MATLAB Implementation of Adaptive Thresholding Algorithm

Adaptive thresholding is an image segmentation method that dynamically adjusts thresholds based on local image characteristics, particularly effective for uneven lighting conditions. The core concept of MATLAB implementation includes: Fundamental Principles Adaptive thresholding analyzes grayscale characteristics within pixel neighborhoods (e.g., 15×15 windows) to compute local thresholds. Common approaches include: - Local Mean Method: Threshold = Window Mean - C (constant) - Gaussian Weighted Method: Using Gaussian kernel weighting for local threshold calculation Algorithm Workflow 1. Divide the image into multiple sub-regions 2. Compute statistical measures (mean/median) for each region 3. Generate threshold matrix based on statistics and offset values 4. Perform pixel-wise comparison between original image and threshold matrix for binarization Implementation Key Points - Utilize `im2col` function for image blocking to enhance processing efficiency - Employ `colfilt` or `nlfilter` for sliding window computations - Parameter tuning: Window size affects detail preservation, while offset C controls sensitivity Usage Example Adaptive threshold functions are typically designed to accept original image, window size, and offset parameters, returning a binary image. Standard implementation requires: - Grayscale conversion of input images - Selection of odd-numbered window sizes (e.g., 15 or 31) - Positive offset values (commonly 5-15) This algorithm overcomes the limitations of global thresholding under uneven illumination, demonstrating significant effectiveness in document scanning and medical image analysis. Practical applications require balancing window size and computational efficiency to prevent over-segmentation or edge blurring. Code Implementation Enhancement: The MATLAB implementation typically involves creating a function that processes image blocks using vectorized operations. Key functions include: - `im2col` for efficient block processing by rearranging image blocks into columns - `mean()` or `medfilt2()` for local statistics calculation - Logical comparison operators for final binarization step Optimal performance is achieved by preallocating memory for threshold matrix and using neighborhood operations instead of nested loops.