Mean Shift Threshold Segmentation

This implementation features MATLAB code for threshold segmentation based on the Mean Shift algorithm. The process involves two main computational stages: initially, the Mean Shift algorithm performs clustering by iteratively shifting points toward the mode of their local density distribution, effectively grouping pixels with similar grayscale characteristics. This clustering mechanism utilizes kernel density estimation to identify natural groupings in the pixel intensity space. Following this clustering phase, threshold segmentation is applied to the clustered results, where an optimal threshold value is determined either automatically through methods like Otsu's algorithm or manually specified to distinguish foreground objects from the background. This segmentation approach effectively extracts regions of interest from images while separating them from the background, resulting in clearer images that are more amenable to analysis. The algorithm demonstrates particular strength in handling images with gradual intensity transitions and complex backgrounds. The method finds applications in various image processing tasks including object detection, image segmentation, and image enhancement. Within computer vision applications, this technique has proven particularly valuable for medical image analysis, surveillance systems, and industrial inspection where precise foreground-background separation is crucial. The MATLAB implementation typically involves key functions such as meanshift for pixel clustering, graythresh for automatic threshold calculation, and imbinarize for final binary segmentation. The code structure generally includes parameters for bandwidth selection in Mean Shift clustering and threshold adjustment options to accommodate different image characteristics.