Abstract
This paper presents a technique for color image segmentation, product of the combination and improvement of a number of traditional approaches: Seed region growth, Threshold classification and level on detail in the analysis of demand. First, a set of precise color classes with variable threshold is defined based on sample data. A scanline algorithn uses color clases with a small threshold to extract an initial group of pixels. These pixels are passed to a region growth method, which performs segmentation using higher-threshold classes as homogeneity criterion to stop growth. This hybrid technique solves disadvantages from individual methods and keeps their strengths. Its advantages include a higher robustness to external noise and variable illumination, efficiency on image processing, and quality on region segmentation, outperforming the results of standalone implementations of individual techniques. In addition, the proposed approach sets a starting point for further improvements.
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Álvarez, R., Millán, E., Swain-Oropeza, R. (2005). Multilevel Seed Region Growth Segmentation. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_36
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DOI: https://doi.org/10.1007/11579427_36
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