adaptive window for aerial image in stereo vision. Proceedings of the Image and Signal Processing for Remote Sensing. Rome, Italy
  4. S. Intille and A. Bobick (1994) Disparity-Space Images and Large Occlusion Stereo. Proceedings of the European Conference on Computer Vision
  5. H. Hirschmuller (2001) Improvements in real-time correlationbased stereo vision. Proceedings of IEEE workshop on Stereo and Multi-Baseline Vision. Kauai, Hawaii
  6. N. Chang and L. Ting and T. Tsung and T. Yu (2007) Real- Time DSP Implementation on Local Stereo Matching. Proceedings of the International Conference on Multimedia and Expo. Hefei, China
  7. O. Veksler (2002) Stereo Matching by Compact Windows via Minimum Ratio Cycle. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol:24 1654–1660
  8. Q. Yang (2014) Stereo Matching using tree filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI 10. 1109/TPAMI. 2014. 2353642
  9. D. Min and J. Lu and M. Do (2013) Joint Histogram-Based Cost Aggregation for Stereo Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol: 35 2539-2545
  10. G. Chatterji (2004) Fuzzy Compactness Based AdaptiveWindow Approach for Image Matching in Stereo Vision. Lecture Notes in Computer Sciences. Vol: 3316 935-940
  11. C. Zitcnick and T. Kanade (2000) A cooperative algorithm for stereo matching and occlusion detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol: 22 1-10
  12. A. Fusiello, V. Roberto and E. Trucco (2000) Symmetric stereo with multiple windowing. International Journal of Pattern Recognition and Artificial Intelligence. Vol: 8 1053-1066
  13. L. Wang and R. Yang and M. Gong and M. Liao (2014) Realtime stereo using approximated joint bilateral filtering and dynamic programming. Journal of Real-Time Image Processing. Vol: 9 447-461
  14. M. Humenberger and C. Zinner and M. Weber and W. Kubinger and M. Vincze (2010) A fast stero matching algorithm suitable for embedded real-time systems. Computer vision and Image Understanding. Vol: 114 1180-1202
  15. F. Hosseini and A. Fijany and S. Safari and J. Fontaine (2013) Fast implementation of dense stereo vision algorithms on a highly parallel SIMD architecture. Journal of Real-Time Image Processing. Vol: 8 421-435
  16. R. Yanga and M. Pollefeys (2005) A versatile stereo implementation on commodity graphics hardware. Real-Time Image. Vol: 11 7-18
  17. K. Ambrosch and W. Kubinger (2010) Accurate hardwarebased stereo vision. Computer vision and Image Understanding. Vol: 114 1303-1316
  18. X. Sun and X. Mei and S. Jiao and M. Zhou and Z. Liu and H. Wang (2014) Real-time local stereo via edge-aware disparity propagation. Pattern Recognition Letters. Vol: 49 201-206
  19. M. Jin and T. Maruyama (2014) Fast and Accurate Stereo Vision System on FPGA. ACM Transactions on Reconfigurable Technology and Systems. Vol: 7 1-24
  20. J. Ding and J. Liu and W. Zhou and H. Yu and Y. Wang and X. Gong (2011) Real-time stereo vision system using adaptive weight cost aggregation approach. EURASIP Journal on Image and Video Processing. Vol: 20 1-19
  21. C. Ttofis and S. Hadjitheophanous and A. Georghiades and T. Theocharides (2013) Edge-Directed Hardware Architecture for Real-Time Disparity Map Computation. IEEE Transactions on Computers. Vol: 62 690-704
  22. X. Zhang and Z. Chen (2013) SAD-Based Stereo Vision Machine on a System-on-Programmable-Chip (SoPC). Sensors. Vol: 13 3014-3027
  23. S. Jin and J. Cho and X. D. Pham and K. M. Lee and S. -K. Park and M. Kim and J. W. Jeon (2010) FPGA Design and Implementation of a Real-Time Stereo Vision System. IEEE Transactions on Systems and Circuits for Video Technology. Vol: 20 15-26
Index Terms

Computer Science
Information Sciences

Keywords

fuzzy logic dense stereo vision real-time processing dedicated hardware feature-based

Powered by PhDFocusTM