Image Segmentation Using SVM with OTSU Thresholding Method
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Resource Overview
Image segmentation using Support Vector Machine (SVM) with OTSU thresholding produces excellent results, directly generating binary images with enhanced feature extraction and classification capabilities
Detailed Documentation
This paper presents an image segmentation approach using Support Vector Machine (SVM) combined with the OTSU thresholding method. The implementation involves feature extraction from image regions followed by SVM classification to distinguish between different segments. The OTSU algorithm automatically determines the optimal threshold value by maximizing inter-class variance, which effectively converts the classified output into a clean binary image. This method demonstrates superior performance in producing clear, intuitive binary segmentation results through machine learning-based pixel classification.
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