Adaptive Thresholding Algorithm for Irregular Illumination Conditions

In the given problem, we need to develop an adaptive thresholding algorithm that can effectively extract foreground images from backgrounds with irregular illumination conditions. To achieve this objective, we will leverage advanced image processing techniques and algorithms that analyze and process images to determine appropriate thresholds for distinguishing foreground from background. This adaptive algorithm employs local region analysis rather than global thresholding, typically using methods like Gaussian-weighted moving averages or local statistical calculations (mean/standard deviation) within sliding windows. The implementation involves scanning the image pixel by pixel while dynamically adjusting thresholds based on surrounding pixel intensity distributions. Key functions may include compute_local_mean() for neighborhood analysis and adaptive_threshold_calculation() that considers illumination variations. This adaptive thresholding approach can accommodate different lighting conditions and demonstrates strong robustness and accuracy. Through this methodology, we can better handle complex image scenarios and effectively extract the foreground information we need to focus on.