Image Matching Using Unique Region Brightness to Reduce Gibbs Noise
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Solution Overview
Problem
The amplitude correlation based image matching method faces challenges in maintaining accuracy due to noise generation from the Gibbs phenomenon when replacing brightness values in common regions, leading to decreased matching scores and reduced individual identification accuracy.
Innovation Solution
An image matching apparatus and method that specifies common regions between images, replaces brightness values in these regions based on unique region pixels to minimize noise, and performs matching using frequency characteristics of the replaced images, thereby reducing the impact of noise on amplitude components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If brightness values in common regions are replaced with fixed values to eliminate correlation in common region, then matching speed is improved, but noise is generated due to Gibbs phenomenon causing matching score decrease
Solution Approach 1:
The patent changes the parameter used for replacement from fixed values to values calculated based on unique region characteristics. Specifically, it replaces brightness values in common regions with values derived from the unique regions of the same image, thereby maintaining continuity and avoiding the Gibbs phenomenon while preserving the speed advantage of the amplitude correlation method
Solution Approach 2:
The patent introduces unique region characteristics as an intermediary to mediate the replacement process. Instead of directly using fixed values that cause discontinuity, it uses characteristics from unique regions (which contain individual differences) as the basis for generating replacement values, thus eliminating noise while maintaining efficiency
2Reliability
If masking process is performed by replacing brightness values with fixed values, then common region correlation is eliminated, but discontinuity is generated at region boundaries causing Gibbs phenomenon
Solution Approach 1:
The patent changes the replacement parameter from fixed values to dynamically calculated values based on unique region statistics (mean or median brightness values). This ensures that replacement values are consistent with the overall image characteristics, eliminating boundary discontinuity and the resulting Gibbs phenomenon while maintaining reliable matching accuracy
Solution Approach 2:
The patent applies different replacement strategies to different regions: common regions are replaced with values derived from their own unique regions, preserving local characteristics and avoiding global discontinuity. This local adaptation eliminates the harmful Gibbs phenomenon while maintaining the ability to distinguish individual differences
Data Source
AI summary
An image matching apparatus according to the present invention includes: a common region specification unit configured to specify a common region between a first image and a second image; a date replacement unit configured to generate a first replaced image in which a brightness value of the common region of the first image is replaced based on a pixel in the first image, and a second replaced image in which a brightness value of the common region of the second image is replaced based on a pixel in the second image; and a matching unit configured to perform matching between the first image and the second image based on frequency characteristics of the first replaced image and the second replaced image.