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Image Segmentation of Pseudo-foreign Fibers in Cotton on the Basis of Improved Genetic Algorithm

Abstract : In the foreign fibers cleaning process, pseudo-foreign fibers are often mistaken for foreign fibers, this result not only seriously affects the detecting precision of foreign fibers cleaning machine, but also doubles the time of cleaning up lint. As for false identification problem of pseudo-foreign fibers in cotton, this paper proposes a new approach for fast segmentation of pseudo-foreign fibers in cotton on the basis of improved genetic algorithm. This improved genetic algorithm reduced the searching range for calculating optimal threshold from 0~255 to 100~220. The calculating speed in this stage was improved more than twice in average. The fitness amendments formula is also proposed to improve genetic algorithm disadvantage, at the same time, this solved issues of "premature", and converging to global optimal solution difficultly in the traditional algorithm. The results show that the algorithm has high speed, accuracy, anti-interference and so on.
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Lulu Ge, Daoliang Li, Liu Yang, Wenzhu Yang. Image Segmentation of Pseudo-foreign Fibers in Cotton on the Basis of Improved Genetic Algorithm. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.538-548, ⟨10.1007/978-3-642-27278-3_55⟩. ⟨hal-01361027⟩



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