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Comparative Study of Morphological, Correlation, Hybrid and DCSFPSS based Morphological & Tribrid Algorithms for GFDD

Comparative Study of Morphological, Correlation, Hybrid and DCSFPSS based Morphological & Tribrid Algorithms for GFDD

V. Jayashree, S. Subbaraman

Journal Title:ACEEE International Journal on Signal And Image Processing
Abstract


This paper proposes comparative study of two basic approaches such as Morphological Approach (MA) and Correlation Approach (CA) and three modified algorithms over the basic approaches for detection of micronatured defects occurring in plain weave fabrics. A Hybrid of CA followed by MA was developed and has shown to overcome the drawbacks of the basic methods. As automation of MA using DC Suppressed Fourier Power Spectrum Sum (DCSFPSS), DCSFPSSMA could not yield improvement in Overall Detection Accuracy (ODA) for micronatured defects, automation of modified Hybrid Approach (HA) was proposed leading to the development of Tribrid Approach (TA). Modified Hybrid approach involves cascade operation of CA and MA both automated using DCSFPSS. Texture periodicity of defect free fabric was obtained using DCSFPSS which was extended for the design and extraction of defect independent template for CA and for the design of the size of structuring element for morphological filtering process. Overall Detection Accuracy was used by adopting simple binary based defect search algorithm as the last step in the experimentation to detect the defects. Overall Detection Accuracy was found to be ~100%/97.41%/ 98.7 % for 247 samples of warp break defect/ double pick/ normal samples and 96.1% /99% for 205 thick place defect samples/normal samples belonging to two different plain grey fabric classes. Robustness of the performance of TA scheme was tested by comparing TA with two traditional algorithms viz., CA and MA and our previously proposed hybrid algorithm and DCSFPSSMA. This TA algorithm outperformed when compared to CA-only, MA-only, HA and DCSFPSSMA by yielding an overall ODA of more than 98% for the defect and defect free samples of different fabric classes. Secondly, the recognition of defect area less than 1 mm2 which has not been reported in the literature yet, was possible using this algorithm. We propose to use this method as a means to grade the grey fabric similar to the standard fabric grading system.

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