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  Enhancement in Image Mosaicing using Voronoi and Surf Algorithm  
  Authors : Arya Vijayakumaran Nair; Dr. D. Loganathan; Asha. S
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Image mosaicking or image stitching is a process of combining two or more images to create a large panoramic image. The resultant image can also be used for texture mapping of a 3D environment. Consider the case of images taken from a normal camera. We can convert those images in to mosaicked image using mosaicking technique. This paper presents a new method of image mosaicking based on SURF and VORONOI. SURF algorithm is used to extract features from the input images. Then the input images are subdivided in to regions based on VORONOI method. In order to match the regions from VORONOI, ZNCC (Zero Mean Normalized Cross Correlation) method is used and geometric transformation of input images are calculated based on that value the two images are merged together to form mosaicked image. Finally to create a natural-looking mosaics, a quasi homography warp is applied, which balances the perspective distortion against projective distortion in non-overlapping region. This method helps to reduce projection time and execution time. It is faster than traditional mosaicking algorithms.


Published In : IJCSN Journal Volume 6, Issue 3

Date of Publication : June 2017

Pages : 313-319

Figures :12

Tables : 01


Arya Vijayakumaran Nair : received the B. Tech degree in Information Technology from M. G University, Kerala, India, in 2012, and currently doing M. Tech in Cyber Security in MET’S School of Engineering, Mala – APJ Abdul Kalam Technological University, Kerala, India.

Dr. D. Loganathan : is a Professor and Head of Computer Science and Engineering department in MET’S School of Engineering, Mala, Trissur, Kerala. After his B.E., and M.E degree, he accomplished a doctoral degree from Anna University, Chennai, India. He has more than 20 years of teaching experience and having 8 years of research experience in engineering field. His research interest includes Wireless Communication, Wireless Ad hoc Networks and Image Processing. He has published several research papers in various international journals.

Asha. S : is an Assistant professor in MET’S School of Engineering Mala, Thrissur, Kerala. Received the Btech degree in Govt. Engineering College Idukki, Kerala and M.Tech from Anna University. She has more than 4 years of teaching experience. Subject of interest are programming languages such as C, C++, Theory of computation, Compiler design.


Image mosaicking, feature extraction, direct methods, SURF, VORONOI, ZNCC

An improved method of image mosaic based on VORONOI regions and SURF algorithm is proposed here. In this method the control pints from two input images are extracted using SURF algorithm. VORONOI method is used to divide the images in different regions based on the control points. Then using ZNCC correlation VORONOI region matching is performed. Finally geometric transformation is calculated between the two images, for this the regions with highest correlation scores is selected and the input images are overlapped to get the desired mosaicked. To create a natural-looking mosaics, a quasi homography warp is applied, which balances the perspective distortion against projective distortion in nonoverlapping region


[1] C.-C. Lin, S. U. Pankanti, K. N. Ramamurthy, and A. Y. Aravkin, “Adaptive as-natural-as-possible image stitching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2015, pp. 1155–1163. [2] C.-H. Chang, Y. Sato, and Y.-Y. Chuang, “Shapepreserving halfprojective warps for image stitching,” in Proc. IEEE Conf. Comput. Vis.Pattern Recog., May 2014, pp. 3254–3261. [3] D. G Lowe, "Object recognition from local scaleinvariant features?, International Conference on Computer Vision, vol. 2, pp.1150– 1157,1999 J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73. [4] D. G. Lowe, ?Distinctive Image Features from Scale- Invariant Keypoints?, International Journal of Computer Vision, vol. 60, pp. 91- 110, 2004. M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989. [5] D. G. Lowe, ?Local feature view clustering for 3D object recognition?, IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, pp.682-688, 2001. K. Elissa, “Title of paper if known,” unpublished. [6] D. T. Lee and A. K. Lin. Generalized Delaunay triangulation for planar graphs. Discrete Comput. Geom., 1:201–217, 1986. [7] D. T. Lee. On k-nearest neighbor Voronoi diagrams in the plane. IEEE Trans. Comput., C-31:478–487, 1982. [8] Du, Qiang, Vance Faber, and Max Gunzburger. “Centroidal Voronoi Tessellations: Applications and Algorithms.” SIAM Review 21 (1999): 637-676. [9] F. Aurenhammer and H. Edelsbrunner, “An Optimal Algorithm for Constructing the Weighted Voronoi Diagram in the Plane.” Pattern Recognition 17, 2 (1984): 251-257. [10] Fang X, Zhu J, Luo B (2012) Image mosaic with relaxed motion. SIViP 6(4):647–667. doi:10.1007/s11760-010- 0194-4 [11] Fortune, Steven. “A Sweepline Algorithm for Voronoi Diagrams.” Proceedings of the Second Annual ACM Symposium on Computational Geometry Yorktown Heights, New York: Association for Computing Machinery, 1986: 313-322. [12] H. Bay, et al., ?Speeded up Robust Features?, Proc. of the 9th European Conf. on Computer Vision, Cambridge, U.K., pp. 404-417, 2006 [13] J. Beis, and D. G. Lowe, ?Shape indexing using approximate nearest-neighbour search in highdimensional spaces?, Conference on Computer Vision and Pattern Recognition, Puerto Rico, pp. 1000–1006, 1997.. [14] Ju, Lili, Qiang Du, and Max Gunzburger. “Probabilistic Methods for Centroidal Voronoi Tessellations and Their Parallel Implementations.” Parallel Computer 28 (2002):1477-1500. [15] M. Laraqui, A. Saaidi , A. Mouhib ,M Abarkan(2015) Images matching using voronoï regions propagation. 3D Res 6(3):1–16. doi:10.1007/s13319-015-0056-5 [16] M. Lhuillier , L. Quan (2002) Quasi-dense reconstruction from image sequence. In Computer Vision—ECCV 2002, 125–139. Springer Berlin Heidelberg. doi:10.1007/3-540-47967-8_9 [17] N. I. S. with the Global Similarity Prior, “Yu-sheng chen and yung-yuchuang,” in Proc. Eur. Conf. Comput. Vis., Oct. 2016, pp. 186–201. [18] N.-M. Lˆe. Randomized incremental construction of simple abstract Voronoi diagrams in 3-space. In Proc. 10th Internat. Conf. Fund. Comput. Theory, volume 965 of Lecture Notes Comput. Sci., pages 333–342. Springer- Verlag, 1995. [19] Okabe, Atsuyuki, Barry Boots, Kokichi Sugihara, and Sung Nok Chiu. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, 2nd Ed.. Chichester, West Sussex, England: John Wiley & Sons, 1999. [20] P J Anju , Dr.D.Loganathan, (2016) Image Fusion using NSCT Theory and Wavelet Transform for Medical Diagnosis, International Journal of Computer Science and Information Technologies, Vol. 7 (3) , 1507-1510 [21] S. Peleg and J. Herman, ?Panoramic mosaics by manifold projection? Proc. of IEEE Computer Society conference on Computer Vision and Pattern Recognition, San Juan, pp.338-343, 1997. [22] Wang X, Ying X, Liu Y-J, Xin S-Q,WangW, Gu X,Mueller-WittigW, He Y (2015) Intrinsic computation of centroidal Voronoi tessellation (CVT) on meshes. Comput Des, 51–61. doi:10.1016/j.cad.2014.08.023.