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An Adaptive Bilateral Filter for Noise Removal and Novel Hybrid Fuzzy Cognitive Map-FNN Edge Detection Method for Images


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DOI: https://doi.org/10.15866/irecos.v9i9.3536

Abstract


The important process in image processing is edge detection and it is considered as the basis for the pattern recognition owing to its effect on the subsequent processes of image processing. Edge detection is highly needed for processing of noisy images as noise is the main shortcoming of the images. There are many optimization algorithms such as SVM, WSVM classifiers so far reported for performing edge detection. Inspire of rendering better results, these existing methods suffer from lack of transparency thus affecting the overall performance of the system. This paper presents a novel method for edge detection which operates on the hybrid fuzzy cognitive map based fuzzy neural network (HFCM-FNN). This method employs a facile way of processing images using HFCM-FNN by reducing the execution time and upgrading the visual quality. The experimentation results shows the overall performance results of the proposed HFCM-FNN edge detection method and it can be compared with existing SVM, WSVM methods.
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Keywords


Edge Detection; Weighted SVM (WSVM); Fuzzy Cognitive Map (FCM); Hybrid Fuzzy Cognitive Map Based Fuzzy Neural Network (HFCM-FNN); Adaptive Bilateral Filtering (ABF); Noise Removal

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References


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