Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition |
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Authors: | Omid Khayat Hossein Afarideh |
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Affiliation: | 1. Nuclear Engineering and Physics Department , Amirkabir University of Technology , 424 Hafez Ave, Tehran , 15875-4413 , Iran khayat@aut.ac.ir;3. Nuclear Engineering and Physics Department , Amirkabir University of Technology , 424 Hafez Ave, Tehran , 15875-4413 , Iran |
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Abstract: | Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods. |
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Keywords: | feature extraction texture-based feature shape-based feature neuro-fuzzy classifier nuclear track recognition |
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