ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-4 Issue-3                       
Year of Publication : 2013
Authors :  Tejasha Patil , Shweta Mishra , Poorva Chaudhari , Shalaka Khandale


Tejasha Patil , Shweta Mishra , Poorva Chaudhari , Shalaka Khandale. "IMAGE STITCHING USING MATLAB". International Journal of Engineering Trends and Technology (IJETT). V4(3):302-306 Mar 2013. ISSN:2231-5381. published by seventh sense research group


Images are an integral part of our daily lives. Image stitching is the process performed to generate one panoramic image from a series of smaller, overlapping images. Stitched images are used in applications such as interactive panoramic viewing of images, architectural walk - through, multi - node movies and other applications associated with modeling the 3D environment using images acquired from the real world. Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image processing techniques involve treating the image as a two - dimensional signal and applying standard signal processing techniques to it. Specifically, image stitching presents different stages to render two or more overlapping images into a seamless sti tched image, from the detection of features to blending in a final image. In this process, Scale Invariant Feature Transform (SIFT) algorithm [1] can be applied to perform the detection and matching control points step, due to its go od properties. The process of create an automatic and effective whole stitching process leads to analyze different methods of the stitching stages. Several commercial and online software tools are available to perform the stitching process, offering divers e options in different situations.


[1] Y. Yu, K. Huang, and T. Tan, “A Harris - like scale invariant feature dete ctor,” in Proc. Asian Conf. Comput. Vis. , 2009, pp. 586 – 595.
[2] J. M. Morel and G. Yu, “Asift: A new framework for fully affine invariant image comparison,” SIAM J. Imag. Sci. , vol. 2, no. 2, pp. 438 – 469, Apr. 2009.
[3] J. Rabin, J. Delon, Y. Gou sseau, and L. Moisan, “RANSAC: A robust algorithm for the recognition of multiple objects,” in Proc. 3D’PVT, 2010.
[4] M. Krulakova, Matrix technique of image processing in Matlab, ICCC`2010: proceedings of 11th Interna tional Carpathian Control Conference, 26 - 28 May, 2010, Eger, Hungary, Rekatel 2010, pp. 203 - 206, ISBN 978 - 963 - 06 - 9289 - 2.
[5] Wei Xu and Jane Mulligan. Performance evaluation of color correction approaches for automatic multi - view image stitching. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pages 263 - 270, San Francisco, CA, USA, June 2010.
[6] Oliver Whyte, Josef Sivic1, Andrew Zisserman, and Jean Ponce. Nonuniform deblurring for shaken images. In 2010 IEEE Conferenc e on Computer Vision and Pattern Recognition (CVPR 2010), pages 491 - 498, San Francisco, CA, USA, June 2010.
[7] Xianyong Fang, Bin Luo, Haifeng Zhao, Jin Tang, Biao He, and Hao Wu. A new multi - resolution image stitching with local and global alignment . IET Computer Vision, 2010 .
[8] MathWorks, MATLAB Builder JA 2 user`s guide. [online] August 18, 2010 [cited 12.01.2011] available from ? - doc/ javabuilder/javabuilder.pdf ? .
[9] MathWorks, Bringing java classes and methods into MATLAB workspace. [online] [cited 12.01.2011] available from ? techdoc/matlab - external/ ml ?
[10] Chen Hui, Long AiQun, Peng YuHua. Building Panoramas from Photographs Taken with An Uncalibrated Hand - Held Camera. Chinese Journal of Computers, 2009,(2):328 - 335.
[11] Hsieh, J. - W. Fast stitching algorithm for moving object detection and mosaic construction. in IEEE International Conference on Multimedia & Expo. 2003. Baltimore, Maryland, USA.

seamless stitched image, Panoramic image.