Enhancement of MAD Similarity Measurement Criterion

MAD (Mean Absolute Difference) is a widely used similarity measurement method primarily employed in template matching tasks. Its core principle involves calculating the average of absolute pixel differences between corresponding regions of a template image and a target image, where smaller values indicate higher similarity. Implementation typically involves sliding the template over the target image and computing pixel-wise differences using matrix operations.

However, the traditional MAD criterion presents limitations under certain conditions. For instance, when illumination conditions vary or noise interference exists, direct pixel difference calculations may lead to inaccurate matching results. To address these challenges, the following optimization approaches can be considered:

Normalization Processing: Perform image normalization to mitigate the effects of illumination changes. This can be implemented by calculating local region means and standard deviations, then adjusting pixel values using these statistics through z-score normalization: normalized_pixel = (pixel - mean) / std_dev.

Weighted MAD: Different image regions may contribute unequally to matching results. A weighting mechanism can be introduced, such as assigning higher weights to central template regions using Gaussian weighting functions, and lower weights to edge areas, thereby improving matching precision.

Multi-scale Matching: Global matching on large-scale images can be computationally intensive. A pyramid hierarchy strategy can be employed, implementing coarse matching at smaller scales first using image downsampling, then progressively refining search ranges through upscaling operations to enhance computational efficiency.

Edge Information Integration: Sole reliance on pixel values makes the method susceptible to noise interference. Combining edge detection algorithms (such as Sobel or Canny operators) to compute edge structure similarity can enhance matching stability through gradient-based feature extraction.

Adaptive Thresholding: MAD typically requires a fixed threshold to determine matching success, but optimal thresholds may vary across different scenarios. Adaptive methods can be implemented using sliding window statistics to analyze local variations and dynamically adjust matching criteria based on real-time difference distributions.

Through these enhancements, the MAD similarity measurement criterion can demonstrate superior performance in template matching tasks and adapt to more complex real-world application scenarios.