Communications - Scientific Letters of the University of Zilina 2013, 15(11):185-190 | DOI: 10.26552/com.C.2013.2A.185-190

Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation

Patrik Kamencay1, Martina Zachariasova1, Robert Hudec1, Miroslav Benco1, Jan Hlubik1, Slavomir Matuska1
1 Department of Telecommunications, University of Zilina, Slovakia

In this paper, a stereo matching algorithm based on image segments and disparity measurement using stereo images is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the final disparity map by using a stereo pair of images. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the SIFTSAD matching method to determine the disparity estimate of each image pixel. This matching algorithm is proposed by combining Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). Finally, the comparisons between the three robust feature detection methods SIFT, Affine SIFT (ASIFT) and Speeded Up Robust Features (SURF) are presented. The obtained experimental results demonstrate that the proposed method has a positive effect on overall estimation of disparity map and outperforms other examined methods.

Keywords: belief propagation, mean shift, SIFT, ASIFT, SURF, disparity map

Published: July 31, 2013  Show citation

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Kamencay, P., Zachariasova, M., Hudec, R., Benco, M., Hlubik, J., & Matuska, S. (2013). Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation. Communications - Scientific Letters of the University of Zilina, 15(2A), 185-190. doi: 10.26552/com.C.2013.2A.185-190
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    References

    1. SUN, J., ZHANG, N. N., SHUM, H. Y.: Stereo Matching Using Belief Propagation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(7):787-800, 2003. Go to original source...
    2. YEDIDA, J. S., FREEMAN, W. T., WEISS, Y.: Understanding Belief Propagation and it is Generalizations, Exploring Artificial Intelligence in the New Millennium, Chap. 8, pp. 239-236, January 2003.
    3. DONG, L., OGUNBONA, P., LI, W., YU, G., FAN, L., ZHENG, G: A Fast Algorithm for Color Image Segmentation, First Intern. Conference on Innovative Computing, Information and Control, 2006.
    4. YIN, Z., COLLINS, R.: Belief Propagation in a 3D Spatio - Temporal MRF for Moving Object Detection, Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 2007. Go to original source...
    5. SCHARSTEIN, D., SZELISKI, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, Intern. J. of Computer Vision, 47(1), pp. 7-42, 2002. Go to original source...
    6. KANADE, T., OKUTOMI, M.: A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment, PAMI, 16(9), pp. 920-932, 1995. Go to original source...
    7. VEKSLER, O.: Stereo Matching by Compact Windows via Minimum Ratio Cycle, ICCV, 2002. Go to original source...
    8. HSIEH, Y. C., McKEOWN, D., PERLANT, F. P.: Performance evaluation of scene registration and stereo matching for cartographic feature extraction, IEEE TPAMI, 14(2), pp. 214-238, 1995. Go to original source...
    9. MULLIGAN, J., ISLER, V., DANIILIDIS, K.: Performance Evaluation of Stereo for Tele-Presence, In ICCV, vol. 2, pp. 558-565, 2002.
    10. SZELISKI, R., ZABIH, R.: An Experimental Comparison of Stereo Algorithms, In International Workshop on Vision Algorithms, Springer, pp. 1-19, 2000. Go to original source...
    11. KUHL, A.: A Comparison of Stereo Matching Algorithm for Mobile Robots, Centre for Intelligent Information Processing System, Western Australia, pp. 4-24, 2005.
    12. KAMENCAY, P., BREZNAN, M., JARINA, R., LUKAC, P., ZACHARIASOVA, M.: Improved Depth Map Estimation From Stereo Images Based On Hybrid Method, The Radioengineering J., vol. 21, No. 1, April 2012, ISSN 1210-2512.
    13. LOWE, D. G.: Distinctive Image Feature from Scale Invariant Key Points, Intern. J. of Computer Vision (IJCV), vol. 2, No. 60, 2004, pp. 91-110. Go to original source...
    14. JUAN, L., GWUN, O.: A Comparison of SIFT, PCA-SIFT and SURF, Intern. J. of Image Processing (IJIP), vol. 3, No. 4, pp. 143-152.
    15. KE, Y., SUKTHANKAR, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors, Proc. Conf. Computer Vision and Pattern Recognition, pp. 511-517, 2003.
    16. Middlebury Stereo image pairs dataset. [online 10.10.2012] Available on the internet. http://vision.middlebury.edu/stereo/data/.
    17. ZACHARIASOVA, M., HUDEC, R., BENCO, M., KAMENCAY, P., LUKAC, P., MATUSKA, S.: The Effect of Metric Space on The Results of Graph Based Colour Image Segmentation, Communications - Scientific Letters of the University of Zilina, vol. 14, No. 3, 2012, ISSN 1335-4205. Go to original source...
    18. BOLECEK, L., RICNY, V., SLANINA, M.: Fast Method for Reconstruction of 3D Coordinates, 35th Intern. Conference on Telecommunications and Signal Processing (TSP 2012), July 2012, ISBN 978-1-4673-1115-1. Go to original source...
    19. KOLMOGOROV, V., ZABIH, R.: Computing Visual Correspondence with Occlusions using Graph Cuts, Proc. IEEE Int. Conference Computer Vision 2, pp. 508-515, 2002.

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