Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Edif. Wilhelm Schickard-Institut für Informatik, Sand 1, Universität Tübingen, 72076, Tübingen, GermanyĬomputer Science, Aston Triangle, Aston University, B4 7ET, Birmingham, UK Proceedings of the IEEE, 1423–1447 (1999)ĭepartment of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UKĭepartment of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italyĭepartmento Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain Yao, X.: Evolving artificial neural networks. Advances in Computer Science and Engineering 42, 91–102 (2009) Villegas-Cortez, J., Sossa, H., Aviles-Cruz, C., Olague, G.: Automatic synthesis of associative memories by genetic programming, a first approach. In: Iberoamerican Congress on Pattern Recognition, pp. Vázquez, R.A., Sossa, H.: Hetero-associative memories for voice signal and image processing. Silva, S., Almeida, J.: Gplab-a genetic programming toolbox for matlab (2004), Potter, M.A., Jong, K.A.D.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. In: International Conference on Pattern Recognition (2008) Perez, C., Olague, G.: Learning invariant region descriptor operators with genetic programming and the f-measure. In: International Conference on Pattern Recognition, Hong Kong, China, August 20-24 (2006) Olague, G., Puente, C.: Honeybees as an intelligent based approach for 3d reconstruction. The results established a efficient way of filter creation with the use of the genetic programming.Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. Through the GPLab toolbox together with Matlab, the automatic creation of filters for objects identification was possible, so the detection and deletion of elements in images can be used by other support systems to automatic operation in an industrial environment. The results are presented qualitatively as well as quantitatively through comparative images of the evaluated methods and statistical measures, respectively. Two methods that possess different approaches are evaluated one uses operations between pixels and other mathematical morphology for objects detection. Here, methods are evaluated for creation and replication of binary images filters through the use of the genetic programming with the objective of elements identification in an industrial scenery. This work approaches the field of computer vision through the use of artificial intelligence techniques. Researches are accomplished using several techniques for solving those difficulties. Problems of elements identification in industrial sceneries are examples of an application that can generate a larger complexity in the process automation. Such techniques are limited to the computational cost along with its complexity. The techniques of computer vision have been used more and more by the industries in order to aid the automation of their processes however, the implementation of computer vision techniques has several difficulties according with the application.
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