Abstract
Computer vision is an emerging area which is demanding solutions for solving different problems. The data to be processed are bi-dimensional (2D) images captured from the tri-dimensional (3D) scene. The objects in 3D are generally composed of related parts that joined form the whole object. Fortunately, the relations in 3D are preserved in 2D. Hence, we can exploit this fact by considering specific and basic elements which are related to other elements in the 2D images. The relations with other elements allow establishing a link among them. Hence, we have the necessary ingredients to build a structure under the Fuzzy Cognitive Maps (FCMs) paradigm. FCMs have been satisfactorily used in several areas of computer vision including: pattern recognition, image change detection or stereo vision matching. In this chapter we establish the general framework of fuzzy cognitive maps in the context of 2D images and describe three applications in the three mentioned areas of computer vision. We also give some details about the performance of this paradigm in these applications.
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Pajares, G., Guijarro, M., Herrera, P.J., Ruz, J.J., de la Cruz, J.M. (2010). Fuzzy Cognitive Maps Applied to Computer Vision Tasks. In: Glykas, M. (eds) Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, vol 247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03220-2_11
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