The week's pick
Reseach Article
Article:A New Method for Edge Extraction in Images using Local Form Factors
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 21 - Number 2 |
| Year of Publication: 2011 |
| Authors: Supratim Gupta, Aurobinda Routray, Anirban Mukherjee |
10.5120/2484-3344
|
Supratim Gupta, Aurobinda Routray, Anirban Mukherjee . Article:A New Method for Edge Extraction in Images using Local Form Factors. International Journal of Computer Applications. 21, 2 ( May 2011), 15-22. DOI=10.5120/2484-3344
Abstract
This article proposes a new spatial domain measure of local energy to extract the image features like edges. We define the measure as the local form factor (FF). It is the ratio of RMS to average of the pixel values in a region. Inverse square of the local FF around a center pixel is defined as an index of edge strength at that pixel. The proposed method could be applied directly on any image without smoothing for noise removal. It only needs an estimate of the Signal-to-Noise ratio (SNR) of the images to compensate the effect of noise. The compensated feature image is passed through non minimum suppression and universal thresholding processes to produce the final edge map. The performance of the method is assessed using Baddeley Error Metric (BEM) and compared with those resulted from the popular Canny edge detector with different scales. The experimental results are encouraging the application of the method to extract edges and hence can be used as a potential candidate for general feature extraction.
References
- Sonka M., Hlavac V., and Boyle R., “Image Processing, Analysis, and Machine Vision,” 2nd Ed., Singapore: Thomson Learning Asia Pte. Ltd., 1998.
- Gonzalez R. C., and Woods R. E., “Digital Image Processing,” 3rd Ed., vol. 2. Singapore: Pearson Education Singapore Pte. Ltd., 2008.
- Pellegrino F. A., Vanzella W., and Torre V., “Edge Detection Revisited,” IEEE Trans. Syst., Man, and Cybern.—Part B: Cybern., vol. 34, no. 3, pp. 1500 1518, Jun., 2004.
- Rakesh R. R., Chaudhuri P., and Murthy C. A., “Threshonding in Edge Detection: A Statistical Approach,” IEEE Trans. Image Process., vol. 13, no. 7, pp. 927 936, Jul., 2004.
- Jain A. K., “Fundamentals of Digital Image Processing,” 4th Indian reprint, Singapore, Pearson Education Pte. Ltd., 2005.
- Canny J., “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. and Mach. Intell., vol. PAMI 8, no. 6, pp. 679 698, Nov., 1987.
- Sarkar S., and Boyer K. L., “Optimal Infinite Impulse Response Zero Crossing Based Edge Detectors,” CVGIP: Image Understanding, vol. 54, no. 2, pp. 224 243, Sep., 1991.
- Marr D., and Hildreth E., “Theory of Edge Detection,” in Proc. Roy. Soc. of London, Series B, Biological Sci., vol. 207, no. 1167, Feb., 1980, pp. 187 217.
- Scotney B. W., and Coleman S. A., “Improving angular error via systematically designed near-circular Gaussian-based feature extraction operators,” Pattern Recognition, vol. 40, no. 5, pp. 1451 1465, May, 2007.
- Morrone M. C., and Burr D. C., “Feature Detection in Human Vision: A Phase Dependent Energy Model,” in Proc. Roy. Soc. of London, Series B, Biological Sci., vol. 235, no. 1280, Dec., 1988, pp. 221 245.
- Morrone M. C., and Owens R. A., “Feature Detection from Local Energy,” Pattern Recognition Letts., vol. 6, no. 5, pp. 303 313, Dec., 1987.
- Kovesi P., “Invariant Measures of Image Features from Phase Information,” Ph.D. Desertation, Dept. Psychology, Western Australia Univ., May, 1996.
- Kovesi P., “ Phase Congruency Detects Corners and Edges,” in Proc. 7th Int. Conf. Digital Image Computing: Techniques and Applicat., Sydney, Australia, Dec., 2003, pp. 309–318.
- Kovesi P., “Edges Are Not Just Steps,” in Proc. 5th Asian Conf. on Comput. Vision, Melbourne, Australia, Jan., 2002, pp. 822 827.
