International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 5, No. 12, 8 Oct. 2013
Cover page and Table of Contents: PDF (size: 650KB)
Digital Image Texture Classification and Detection Using Radon Transform
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Author(s)
Satyabrata Sahu
1,*
Santosh Kumar Nanda
2
Tanushree Mohapatra
3
Index Terms
Edge detection, Fast Fourier Transform, Discrete Wavelet Transform, Radon Transform
Abstract
A novel and different approach for detecting texture orientation by computer was presented in this research work. Many complex real time problem example detection of size and shape of cancer cell, classification of brain image signal, classification of broken bone structure, detection and classification of remote sensing images, identification of foreign particle in universe, detection of material failure in construction design, detection and classification of textures in particularly fabrications etc where edge detection and both vertical and horizontal line detection are essential. Thus researches need to develop different algorithm for this above complex problem. It is seen from literature that conventional algorithm DCT, FFT are all highly computational load and hence impossible task to implemented in hardware. These difficulties were solved in this particular research work by applying DWT and radon transform. It was seen from the simulation result that with very high computational load the entire algorithm takes very less CPU time and proved its robustness.
Cite This Paper
Satyabrata Sahu, Santosh Kumar Nanda, Tanushree Mohapatra,"Digital Image Texture Classification and Detection Using Radon Transform", IJIGSP, vol.5, no.12, pp.38-48, 2013. DOI: 10.5815/ijigsp.2013.12.06
Reference
[1]W. K. Pratt. Digital image processing, Wiley-Interscience, 2nd edition, 1991.
[2]Mitra,Basu, Gaussian-Based Edge-Detection Methods A Survey ,IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 32, No.3, August 2002, pp. 252-260.
[3]J. Canny, A Computational Approach to Edge Detection, IEEE Transaction on Pattern Analysis and Machine intelligence, No. 6, pp. 679-698, 1986.
[4]D.Heric et.al, Combined edge detection using wavelet transform and signal registration, Elsevier Journal of Image and Vision Computing 25 (2007) 652–662.
[5]M.Y. Shih et.al, Wavelet based multi resolution edge detection and tracking, Elsevier Journal of Image and Vision Computing 23 (2005) 441–451.
[6]S.Konishi et.al, Statistical Edge Detection: Learning and Evaluating Edge Cues, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 25, No. 1, pp 57- 74, 2003.
[7]J. C. Bezdek,et.al., A Geometric Approach to Edge Detection, IEEE Transactions On Fuzzy Systems, Vol. 6, No. 1, pp 52-75,1998.
[8]A. D. Santiset.al, A Bayesian Approach to Edge Detection in Noisy Images, IEEE transactions On Circuits And Systems I: Fundamental Theory And Applications, Vol. 46, No. 6, pp 686-699, 1999 .
[9]D.Marr et.al, Theory of edge detection‖, Proc. Royal Society of London, B, 1980, 207, pp. 187–217.
[10]A.L.Yuille et.al, Scaling theorems for zero-crossings, IEEE Transactions On Pattern analysis And Machine Intelligence vol. PAMI-8, pp. 15–25, Jan. 1986.
[11]B.G. Schunck, Edge detection with Gaussian filters at multiple scales, in Proc. IEEE Comp. Soc.Work. Comp. Vis., 1987, pp. 208–210.
[12]A.P.Witkin, Scale-space filtering, in Proc. Int. Joint. Conf. Artificial Intelligence, vol. 2, 1983, pp. 1019–1022.
[13]F.Bergholm, Edge focusing, IEEE Transactions On Pattern Analysis And Machine intelligence‖, vol. PAMI-9, pp. 726–741, June 1987.
[14]V.Lacroix, The primary raster: A multi-resolution image description in Proc. 10th Int. Conf. Pattern Recognition, 1990,pp. 903–907. ISSN: 2
[15]D.J. Williams et.al. , Edge contours using multiple scales, Comput. Vis. Graph Image Process. vol. 51, pp. 256–274, 1990.
[16]A.Goshtasby, On edge focusing‖, Image Vis. Comput, vol. 12, pp. 247–256, 1994.
[17]H. Jeong et.al, Adaptive determination of filter scales for edge-detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, pp. 579–585, May 1992.
[18]G.Deng et.al, An adaptive Gaussian filter for noise reduction and edge detection, in Proc. IEEE Nucl. Sci. Symp.Med. Im. Conf., 1994, pp. 1615–1619.
