Harris Extraction and SIFT Matching for Correlation of Two Tablets

This article presents the developments of efficient algorithms for tablet copies comparison. Image recognition has specialized use in digital systems such as medical imaging, computer vision, defense, communication etc. Comparison between two images that look indistinguishable is a formidable task. Two images taken from different sources might look identical but due to different digitizing properties they are not. Whereas small variation in image information such as cropping, rotation, and slight photometric alteration are unsuitable for based matching techniques. In this paper we introduce different matching algorithms designed to facilitate, for art centers, identifying real painting images from fake ones. Different vision algorithms for local image features are implemented using MATLAB. In this framework a Table Comparison Computer Tool “TCCT" is designed to facilitate our research. The TCCT is a Graphical Unit Interface (GUI) tool used to identify images by its shapes and objects. Parameter of vision system is fully accessible to user through this graphical unit interface. And then for matching, it applies different description technique that can identify exact figures of objects.




References:
[1] S. Seven, Feature Histograms for Content-Based Image Retrieval. PhD
The- sis, Albert-Ludwigs-University Freiburg. December (2002).
[2] K. Mikolajczyk, C. Schmid. Indexing Based on Scale Invariant Interest
Points. In: ICCV, (2001) 525-531.
[3] G. Van, M. Theo, and U, Dorin. "Affine photometric invariants for
planar intensity patterns". European Conference on Computer Vision
(ECCV), pp. 642-651, (1996).
[4] D. Lowe, Distinctive Image Features from Scale-Invariant
Keypoints.Inter-national Journal of Computer Vision, 60, 2 (2004),
pp.91-110.
[5] D. Lowe, Object Recognition from Local Scale Invariant Features. In
Proceedings of the International Conference on Computer Vision, pages
1150-1157, Corfu, Greece, September (1999).
[6] S. Kima, J. Leea and J. Kima, new chain-coding algorithm for binary
images using run-length codes. Department of Electrical Engineering,
Korea Advanced Institute of Science and Technology, P.O. Box 150,
Chon gryang, 131, Seoul, Korea.
[7] B. Lucas and T. Kanade, an Iterative Image Registration Technique with
an Application to Stereo Vision. Computer Science Department
Carnegie-Mellon University Pittsburgh, Pennsylvania.
[8] D. FLEET, Phase-Based Disparity Measurement. Department of
Computing and Information Science.
[9] J. Crowley and A. Parker, A representation for shape based on peaks and
ridges in the difference of low-pass transform. IEEE Trans. on Pattern
Analysis and Machine Intelligence, (1984), 6(2):156-170.
[10] L. Peterson, "Fast Normalized Cross-Correlation," Industrial Light &
Magic. http://www.idiom.com/~zilla/Papers/nvisionInterface/nip.html.
[11] Grande Galerie. Le journal du Louvre - juin-juillet-ao├╗t. (2010).
[11] A. ALZAABI, G. ALQUIÉ, H. TASSADAQ, A. SEBA. TCCT: A GUI
Table Comparaison Computer Tool. International Joint Conferences on
Computer, Information, and Systems Sciences, and Engineering (CISSE
10). Bridgeport, CT, USA, (2010).