Multi-frequency transformation for edge detection

This paper investigates the use of a multi-frequency transformation for edge detection. Most edge detectors are good at detecting non-texture edges, but have problems with texture edges. In order to detect texture edges, prior knowledge is usually required to avoid heavy computational cost. In this study, a fast and simple transformation based on multi-frequencies is proposed to improve detection performance and the relevant analysis for proper responses on texture and non-texture edges is given. The experimental results show that a classical edge detector improves detection performance after using the proposed transformation based on multi-frequencies, and the detection result from the edge detector using the transformation is better than the detection result from some popular feature extraction techniques, such as extraction based on Gaussian gradients, histogram gradients, and surround suppression.

[1]  Domenec Puig,et al.  A new methodology for evaluation of edge detectors , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[2]  Mitra Basu,et al.  Gaussian-based edge-detection methods - a survey , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[3]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[5]  Nicolai Petkov,et al.  Contour and boundary detection improved by surround suppression of texture edges , 2004, Image Vis. Comput..

[6]  Pritimoy Bhattacharyya,et al.  Edge detection in untextured and textured images-a common computational framework , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[7]  Mark Nitzberg,et al.  Nonlinear Image Filtering with Edge and Corner Enhancement , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Iasonas Kokkinos,et al.  Boundary Detection Using F-Measure-, Filter- and Feature- (F3) Boost , 2010, ECCV.

[9]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[10]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[11]  Bryan W. Scotney,et al.  Multi-scale edge detection on range and intensity images , 2011, Pattern Recognit..

[12]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  S. J. Jang,et al.  Comparison of two‐sample tests for edge detection in noisy images , 2002 .

[14]  Zhuowen Tu,et al.  Supervised Learning of Edges and Object Boundaries , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Nicolai Petkov,et al.  Edge and line oriented contour detection: State of the art , 2011, Image Vis. Comput..

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jianqin Zhou,et al.  On discrete cosine transform , 2011, ArXiv.

[18]  Mark Johnston,et al.  Genetic programming for edge detection: A global approach , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).