Image Segmentation Implementation

In this context, image segmentation refers to the technique and process of dividing an image into specific regions possessing unique characteristics and extracting target areas of interest. It constitutes a crucial transition step from image processing to image analysis. Current image segmentation methodologies are mainly categorized into threshold-based approaches (using techniques like Otsu's method for automatic threshold determination), region-based methods (employing algorithms such as region growing and watershed transformation), edge detection-based techniques (utilizing operators like Sobel, Canny, or Laplacian), and theory-specific segmentation methods (incorporating concepts like fuzzy logic, neural networks, or deep learning architectures). Since 1998, researchers have consistently enhanced traditional segmentation approaches while integrating novel theories and methodologies from other scientific disciplines, resulting in numerous advanced segmentation techniques. Targets extracted through image segmentation enable applications across multiple domains including image semantic recognition (using convolutional neural networks for object classification), image search systems (implementing content-based image retrieval algorithms), and various computer vision applications, substantially expanding their practical implementation scope.