MATLAB Implementation of Adaptive Thresholding for Image Segmentation

This adaptive thresholding program represents a significant approach to solving challenging problems in the field of image segmentation. The method demonstrates excellent performance by effectively separating foreground objects from background areas with non-uniform illumination through adaptive threshold determination. This approach proves particularly valuable for addressing difficult image segmentation problems, as it can efficiently process images under varying lighting conditions. The implementation typically involves calculating local statistics (such as mean or median values) within sliding windows across the image, then applying region-specific thresholds based on these local characteristics. Key MATLAB functions that may be employed include: - blockproc for processing image blocks - nlfilter for neighborhood operations - local statistical calculations using mean or standard deviation functions By utilizing this adaptive thresholding program, we can achieve superior separation between background and foreground elements, thereby significantly improving the accuracy and effectiveness of image segmentation results. The algorithm adapts to local intensity variations, making it robust against illumination inconsistencies that often challenge traditional global thresholding methods.