The week's pick
Reseach Article
Extraction of Building in Satellite image THR using Features Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 181 - Number 10 |
| Year of Publication: 2018 |
| Authors: Zaaj Ibtissam, Chaouki Brahim El Khalil, Masmoudi Lhoussaine |
10.5120/ijca2018917649
|
Zaaj Ibtissam, Chaouki Brahim El Khalil, Masmoudi Lhoussaine . Extraction of Building in Satellite image THR using Features Detection. International Journal of Computer Applications. 181, 10 ( Aug 2018), 23-27. DOI=10.5120/ijca2018917649
Abstract
Edge detection is a critical stage in many computer vision systems as image segmentation and object detection. As it is difficult to detect image edges with precision and with low complexity, it is appropriate to find new methods for edge detection. In this paper, we take advantage of Corner detection to detect edges in a multi-scale way with low complexity, and we propose a novel corner feature. The principle of Corner detectors is always the same; it looks for a quick change of direction of the contour. In the first part, this principle has been used to detect the corners of our image since the general objective is to detect buildings from THR satellite images based on the geometrical shape of buildings. Histogram has too been used as a next step to analyze R in order to set the number of points of interest. The second part of this work used to detect the edges points with canny, choosing different thresholds to view the problem of canny to the automation of the threshold. Finally, an automatic method has been proposed to partly answer the problem of the operator of Harris and Canny; by combining the results of the first part with the result of the second part. The contribution is to choose the Harris operator as the selected threshold determiner. The effectiveness of the proposed method is supported by the experimental results that prove that the method is faster than many competing state-of-the-art approaches and can be used in real-time applications.
References
- C. Harris, M. Stephens.1988, “A combined corner and edge detector,” presented at the In: Proc. 4th Alvey Vision Conf.
- H. Moravec.1980, Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover, Robotics Institute, Carnegie-Mellon University.
- L. Yi-bo, L. Jun-jun, Harris corner detection algorithm based on improved contourlet transform, Proc. Eng. 15 (0) (2011) 2239–2243.
- A. Kovacs, T. Sziranyi.2012, Harris function based active contour external force for image segmentation, Pattern Recogn. Lett. 33 (9) 1180–1187.
- B. Telle, M.J. Aldon.2002, Interest points detection in color images, in: Proc IARP Workshop on Machine Vision Applications, pp. 550–553.
- S. Nassif, D. Capson, A. Vaz.1997, Robust real-time corner location measurement, in: IEEE Instrumentation and Measurement Technology Conference, pp. 106–111.
- K. Mikolajczyk, C. Schmid.2004, Scale and affine invariant interest point detectors, Int. J. Comput. Vis. 60 (1) 63–86.
- S.C. Pei, J.J. Ding.2005, New corner detection algorithm by tangent and vertical axes and case table, in: International Conference on Image Processing, pp. 365–368.
- D. Marr, E.1980. Hildreth, Theory of edge detection, Proceedings of the Royal Society of London 207 (1167) (1980) 187–217.
- J. Canny.1986, A computational approach to edge detection, IEEE Transactions Pattern Analysis and Machine Intelligence 8 (1986) 679–698.
- C. Ducottet, T. Fournel, C. Barat.2004, Scale-adaptive detection and local characterization of edges based on wavelet transform, Signal Processing 84 (2004) 2115–2137.
- K.N. Le, K.P. Dabke, G.K. Egan.2006, On mathematical derivations of auto-term functions and signal-to-noise ratios of Choi–Williams, first- and nth-order hyperbolic kernels, Digital Signal Processing 16 (2006) 84–104.
- S. Lu, Z. Wang, J. Shen.2003, Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement, Pattern Recognition 36 (2003) 2395–2409.
- R. Rakesh, P. Chaudhuri, C.A. Murthy.2004, Thresholding in edge detection: a statistical approach, IEEE Transactions on Image Processing 13 (2004) 927–936.
- J. Bezdek, R. Chandrasekhar, Y. Attikouzel.1998, A geometric approach to edge detection, IEEE Transactions on Fuzzy Systems 6 (1998) 52–75.
- D.-S. Kim, W.-H. Lee, I.-S.2004. Kweon, Automatic edge detection using 3 3 ideal binary pixel patterns and fuzzy-based edge thresholding, Pattern Recognition Letters 25 (2004) 101–106.
- M. Wang, J.S. Jin, Y. Jing, X. Han, L. Gao, L. Xiao.2016, The improved Canny edge detection algorithm based on an anisotropic and genetic algorithm, Chinese Conference on Image and Graphics Technologies, Springer, Singapore, 2016, pp. 115–124.
- R. Gupta.2016, Enhanced edge detection technique for satellite images, International Conference on Cloud Computing and Security, Springer International Publishing, pp. 273–283.
- O. Cheng, W. Guangzhi, Z. Quan, K. Wei, D. Hui.2005, Evaluating Harris method in camera calibration, in: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 6383–6386.
- Q. Zhu, B.Wu, N.Wan, A sub-pixel location method for interest points by means of the Harris interest strength, Photogram. Rec. 22 (120) (2007) 321–335.
- I.R. Peter, Performance assessment of feature detection algorithms a methodology and case study on corner detectors, IEEE Trans. Image Process. 12 (12) (2003) 1668–1676.
- F. Shi, X. Huang, and Y. Duan.2009, “Robust harris-laplace detector by scale multiplication,” in Advances in Visual Computing, ser. Lecture Notes in Computer Science, vol. 5875. Springer Berlin / Heidelberg, pp. 265–274.
- Cai LH, Liao YH, Guo DH.2008. Study on Image Stitching Methods and Its Key Technologies [J], computer technology and developmemt,p.1-4,20.
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