Study of Venation of leaf using Image Processing
Keywords:
Gradient, first order derivative, second order derivative, Gaussian of an image, Step edgesAbstract
In this work, various edge detection techniques have been implemented on an image of a leaf taken under different light conditions to study the venation pattern of that leaf. The efficiency and the accuracy of these techniques in detection of the veins have been compared and analyzed. Edge detection operators such as Sobel and Canny edge detectors have also been implemented in the leaf image to identify the difference between the two. Step edges and ridge edges have been found out by taking the Gaussian and the first and second order derivative of the Gaussian of the image. The experimental result showed that canny edge detectors have been more accurate and can detect the veins more precisely with more details as compared to other techniques.
References
Krishna Kant Singh ,Akansha Singh, “A Study Of Image Segmentation Algorithms For Different Types Of Images”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5,September 2010.
Peck, M.A., Linda G.Shapiro.George C.Stockman, “Computer Vision,” vol. 103, pp. 2005-2024, 2000.
A.Rosenfeld and D.pfaltz, “Sequential operations in Digital Picture Processing”, J.A.C.M. (1966) EN0 4 pp 471-494.
Nassir Salman, “Image Segmentation Based on Watershed and Edge Detection Techniques”, The International Arab Journal of Information Technology, Vol. 3, No. 2, April 2006
Tang H., Wu E. X., Ma Q. Y., Gallagher D., Perera G. M., and Zhuang T., “MRI Brain Image Segmentation by Multi-Resolution Edge Detection and Region Selection,” Computerized Medical Imaging and Graphics, vol. 24, no. 6, pp. 349-357, 2000.
Qing Wu and Yizhou Yu, “ Two level image segmentation based on region and edge integration”, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Wu, Z.,Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Trans Pat. Anal. Mach. Intell 11(1993) 1101-1113.
Giridhar S Sudi and Aashish A. Gadgil, "Improved Color Image Segmentation using Kindred Thresholding and Region Merging", International Journal of Computer Sciences and Engineering, Volume-01, Issue-03, Page No (1-9), Nov -2013
Canny J, “A computational approach to edge detection”, IEEE Trans Pat Anal Mach,Intell 8(1986) 679-698
Heeger, D, Fowlkes, C.,Malik,J, “ Learning to natural image boundaries using brightness and texture”, In: Neutral Information Processing Systems(NIPS) (2002)
Yan Li, Zheru Chi, Member, IEEE, and David D. Feng, Fellow, IEEE, “Leaf Vein Extraction Using Independent Component Analysis”, 2006 IEEE Conference on Systems, Man, and CyberneticsOctober 8-11, 2006, Taipei, Taiwan
Ghosh, Bibek Ranjan, et al. "Automatic Number Plate Recognition (ANPR) of Vehicle using Image processing and Graph based Pattern Matching." International Journal of Computer Sciences and Engineering, Vol.-3(1), PP(68-75) Feb 2015,
Ashna Jain, Harshitha Reddy and Sarthak Dubey, "Automated Driving Vehicle Using Image Processing", International Journal of Computer Sciences and Engineering, Volume-02, Issue-04, Page No (138-140), Apr -2014,
Yu. K. Ross, “Radiation Regime and Architectonics of Vegetation’’, Leningrad (1975).
Om Pavithra Bonam and Sridhar Godavarthy, “ Edge Detection”, University of Florida
W. B. Park, E. Ryu and Y. J. Song, “Visual feature extraction under wavelet domain for image retrieval,”, Key Engineering Materials, Vol. 277., pp. 206-211, 2005.
B. S. Manjunath, W. Y. Ma, “Texture features for browsing and retrieval of image data,”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18(8), pp. 837-842, 1996.
A. Hyvarinen, “A family of fixed-point algorithms for independent component analysis”, In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’97), pp. 3917-1920, Munich, Germany, 1997.
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