Dual-Threshold Segmentation Algorithm

I have developed a custom dual-threshold segmentation algorithm that can be applied to various scenarios. The algorithm operates based on pixel intensity values in images, utilizing two distinct thresholds to partition the image into two segments. This approach is particularly useful in image processing, object detection, and image analysis applications. The algorithm implementation typically involves processing each pixel through threshold comparisons: pixels with intensity values between the lower and upper thresholds are classified into one category, while those outside this range fall into another category. This can be efficiently implemented using conditional statements or vectorized operations in programming languages like Python with OpenCV or MATLAB. Key advantages of the dual-threshold segmentation method include its simplicity, interpretability, and straightforward implementation. The algorithm helps in better understanding image content by enabling effective segmentation and extraction of regions of interest. Through practical implementation, users can adjust threshold values dynamically based on histogram analysis or adaptive thresholding techniques to handle varying lighting conditions. With its robust performance and adaptability, this algorithm demonstrates significant potential for real-world applications in computer vision and image analysis systems.