- Smith S. M., and Brady J. M., “SUSAN¬¬–A New Approach to Low Level Image Processing,” Int. J. Comput. Vision, vol. 23, no. 1, pp. 45 78, 1997.
- Bankman I. N., and Rogala E. W., “Corner Detection for Identification of Man Made Objects in Noisy Aerial Images,” in Proc. Int. Conf. Soc. of Photographic Instrumentation Engineers, vol. 4726, 2002, pp. 304 309.
- Coleman S. A., Scotney B. W., and Suganthan S., “Multi-scale edge detection on range and intensity images ,” Pattern Recognition, vol. 44, no. 4, pp. 821 838, Apr., 2011.
- Yi S., Labate D., Easley G. R., and Krim H., “A Shearlet Approach to Edge Analysis and Detection,” IEEE Trans. Image Process., vol. 18, no. 5, pp. 929 941, May, 2009.
- Konishi S., Yuille A. L., Coughlan J. M., and Zhu S. C., “Statistical Edge Detection: Learning and Evaluating Edge Cues,” IEEE Trans. Pattern Anal. and Mach. Intell., vol. 25, no. 1, pp. 57 74, Jan., 2003.
- Konishi S., Coughlan J. M., and Yuille A. L., “Statistical Approach to Multi Scale Edge Detection,” Image Vision and Comput., vol. 21, no. 1, pp. 37-48, Jan., 2003.
- J. A. Baddeley, “An error metric for binary images,” in Proc. IEEE Workshop on Robust Comput. Vision, Bonn, 1992, pp. 59–78.
- Sim K. S., Lai M. A., Tso C. P., and Teo C. C., “Single Image Signal to Noise Ratio Estimation for Magnetic Resonance Images,” J. Med. Syst., Springer Link, Jul., 2009, doi 10.1007/s10916-009-9339-9.
- Mukherjee D., and Ratnaparkhi M. V., “On the Functional Relationship between Entropy and Variance with Related Application,” Commun. in Stat. – Theory and Methods, vol. 15, no. 1, pp. 291 311, 1986.
- Martin D., Fowlkes C., Tal D., and Malik J., “A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,” in Proc. 8th Int. Conf. Comput. Vision, vol. 2, Jul., 2001 , pp. 416 423.
- Lopez Molina C., Bustince H., Fernandez J., Couto P., and Baets B. D., “A Gravitational Approach to Edge Detection based on Triangular Norms,” Pattern Recognition, Elsivier, vol. 43, no. 11, pp. 3730 3741, Nov. 2010.
- Lopez Molina C., Bustince H., Fernandez J., Barrenechea E., Couto P., and Baets B. D., “A t Norm Based Approach to Edge Detection,” Bio-Inspired Systems: Computational and Ambient Intell., Lecture notes in Comput. Sci., vol. 5517, pp. 302 309, 2009.
- Venkatesh S., and Kitchen L. J., “Edge Evaluation Using Necessary Components,” Comput. Vision, Graph. and Image Process., vol. 54, no. 1, pp. 23 30, Jan., 1992.
- Demigny D., Lorca F. G., and Kessal L., “Evaluation of edge detectors performances with a discrete expression of Canny's criteria,” in Proc. Int. Conf. Image Process., vol. 2, Oct., 1995, pp. 169 172.
- Pratt W. K., “Digital Image Processing,” 3rd Ed., John Wiley & Sons inc., Singapore, 2003.
- Bowyer K., Kranenburg C., and Dougherty S., “Edge Detector Evaluation Using Empirical ROC Curves,” Comput. Vision and Image Understanding, Elsevier, vol. 84, no. 1, pp. 77 103, Oct., 2001.
- Medina Carnicer R., Carmona Poyato A., Munoz Salinas R., and Madrid Cuevas F.J., “Determining Hysteresis Thresholds for Edge Detection by Combining the Advantages and Disadvantages of Thresholding Methods,” IEEE Trans. Image Process., vol. 19, no. 1, pp. 165 173, Jan., 2010
Index Terms
Keywords