Video Image Binarization Using Enhanced Otsu's Maximum Inter-Class Variance Method

In this research, we employ an enhanced version of Otsu's algorithm, commonly known as the maximum inter-class variance method, for video image binarization. This algorithm's key advantage lies in its adaptive threshold determination capability, where the optimal threshold is calculated automatically based on image histogram analysis. The implementation typically involves computing between-class variance for all possible threshold values and selecting the one that maximizes separability between foreground and background classes. Furthermore, the improved algorithm incorporates regional processing functionality, allowing differential treatment of distinct image areas through segmentation techniques. This enhanced approach has found widespread applications across multiple domains including medical image processing (e.g., tissue segmentation in MRI scans), industrial image analysis (defect detection in manufacturing), and computer vision systems (object recognition frameworks). From a code implementation perspective, the core algorithm involves histogram calculation, probability distribution analysis, and iterative variance computation. Key functions would include: - calculate_histogram() for pixel intensity distribution - compute_class_variance() for separation metric evaluation - adaptive_threshold() for region-wise processing In future research, we will explore further optimization strategies such as parallel processing for real-time video applications, dynamic threshold adjustment for varying lighting conditions, and machine learning integration for enhanced segmentation accuracy, aiming to broaden its practical implementation across diverse real-world scenarios.