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Reseach Article

Proposed Method for Detecting Objects

by Hany A. Elsalamony
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 2
Year of Publication: 2014
Authors: Hany A. Elsalamony
10.5120/18722-9946

Hany A. Elsalamony . Proposed Method for Detecting Objects. International Journal of Computer Applications. 107, 2 ( December 2014), 11-18. DOI=10.5120/18722-9946

@article{ 10.5120/18722-9946,
author = { Hany A. Elsalamony },
title = { Proposed Method for Detecting Objects },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 2 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number2/18722-9946/ },
doi = { 10.5120/18722-9946 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:59.162848+05:30
%A Hany A. Elsalamony
%T Proposed Method for Detecting Objects
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 2
%P 11-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most of object detection and classification algorithms are only locating regions in the image, whether it is within a template-sliding mask or interested region blobs. However, such regions may be ambiguous, especially when the object of interest is very small, unclear, or anything else. This paper presents proposed algorithm for automatic object detection and matching based on its own proposed signature using morphological segmentation tools. Moreover, the algorithm tries to match the objects; neither among object's blobs nor among regions of interest; but among the constructed proposed objects' signatures. During the matching process, SURF method has presented to make a comparison of the experimental results. The performance has been tested 120 from a wide variety of unlike objects; it has been achieved 100% in the case of constructing object signatures, also it has been achieved 96% of right matching whereas SURF has achieved 85% for all test objects.

References
  1. Adel. A. Darwish. , Hesham F. Ali. , Hany A. M. El-Salamony. ''3D Human Body Motion Detection and Tracking in Video''. The 14th IASTED International Conference on Applied Simulation and Modeling, Spain, June 15-17, 2005.
  2. C. Papageorgiou and T. Poggio. "A trainable system for object detection". Intl. J Computer Vision, 38 (1): 15–33, 2000.
  3. C. Zahn and R. Roskies. "Fourier descriptors for plane closed curves". IEEE Trans. Computers, 21 (3): 269–281, March 1972.
  4. D. Gavrila and V. Philemon. "Real-time object detection for smart vehicles". In Proc. 7th Int. Conf. Computer Vision, pages 87–93, 1999.
  5. D. Huttenlocher, R. Lilien, and C. Olson. "View-based recognition using an eigenspace approximation to the Hausdorff measure. " PAMI, 21 (9): 951–955, Sept. 1999.
  6. Elsalamony, H. A. , "Automatic video stream indexing and retrieving based on face detection using wavelet transformation," Signal Processing Systems (ICSPS), 2010 2nd International Conference on, volume. 1, pp: 153-157, 5-7 July 2010.
  7. Elsalamony, H. A. , "Automatic object detection and matching based on proposed signature," Audio, Language and Image Processing (ICALIP), 2012 International Conference on, volume l, pp: 68-73, 16-18 July 2012.
  8. G. Bouchard and B. Triggs. "A hierarchical part-based model for visual object categorization. " In CVPR, 2005.
  9. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," International Journal of Computer Vision and Image Understanding vol. 110, June 2008.
  10. H. Bay, T. Tuytelaars, and L. VanGool, "Surf: Speeded up robust features," presented at the Proceedings of European Conference on Computer Vision, Austria, 2006.
  11. Hany A. Elsalamony. "Object Detection and Matching using Proposed Signature and SURF. " 18th International Conference on Image Processing, Computer Vision, and Pattern Recognition. IPCV'14, Worldcomp'14 Proceeding, pp: 377-383, Las Vegas, Nevada, USA, July 21-24, 2014.
  12. Hany A. Elsalamony, "Detection and matching of object using proposed signature. " Emerging Trends in Image Processing, Computer Vision and Pattern Recognition. Elsevier Book Chapter: 38, 2014.
  13. Henry A. Rowley. , Shumeet Baluja. , and Takeo Kanade. "Human face detection in visual scenes". Advances in Neural Info. Proc. Systems, volume 8, 1995.
  14. Imtiaz Ali, Julien Mille, Laure Tougne, "Space–time spectral model for object detection in dynamic textured background". Pattern Recognition Letters, Volume 33, Issue 13, 1 October 2012, Pages 1710-1716, ISSN 0167-8655.
  15. Kevin Lai, Liefeng Bo, Xiaofeng Ren, and Dieter Fox. "Detection-based Object Labeling in 3D Scenes. " International Conference on Robotics and Automation, 2012.
  16. Li He; Hui Wang; Hong Zhang. "Object detection by parts using appearance, structural and shape features". International Conference on Mechatronics and Automation (ICMA), page(s): 489 – 494, 2011.
  17. Lowe, David G, "Distinctive Image Features from Scale-Invaria (t Key points," International Journal of Computer Vision vol. 60, January 2004.
  18. Lowe, David G, "Object recognition from local scale-invariant features," presented at the Proceedings of the International Conference on Computer Vision, Greece, 1999.
  19. Luo Juan, Oubong Gwun, "A Comparison of SIFT, PCA-SIFT and SURF," International Journal of Image Processing vol. 3, June 2009.
  20. M. Fischler and R. Elschlager. "The representation and matching of pictorial structures". IEEE Trans. Computers, C-22 (1): 67–92, 1973.
  21. Mustafa Teke; M. Firat Vural; Alptekin Temizel; Yasemin Yard?mc?. "High-resolution multispectral satellite image matching using scale invariant feature transform and speeded up robust features. " J. Appl. Remote Sens. 5(1), 053553 September 23, 2011.
  22. P. Viola, M. Jones, and D. Snow. "Detecting pedestrians using patterns of motion and appearance. " In IEEE Conference on Computer Vision and Pattern Recognition, 2003.
  23. Perhaad Mistry, Chris Gregg, Norman Rubin, David Kaeli, and Kim Hazelwood. "Analyzing program flow within a many-kernel OpenCL application. " In Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units (GPGPU-4). ACM, New York, NY, USA, Article 10, 8 pages. 2011.
  24. R. C. Veltkamp and M. Hagedoorn. "State of the art in shape matching". Technical Report UU-CS-1999-27, Utrecht, 1999.
  25. Y. Amit, D. Geman, and K. Wilder. "Joint induction of shape features and tree classifiers". IEEE Trans. PAMI, 19 (11): 1300–1305, November 1997.
  26. Yan Ke, Rahul Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors," presented at the 2004 Proc. IEEE Conference on Computer Vision and Pattern Recognition, USA, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Object Detection and Matching Signature SURF Segmentation.