Sub-region LBP Feature Extraction Reducing Noise and Load
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Solution Overview
Problem
Existing local binary pattern (LBP) techniques face a trade-off between increasing the region size for feature extraction, which doubles the number of bits and thus increases processing load, and maintaining detection accuracy, as wider regions lead to higher dimensionality histograms and increased noise sensitivity.
Innovation Solution
The proposed solution involves setting sub-regions constituted by multiple pixels and calculating representative values for these sub-regions, generating local binary patterns based on differences with the pixel of interest, and using these representative values to reduce the number of bits and noise, thereby allowing more pixels to be used in feature extraction while limiting processing load and accuracy decrease.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a wider region (e.g., 5 pixels × 5 pixels) is targeted for arithmetic operation of local binary pattern, then more pixels can be used for feature extraction, but the number of bits of local binary pattern increases (from 8 to 16), leading to increased processing load and histogram dimensionality
Solution Approach 1:
The patent divides the neighboring region into multiple sub-regions (e.g., four 2×2 sub-regions within a 4×4 region). Instead of computing LBP using all pixels in the entire neighboring region, the patent computes LBP by comparing the pixel of interest with representative values (e.g., average intensity) of each sub-region. This segmentation reduces the number of bits required while still capturing broader spatial information.
Solution Approach 2:
The patent applies different processing strategies to different parts of the image by dividing the neighboring region into sub-regions. Each sub-region is processed independently to generate a representative value, allowing the system to capture local variations while reducing overall computational complexity. This enables selective focus on important local patterns without processing every pixel uniformly.
2Adaptability or versatility
If a wider region is targeted for arithmetic operation, then co-occurrence of features in wider range can be utilized, but the number of dimensions of histogram increases, leading to increased processing load in generating classifier or detecting object
Solution Approach 1:
The patent segments the neighboring region into multiple sub-regions and computes LBP features by comparing the pixel of interest with representative values of these sub-regions. This approach enables the capture of feature co-occurrence over a wider spatial range while maintaining a manageable number of histogram dimensions, thus preserving adaptability without sacrificing productivity.
3Quantity of substance
If the number of bits of local binary pattern increases, then more pixels are used in feature extraction, but the number of dimensions of histogram increases, leading to increased noise sensitivity
Solution Approach 1:
The patent divides the neighboring region into sub-regions and uses representative values (such as average intensity) of these sub-regions for LBP computation. This segmentation approach allows more pixels to be utilized in feature extraction while reducing noise sensitivity by aggregating pixel values within sub-regions before comparison, thereby improving reliability.
Data Source
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AI summary
Provided is a feature extraction device including a sub region setting unit (433) which sets a plurality of sub regions with respect to a pixel of interest, and a binary pattern generation unit (434) which generates a local binary pattern indicating pixel value comparison results relative to each sub region with respect to each pixel of interest. The sub region setting unit (433) sets (436) a region constituted by a plurality of pixels including a pixel separated from the pixel of interest as a sub region, and the binary pattern generation unit (434) calculates (437) a representative value for each sub region and generates (439) a local binary pattern indicating whether or not the difference (438) between the representative value and the value of the pixel of interest is equal to or larger than a predetermined threshold value.