Canny Edge Detection in Cross-Spectral Fused Images
Keywords:Structuring elements, morphological filter, GQM, near infrared, fusion, cross-spectral images
Considering that the images of different spectra provide an ample information that helps a lo in the process of identification and distinction of objects that have unique spectral signatures. In this paper, the use of cross-spectral images in the process of edge detection is evaluated. This study aims to assess the Canny edge detector with two variants. The first relates to the use of merged cross-spectral images and the second the inclusion of morphological filters. To ensure the quality of the data used in this study the GQM (Goal-Question- Metrics), framework, was applied to reduce noise and increase the entropy on images. The metrics obtained in the experiments confirm that the quantity and quality of the detected edges increases significantly after the inclusion of a morphological filter and a channel of near infrared spectrum in the merged images.
Calero, C., Piattini, M., & Genero, M. (2001, July). Method for Obtaining Correct Metrics. ICEIS (2), (pp. 779-784).
Canny, J. (1986). A computational approach to edge detection. Transactions on pattern analysis and machine intelligence, 679-698.
Demigny, D. (2002, July). On Optimal Linear Filtering for Edge Detection. IEEE Trans. Image Processing, 11, 728-1220.
Deng, C. X., Wang, G. B., & Yang, X. R. (2013, July). Image edge detection algorithm based on improved canny operator. International Conference on Wavelet Analysis and Pattern Recognition, pp. 168-172.
Elder, J. H., & Zucker, S. W. (1998). Local scale control for edge detection and blur estimation, 20(7), 699-716. IEEE Transactions on pattern analysis and machine intelligence, 20(7), 699-716.
Esteves, J., Pastor-Collado, J., & Casanovas, J. (2002). Measuring sustained management support in ERP implementation projects: a GQM approach. AMCIS Proceedings, 190.
Heric, D., & Zazula, D. (2007). Combined edge detection using wavelet transform and signal registration. Image and Vision computing, 25(5), 652-662.
Ma, C., Gao, W., Yang, L., & Liu, Z. (2010, August). An improved Sobel algorithm based on median filter. In Mechanical and Electronics Engineering (ICMEE), 1, V1-88-V1-92.
Marr, D., & Hildreth, E. (1980). Theory of Edge Detection,”. Proc. Royal Soc.London, vol. 207,, 207, 187-217.
O'Callaghan, Robert, J., & Bull., D. R. (2005). Combined morphological-spectral unsupervised image segmentation. IEEE Transactions on Image Processing, 14(1), 49-62.
Patel, Dhiraj, K., Sagar, A., & More. (2013, January 4-6). Edge detection technique by fuzzy logic and Cellular Learning Automata using fuzzy image processing. Computer Communication and Informatics (ICCCI), 1-6.
Pohl, C., & Van , G. (n.d.). Multisensor image fusion in remote sensing: Concepts, methods and applications. 823-854.
Rosenfeld, A. (1970, May). A Nonlinear Edge Detection Technique. Proc. IEEE, 814-816.
Van Solingen, R., Basili, V., Caldiera, G., & Rombach, H. D. (2002). Goal question metric (gqm) approach. Encyclopedia of software engineering.
Wang, W., & Wang, L. (2009, April). Edge Detection of the Canny Algorithm Based on Maximum between-class Posterior Probability". Computer Applications, 29(A), 962-1027.
Wilkinson., G. G. (2005). Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Trans. Geosci. Remote Sens, 43(3), 433-440.
Xia, K. J., Yao, Y. F., Chang, J. Y., & Zhong, S. (2010, February). An Edge Detection Improved Algorithm Based on Morphology and Wavelet. Computer and Automation Engineering (ICCAE), 1(404-4).
Xu, P., Miao, Q., Shi, C., Zhang, J., & Yang, M. (2012, October). General method for edge detection based on the shear transform. IET image processing, 6(7), 839-853.
Xu, Q., Varadarajan, S., Chakrabarti, C., & Karam, L. J. (2014, July). A distributed canny edge detector: algorithm and FPGA implementation. IEEE Transactions on Image Processing, 23(7), 2944-2960.
Xu, Y., Weaver, J. B., & Healy, D. M. (1994). Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique. IEEE Transactions on Image Processing, 3(6), 747 - 758.
Zue, L. X., Li, T., & Wang, Z. C. (2010, September). Adaptive Canny edge detection algorithm. Jisuanji Yingyong Yanjiu, 27(9), 3588-3590.
How to Cite
This work is licensed under a Creative Commons 3.0 BY EC License.