Abstract
In the last few decades the human vision system has been the focus of several researches, using it as a model for solving the object detection problem in digital images. In this work this approach is taken to define the algorithm called Artificial Visual Cortex (AVC) which is inspired in the information flow in the human visual cortex. Additionally, a new methodology for image description is proposed, which allows the detection and description of an object in the scene. Furthermore, this paper describes a new multi-objective learning technique called brain programming. This paradigm is implemented for the training stage of the proposed model in order to classify the persons set of the GRAZ-02 image database. The solutions found in this research outperform other techniques in the state-of-the-art.
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Clemente, E., Olague, G., Hernández, D., L. Briseño, J., Mercado, J. (2015). Object Detection in Natural Images Using the Brain Programming Paradigm with a Multi-objective Approach. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_17
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