Biological Pattern Separation via Spatial Domain Normalization
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Solution Overview
Problem
Existing technologies for separating biological patterns, such as vein and fingerprint patterns, are inefficient due to high computational load and susceptibility to noise, leading to inaccurate separations.
Innovation Solution
An image processing apparatus that normalizes pixel values based on average and standard deviation within local regions to separate biological patterns with different textures, using a combination of Gaussian filters and gray level normalization, and collates known patterns with the separated images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Fourier transform is used to separate biological patterns, then separation capability is improved, but computational load increases heavily
Solution Approach 1:
The patent transforms the separation approach from frequency domain (Fourier transform) to spatial domain operations using Gaussian filtering and gray level normalization. By changing the operational parameters and domain of processing, the system achieves pattern separation with significantly reduced computational requirements while maintaining effectiveness in separating vein patterns from fingerprint/wrinkle patterns
2Speed
If conventional separation technology is used, then processing speed is improved, but noise resistance deteriorates
Solution Approach 1:
The patent applies Gaussian filtering as a preliminary action before pattern separation to suppress noise in the captured image. This pre-processing step removes noise components while preserving the essential pattern information, thereby improving noise resistance without significantly impacting processing speed. The filtering operation is optimized to balance noise suppression with computational efficiency
3Measurement precision
If Fourier transform is applied, then separation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces the complex Fourier transform mechanical system with simpler spatial domain operations including Gaussian filtering and gray level normalization. This substitution maintains separation accuracy by operating directly on the image pixel values in the spatial domain, avoiding the computational complexity of frequency domain transformations while achieving comparable or superior separation results
Data Source
AI summary
[Problem] To separate biological patterns more efficiently and effectively.[Solution] An image processing apparatus according to the present invention is characterized by comprising: an image obtaining means for obtaining an image in which a first biological pattern and a second biological pattern having different textures are superimposed; a normalizing means for normalizing density of the image on the basis of an average and a standard deviation of pixel values inside a local region in the image using a parameter in accordance with difference in texture between the first biological pattern and the second biological pattern to thereby separate the first biological pattern and the second biological pattern in the image; and a pattern collating means for collating a known pattern for collation with each of the first biological pattern and the second biological pattern separated by the normalizing means.