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.
[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).
[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).
@article{"International Journal of Information, Control and Computer Sciences:57774", author = "Ali Alzaabi and Georges Alquié and Hussain Tassadaq and Ali Seba", title = "Harris Extraction and SIFT Matching for Correlation of Two Tablets", abstract = "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.", keywords = "Harris Extraction and SIFT Matching", volume = "5", number = "4", pages = "369-5", }