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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [1] R. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, “LSD: A fast line segment detector with a false detection control,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 722 -732, April 2010.
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    • [3] J. Cha, R. Cofer, and S. Kozaitis, “Extended Hough transform for linear feature detection,” Pattern Recognition, vol. 39, no. 6, pp. 1034 - 1043, 2006.
    • [4] K.-L. Chung, T.-C. Chang, and Y.-H. Huang, “Comment on: Extended Hough transform for linear feature detection,” Pattern Recognition, vol. 42, no. 7, pp. 1612 - 1614, 2009.
    • [5] S. Du, B. van Wyk, C. Tu, and X. Zhang, A“n improved Hough transform neighborhood map for straight line segments,” IEEE Transactions on Image Processing, vol. 19, pp. 573 -585, March 2010.
    • [6] S. Du, C. Tu, B. J. van Wyk, and Z. Chen, “Collinear segment detection using HT neighborhoods,” IEEE Transactions on Image Processing, vol. 20, pp. 3612 -3620, December 2011.
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    • [10] L. Zheng and D. Shi, A“dvanced Radon transform using generalized interpolated Fourier method for straight line detection,” Computer Vision and Image Understanding, vol. 115, pp. 152-160, February 2011.
    • [11] W. Pan, K. Qin, and Y. Chen, A“n adaptable-multilayer fractional Fourier transform approach for image registration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 400 -414, March 2009.
    • [12] M. Atiquzzaman and M. Akhtar, A“ robust Hough transform technique for complete line segment description,” Real-Time Imaging, vol. 1, no. 6, pp. 419 - 426, 1995.
    • [13] V. Kamat and S. Ganesan, A“ robust Hough transform technique for description of multiple line segments in an image,” in Proceedings of IEEE International Conference on Image Processing, vol. 1, pp. 216 -220, October 1998.
    • [14] C. G. Ho, R. C. D. Young, C. D. Bradfield, and C. R. Chatwin, A“ fast Hough transform for the parametrisation of straight lines using fourier methods,” Real-Time Imaging, vol. 6, no. 2, pp. 113 - 127, 2000.
    • [8] A. Borkar, M. Hayes, and M. Smith, “Robust lane detection and tracking with ransac and kalman filter,” in 16th IEEE International Conference on Image Processing (ICIP), pp. 3261-3264, November 2009.
    • [9] A. Borkar, M. Hayes, and M. Smith, A“ novel lane detection system with efficient ground truth generation,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, pp. 365-374, March 2012.
    • [10] D. Shi, L. Zheng, and J. Liu, A“dvanced Hough transform using a multilayer fractional Fourier method,” IEEE Transactions on Image Processing, vol. 19, pp. 1558 -1566, June 2010.
    • [11] S. R. Deans, “Hough transform from the Radon transform,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, pp. 185-188, March 1981.
    • [12] C. G. Ho, R. C. D. Young, C. D. Bradfield, and C. R. Chatwin, A“ fast Hough transform for the parametrisation of straight lines using fourier methods,” Real-Time Imaging, vol. 6, no. 2, pp. 113-127, 2000.
    • [13] W. Pan, K. Qin, and Y. Chen, A“n adaptable-multilayer fractional Fourier transform approach for image registration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 400-414, March 2009.
    • [14] L. Zheng and D. Shi, A“dvanced Radon transform using generalized interpolated Fourier method for straight line detection,” Computer Vision and Image Understanding, vol. 115, pp. 152-160, February 2011.
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  • Discovered through pilot similarity algorithms. Send us your feedback.

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