Abstract
A general model for the segmentation and labelling of acquired images in real conditions is proposed. These images could be obtained in adverse environmental conditions, such as faulty illumination, non-homogeneous scale, etc. The system is based on surface identification of the objects in the scene using a database. This database stores features from series of each surface perceived with successive optical parameter values: the collection of each surface perceived at successive distances, and at successive illumination intensities, etc. We propose the use of non-specific descriptors, such as brightness histograms, which could be systematically used in a wide range of real situations and the simplification of database queries by obtaining context information. Self-organizing maps have been used as a basis for the architecture, in several phases of the process. Finally, we show an application of the architecture for labelling scenes obtained in different illumination conditions and an example of a deficiently illuminated outdoor scene.
This work was supported by the CICYT TAP1998-0333-C03-03
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García Chamizo, J.M., Fuster Guilló, A., Azorín López, J., Maciá Pérez, F. (2003). Architecture for Image Labelling in Real Conditions. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_13
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DOI: https://doi.org/10.1007/3-540-36592-3_13
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