A COMPARATIVE ANALYSIS OF BASELINE AND CONTOUR-BASED IMAGE SEGMENTATION METHODS (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2024)
Image segmentation is one of the important components of computer vision and is widely used for automatic object detection and classification. This paper focuses on the analysis of basic and contour-based segmentation methods used in image recognition. The results of basic segmentation methods, including thresholding, k-means clustering, and other techniques, and contour-based methods, such as Canny and Sobel filters, were compared. During the study, the effectiveness, accuracy level, and computational efficiency of each method were analyzed, and their advantages and disadvantages were determined. Experiments have shown which aspects of each method are more effective for different types of images. The results of the study can be used to choose the optimal method for image segmentation. Recommendations for further development of these methods in real-time systems and medical image processing are given in the future.
Keywords: Image segmentation; Edge detection; Contour-based methods; Thresholding; Sobel operator; Canny edge detection; K-means clustering