CFP last date
20 May 2024
Reseach Article

Feature Detection of an Object by Image Fusion

by Umesh C. Pati, Pranab K. Dutta, Alok Barua
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 17
Year of Publication: 2010
Authors: Umesh C. Pati, Pranab K. Dutta, Alok Barua
10.5120/362-549

Umesh C. Pati, Pranab K. Dutta, Alok Barua . Feature Detection of an Object by Image Fusion. International Journal of Computer Applications. 1, 17 ( February 2010), 50-57. DOI=10.5120/362-549

@article{ 10.5120/362-549,
author = { Umesh C. Pati, Pranab K. Dutta, Alok Barua },
title = { Feature Detection of an Object by Image Fusion },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 17 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 50-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number17/362-549/ },
doi = { 10.5120/362-549 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:57.139500+05:30
%A Umesh C. Pati
%A Pranab K. Dutta
%A Alok Barua
%T Feature Detection of an Object by Image Fusion
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 17
%P 50-57
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a novel method for feature detection of an object by fusion of range and intensity images. For this purpose, we have developed a data acquisition system with a laser source and camera interfaced with Silicon Graphics machine. 3-D mesh representation of the surface of the object is obtained from the acquired range images. Extraction of structural features from the range images has been performed by two methods i.e. coordinate thresholding technique and Laplacian of Gaussian (LoG) edge detector. Extraction of structural features from the intensity image of the object has been performed by the Hough transform technique and Canny edge detector. An approach using shape signatures has been proposed to detect corner points in the edge maps obtained using LoG detector as well as Canny detector. The extracted 3-D edge maps as well as the detected corner points have been mapped to 2-D plane. The methodology for manual fusion of edge maps with the help of affine transformation and the concept of automatic fusion of edge maps by affine transformation followed by iterative closest point (ICP) algorithm have been introduced in this work. The automated technique for fusion overcomes the drawbacks associated with manual fusion. The fusion algorithm provides a composite image with more accurate and reliable information about the important features of the object.

References
  1. Jiang, X., and Bunke, H. 1999. Edge detection in range images based on scan line approximation. Comput. Vis. Image Und. 73, 2 (Feb. 1999), 83-199.
  2. Baccar, M., Gee, L.A., Gonzalez, R.C., and Abidi, M.A. 1996. Segmentation of range images via data fusion and morphological watersheds. Pattern Recogn. 29, 10 (Oct. 1996), 1673-1687.
  3. Qi, Z., Weikang, G., and Xiuqing, Y. 1997. Real-time edge detection of obstacles in range image by automatically thresholding the normalized range difference. In Proceedings of the IEEE International Conference on Intelligent Processing Systems (October 1997), 2, 1047-1051.
  4. Yu, Y., Ferencz, A., and Malik, J. 2001. Extracting objects from range and radiance images. IEEE T. Vis.Comput. Gr. 7, 4 (Oct. 2001), 351- 364.
  5. Woo, H., Kang, E., Wang, S., and Lee, K.H. 2002. A new segmentation method for point cloud data. Int. J. Mach. Tool. Manu. 42, 2 (Jan. 2002), 167-178.
  6. Huang, J., and Menq, C.H. 2001. Automatic data segmentation for geometric feature extraction from unorganized 3-D coordinate points. IEEE T. Robotic. Autom. 17, 3 (June 2001), 268-279.
  7. Qian, R.J., and Huang, T.S. 1996. Optimal edge detection in two-dimensional images. IEEE T. Image Process. 5, 7 (July 1996), 1215-1220.
  8. Liang, L.R., and Looney, C.G. 2003. Competitive fuzzy edge detection. Appl. Soft Comput. 3, 2 (Sept. 2003), 123-137.
  9. Kim, D.S., Lee, W.H., and Kweon, I.S. 2004. Automatic edge detection using 3×3 ideal binary pixel patterns and fuzzy-based edge thresholding. Pattern Recogn. Lett. 25, 1 (Jan. 2004), 101-106.
  10. Rakesh, R.R., Chaudhuri, P., and Murthy, C.A. 2004. Thresholding in edge detection: a statistical approach. IEEE T. Image Process. 13, 7 (July 2004), 927-936.
  11. Kang, C.C., and Wang, W.J. 2007. A novel edge detection method based on the maximizing objective function. Pattern Recogn. 40, 2 (Feb. 2007), 609-618.
  12. Kor, S., and Tiwary, U. 2004. Feature level fusion of multimodal medical images in lifting wavelet transform domain. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (San Francisco, CA, USA, September 2004), 1, 1479-1482.
  13. Liao, Z. W., Hu, S.X., and Tang, Y.Y. 2005. Region-based multifocus image fusion based on Hough transform and wavelet domain hidden Markov models. In Proceedings of the 4th International Conference on Machine Learning and Cybernetics (Guangzhou, August 2005), 5490-5495.
  14. Cvejic, N., Lewis, J., Bull, D., and Canagarajah, N. 2006. Region-based multimodal image fusion using ICA bases. In Proceedings of the IEEE International Conference on Image Processing (October 2006), 1801-1804.
  15. Lisini, G., Tison, C., Tupin, F., and Gamba, P. 2006. Feature fusion to improve road network extraction in high-resolution SAR images. IEEE Geosci. Remote S. 3, 2 (Apr. 2006), 217-221.
  16. Tupin, F., and Roux, M. 2003. Detection of building outlines based on the fusion of SAR and optical features. ISPRS J. Photogramm. 58, 2 (June 2003), 71-82.
  17. Zhang, Z., Peng, X., Shi, W., and Hu, X. 2000. A survey of surface reconstruction from multiple range images. In Proceedings of the 2nd Asia-Pacific Conference on Systems Integrity and Maintenance (Nanjing, China, 2000), 519-523.
  18. Zhang, Z., Peng, X., and Zhang, D. 2004. Transformation image into graphics. In Integrated Image and Graphics Technologies, D. Zhang, M. Kamel and G. Baciu, Eds. Kluwer Academic Publishers, USA, 111-129.
  19. Besl, P. J., and McKay, N. D. 1992. A method for registration of 3-D shapes. IEEE T.Pattern Anal. 14, 2 (1992), 239-256.
  20. Planitz, B. M., Maeder, A. J., and Williams, J. A. 2005. The correspondence framework for 3-D surface matching algorithms. Comput. Vis. Image Und. 97, 3(2005), 347-383.
  21. Horn, B. K. P. 1987. Closed-form solution of absolute orientation using unit quaternions. J. Opt. Soc. Am. A. 4, 4 (1987), 629-642.
  22. Zhou, H., and Liu, Y. 2005. Incremental point-based integration of registered multiple range images. In Proceedings of the IEEE International Conference on Industrial Electronics, Control and Instrumentation (North Carolina, USA, 2005), 468-473.
  23. Gonzalez, R. C., and Woods, R.E. 2003 Digital Image Processing. Pearson Education.
  24. Gonzalez, R. C., Woods, R. E., and Eddins, S. L. 2004 Digital Image Processing using MATLAB. Pearson Education.
  25. Canny, J. F. 1986. A computational approach to edge detection. IEEE T. Pattern Anal. 8, 6(1986), 679-698.
  26. Gonzalez, R. C., and Woods, R. E. 1999. Digital Image Processing. Addison-Wesley Publishing Company.
  27. Madhavan, R. and Messina, E. 2003. Iterative registration of 3-D LADAR data for autonomous navigation. In Proceedings of the IEEE Intelligent Vehicles Symposium (Columbus, OH, 2003), 186-191.
Index Terms

Computer Science
Information Sciences

Keywords

Range image Intensity image Features Fusion Control point