Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33117
Image Segment Matching Using Affine- Invariant Regions
Authors: Ibrahim El rube'
Abstract:
In this paper, a method for matching image segments using triangle-based (geometrical) regions is proposed. Triangular regions are formed from triples of vertex points obtained from a keypoint detector (SIFT). However, triangle regions are subject to noise and distortion around the edges and vertices (especially acute angles). Therefore, these triangles are expanded into parallelogramshaped regions. The extracted image segments inherit an important triangle property; the invariance to affine distortion. Given two images, matching corresponding regions is conducted by computing the relative affine matrix, rectifying one of the regions w.r.t. the other one, then calculating the similarity between the reference and rectified region. The experimental tests show the efficiency and robustness of the proposed algorithm against geometrical distortion.Keywords: Image matching, key point detection, affine invariant, triangle-shaped segments.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082657
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1895References:
[1] N. S. Vassilieva ,"Content-based Image Retrieval Methods", Programming and Computer Software, 2009, Vol. 35, No. 3, pp. 158- 180. ┬® Pleiades Publishing, Ltd., 2009.Original Russian Text ┬® N.S. Vassilieva, 2009, published in Programmirovanie, 2009, Vol. 35, No. 3.
[2] Yang Gui, Xiaohu Zhang, and Yang Shang, SAR image segmentation using MSER and improved spectral clustering , EURASIP Journal on Advances in Signal Processing 2012, 2012:83.
[3] Jiguang Dai, Weidong Song and Jichao Zhang Remote Sensing Image Matching via Harris Detector and Wavelet Domain, 18th International Conference on Geoinformatics, 2010, pp. 1-4.
[4] M. Paradowski and A. 'Sluzek, "Detection of image fragments related by affine transforms: Matching triangles and ellipses," in Proc. International Conference on Information Science and Applications, vol. 1, 2010, pp.189-196
[5] Babbar, G., Punam Bajaj, AnuChawla, and Monika Gogna. 2010. A comparative study of image matching algorithms, International Journal of Information, Technology and Knowledge Management.July December. 2(2): 337-339.
[6] Schenk, T., A. Krupnik, and Y. Postolov. 2000. Comparative study of surface matching algorithms, International Archives of Photogrammetry and Remote Sensing Vol. XXXIII, part 4B, Amsterdam 2000. p. 518- 524.
[7] Lowe, D.G. 1999. Object recognition from local scale-invariant features, the proceedings of the seventh IEEE International Conference on Computer Vision 1999. 2: 1150-1157.
[8] Mikolajczyk, K, and. C. Schmid. 2004. Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1): 63-86.
[9] Lowe, D.G. 2004. Distintive image feature from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91-110.
[10] Ke, Y., and R. Sukthankar. 2004. PCA-SIFT: A more distinctive representation for local image descriptors. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 04) - 2: 506-513.
[11] Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. 2006. Speeded-up robust features (SURF). Computer Vision ECCV 2006, Vol. 3951. Lecture Notes in Computer Science. p. 404-417.
[12] Juan, L., and O. Gwun. 2009. A comparison of SIFT, PCA-SIFT, and SURF. International Journal of Image Processing (IJIP) 3(4): 143- 152.57
[13] Wassim Messaoudi, Imed Riadh Farah, Karim sahebettabâa, and Basel Solaiman, Semantic Strategic Satellite Image Retrieval, 3rd International Conference on Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. pp. 1-6
[14] S. M. Zakariya, Rashid Ali and Nesar Ahmad, Combining Visual Features of an Image at Different Precision Value of Unsupervised Content Based Image Retrieval, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2010, pp. 1-4.
[15] J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, 2009.
[16] Samer R. Saydam, Ibrahim El Rube, Amin A. Shoukry: Contourlet Based Interest Points Detector. ICTAI (2) 2008: 509-513.