Automatic Resolution Selection for Edge Detection Using Genetic Programming

When Genetic Programming is applied to edge detection, the computational cost is generally expensive. When a set of natural images are used to train edge detectors, using their high resolutions is more expensive than using their low resolutions. However, from existing reports, it is hard to find the influence on performance from using different sampling techniques on low resolutions. In this paper, we propose a GP system to automatically select the resolutions of a single training image to train edge detectors. The results of the experiments show that the GP system can effectively evolve edge detectors based on automatic resolution selection.

[1]  Pierre Collet,et al.  A GP Artificial Ant for Image Processing: Preliminary Experiments with EASEA , 2001, EuroGP.

[2]  Mengjie Zhang,et al.  Overview of Object Detection and Image Analysis by Means of Genetic Programming Techniques , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

[3]  Mark Johnston,et al.  Unsupervised learning for edge detection using Genetic Programming , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[4]  Yang Zhang,et al.  Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection , 2005, GECCO '05.

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

[6]  Riccardo Poli,et al.  Morphological algorithm design for binary images using genetic programming , 2006, Genetic Programming and Evolvable Machines.

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

[8]  Raymond H. Chan,et al.  Wavelet Algorithms for High-Resolution Image Reconstruction , 2002, SIAM J. Sci. Comput..

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

[10]  Bernard F. Buxton,et al.  Evolving edge detectors with genetic programming , 1996 .

[11]  Jerzy Rutkowski,et al.  Application of genetic programming to edge detector design , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[12]  Lei Zhang,et al.  Edge detection by scale multiplication in wavelet domain , 2002, Pattern Recognit. Lett..

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

[14]  R. Poli.  Genetic programming for image analysis , 1996 .

[15]  Mark Johnston,et al.  Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming? , 2014, EvoApplications.

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

[17]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  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.

[19]  R. Marks.  Introduction to Shannon Sampling and Interpolation Theory , 1990 .

[20]  Andrew M. Tyrrell,et al.  Design of highly parallel edge detection nodes using evolutionary techniques , 1999, Proceedings of the Seventh Euromicro Workshop on Parallel and Distributed Processing. PDP'99.

[21]  Mark Johnston,et al.  Genetic programming for edge detection using blocks to extract features , 2012, GECCO '12.

[22]  Mark Johnston,et al.  Figure of Merit Based Fitness Functions in Genetic Programming for Edge Detection , 2012, SEAL.

[23]  Jun Wang,et al.  A novel genetic programming based morphological image analysis algorithm , 2010, GECCO '10.

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

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