On the use of quasi-arithmetic means for the generation of edge detection blending functions

The edge detection process can be broken down into four basic transformations, modifying the image from the original presentation to the final edges one. The adoption of this framework makes the process far more understandable, and offers an starting point for the combination and comparison of different edge detection methods. In this work we analyze the role of the third of the transformations, the blending, where the edge features are combined to obtain the edginess values. This work studies the use of quasi-aritmethic means for the combination of the edge features. Moreover, we show results obtained with different operators on real images, in order to illustrate the importance of the blending phase in the edge detection process. Results will show the impact of the function selection in the final results.

[1]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Lawrence B. Wolff,et al.  Multispectral image visualization through first-order fusion , 2002, IEEE Trans. Image Process..

[3]  M. J. Frank,et al.  Associative Functions: Triangular Norms And Copulas , 2006 .

[4]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  R. P. Johnson,et al.  Contrast based edge detection , 1990, Pattern Recognit..

[7]  DeLiang Wang,et al.  Texture classification using spectral histograms , 2003, IEEE Trans. Image Process..

[8]  In-So Kweon,et al.  Automatic edge detection using 3 x 3 ideal binary pixel patterns and fuzzy-based edge thresholding , 2004, Pattern Recognit. Lett..

[9]  A. Baddeley An Error Metric for Binary Images , 1992 .

[10]  James C. Bezdek,et al.  A geometric approach to edge detection , 1998, IEEE Trans. Fuzzy Syst..

[11]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Francisco José Madrid-Cuevas,et al.  On candidates selection for hysteresis thresholds in edge detection , 2009, Pattern Recognit..

[13]  Mariano Eriz Aggregation Functions: A Guide for Practitioners , 2010 .

[14]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[15]  Salvatore Tabbone,et al.  A multi-scale edge detector , 1993, Pattern Recognit..

[16]  Humberto Bustince,et al.  Interval-valued fuzzy sets constructed from matrices: Application to edge detection , 2009, Fuzzy Sets Syst..

[17]  Qiang Liu,et al.  A novel approach for edge detection based on the theory of universal gravity , 2007, Pattern Recognit..

[18]  Francisco José Madrid-Cuevas,et al.  Unimodal thresholding for edge detection , 2008, Pattern Recognit..

[19]  Lily R. Liang,et al.  Competitive fuzzy edge detection , 2003, Appl. Soft Comput..

[20]  Francisco José Madrid-Cuevas,et al.  Automatic generation of consensus ground truth for the comparison of edge detection techniques , 2008, Image Vis. Comput..

[21]  Fabrizio Russo,et al.  FIRE operators for image processing , 1999, Fuzzy Sets Syst..

[22]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[23]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  Samuel Morillas,et al.  Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images , 2009, IEEE Transactions on Image Processing.

[25]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

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

[27]  F. Russo,et al.  Edge extraction by FIRE operators , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

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

[29]  Humberto Bustince,et al.  A t-Norm Based Approach to Edge Detection , 2009, IWANN.