[19]M.Bennamount et.al, Optimal parameters for edge detection, in Proc.IEEE Int. Conf. SMC, vol. 2, 1995, pp. 1482–1488.
[20]P.Perona et.al. Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Machine Intell. vol. 12, pp. 629–639, July 1990.
[21]R.M. Haralick. Ridge and Valley on Digital Images. Computer Vision, Graphics and Image Processing, 22, 28-38, 1983.
[22]G.Giraudon. Edge Detection from Local Negative Maximum of Second Derivative. In Proceedings of IEEE, International Conference on Computer Vision and Pattern Recognition, 643-645, 1985.
[23]D. Ziou. Line Detection Using an Optimal IIR Filter. Pattern Recognition, 24(6), 465- 478, 1991.
[24]K. Koundinya and B.Chanda.Detecting Lines in Gray Level Images Using Search Techniques. Signal Processing, 37, 287-299, 1994.
[23]A. Koschan et.al. Detection and Classification of Edge sin Color Images, IEEE Signal.
[24]Robi Polikar, ‘The Story of Wavelets’, In physics and Modern topics in Mechanical and Electrical Engineering (ed. N Mastorakis), 192-197, World Scientific and Eng. Society Press.
[25]R R Coifman and D L Donoho, ‘Translation Invariant De-noising’, in A Antoniadis (editor): Wavelets and Statistics, Springer Verlag, 1995.
[26]I Cohen, S Raz, and D Malah, ‘Shift-invariant Wavelet Packet Bases’, Proc. ICASSP-95,Detroit, MI, 1081-1084, 1995.
[27]M C Morrone, and D C Burr, ‘Feature Detection in Human Vision: A Phase Dependent Energy Model’, Proc. R. Soc. Lond., B 235, 221, 1988.
[28]A V Oppenheim, and J S Lim, ‘The Importance of Phase in Signals’, Proc. IEEE, 69, 529-541, 1981.
[29]G P Lorenzetto, and P Kovesi, ‘A Phase Based Image Comparison Technique’,DICTA99, University of Western Australia, 1999.
[30]J Driesen, and R Belmans, ‘Time-Frequency Analysis in Power Measurement Using Complex Wavelets’, IEEE Int. Sympo. on Cir. and Sys. (ISCAS2002), 681-684, 2002.
[31]Asano,A. “Radon transformation and projection theorem”, topic 5, lecture notes of subject pattern information processing, 2002 autumn semester.
[32]Kupce, E., Freeman, R., “The Radon Transform: A New Scheme for Fast Multidimensional NMR”, Concepts in Magnetic Resonance, Wiley Periodicals, Vol. 22,pp. 4-11, 2004.
[33]J.Radon, The Radon Transform and Some of Its Applications (“trans-lation of radon’s 1917 paper”), 1983.
[34]H. Al-Yousefi and S. S. Udpa, “Recognition of arabic characters,” IEEE Trans. Pattern Anal. Mach. Intell: vol. 14, no. 8, pp. 853–857, Aug.1992.
[35]A. K. Jain, Fundamentals of Digital Image Processing. Upper Saddle River, NJ: Prentice- Hall, 1989.
[36]J. L. C. Sanz, E. B. Hinkle, and A. K. Jain, Radon and Projection Trans-form-Based Computer Vision: Algorithms, a Pipeline Architecture, and Industrial Applications.New York: Springer-Verlag, 1988.
[37]O.Al-Shaykh and J.Doherty,“Invariant image analysis based on radon transform and svd,” IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. ,vol. 43, no. 2, pp. 123– 133, 1996.
[38]Rafael C. Gonzalex, Richard E. Woods, Image Segmentation in the book titled Digital Image Processing Chapter 10,Page No>589-664,Second Edition.
[39]Paulus, Color image processing: methods and applications in color image Segmentation Selected Techniques, chapter 5, pp.103-128, CRC Press, Boca Rato, Fla, USA, 2007.
[40]D. Heric, and D. Zazula, Combined edge detection using wavelet transform and signal registration, Elsevier Journal of Image and Vision Computing 25 (2007) 652–662.
[41]M. Y. Shih, D. C. Tseng, A wavelet based multi resolution edge detection and tracking, Elsevier, Journal of Image and Vision Computing 23 (2005) 441–451.
[42]Guangcan Liu, Zhouchen Lin , Senior Member, IEEE, and Yong Yu:Radon representation based feature descriptor for texture classification, IEEE Transactions On Image Processing, Vol. 18, No 5, May 2009.