Abstract
Basic features for edge detection, such as derivatives, can be further manipulated to improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. In this study, Genetic Programming (GP) is used to automatically and effectively construct rotation variant features based on basic features from derivatives, F-test, and histograms of images. To reduce computational cost in the training stage, the basic features only use the horizontal responses to construct new horizontal features. These new features are then combined with their own rotated versions in the vertical direction in the testing stage. The experimental results show that the rotation variant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance.
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References
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Dollar, P., Tu, Z., Belongie, S.: Supervised learning of edges and object boundaries. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1964–1971 (2006)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience (2000)
Ebner, M.: On the edge detectors for robot vision using genetic programming. In: Proceedings of Horst-Michael Groβ, Workshop SOAVE 1997 - Selbstorganisation von Adaptivem Verhalten, pp. 127–134 (1997)
Fu, W., Johnston, M., Zhang, M.: Genetic programming for edge detection using blocks to extract features. In: Genetic and Evolutionary Computation Conference, pp. 855–862 (2012)
Fu, W., Johnston, M., Zhang, M.: Genetic programming for edge detection via balancing individual training images. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)
Golonek, T., Grzechca, D., Rutkowski, J.: Application of genetic programming to edge detector design. In: Proceedings of the International Symposium on Circuits and Systems, pp. 4683–4686 (2006)
Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour and boundary detection improved by surround suppression of texture edges. Image and Vision Computing 22(8), 609–622 (2004)
Harris, C., Buxton, B.: Evolving edge detectors with genetic programming. In: Proceedings of the First Annual Conference on Genetic Programming, pp. 309–314 (1996)
Kadar, I., Ben-Shahar, O., Sipper, M.: Evolution of a local boundary detector for natural images via genetic programming and texture cues. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1887–1888 (2009)
Lim, D.H., Jang, S.J.: Comparison of two-sample tests for edge detection in noisy images. Journal of the Royal Statistical Society. Series D (The Statistician) 51(1), 21–30 (2002)
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 530–549 (2004)
Moreno, R., Puig, D., Julia, C., Garcia, M.: A new methodology for evaluation of edge detectors. In: Proceedings of the 16th IEEE International Conference on Image Processing (ICIP), pp. 2157–2160 (2009)
Papari, G., Campisi, P., Petkov, N., Neri, A.: A biologically motivated multiresolution approach to contour detection. EURASIP Journal on Applied Signal Processing 2007, 119–119 (2007)
Papari, G., Petkov, N.: Edge and line oriented contour detection: state of the art. Image and Vision Computing 29, 79–103 (2011)
Poli, R.: Genetic programming for image analysis. In: Proceedings of the First Annual Conference on Genetic Programming, pp. 363–368 (1996)
Smart, W., Zhang, M.: Probability Based Genetic Programming for Multiclass Object Classification. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 251–261. Springer, Heidelberg (2004)
Wang, J., Tan, Y.: A novel genetic programming based morphological image analysis algorithm. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 979–980 (2010)
Zhang, Y., Rockett, P.I.: Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 795–802 (2005)
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Fu, W., Johnston, M., Zhang, M. (2013). Genetic Programming for Automatic Construction of Variant Features in Edge Detection. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_36
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DOI: https://doi.org/10.1007/978-3-642-37192-9_36
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