Adaptive Binarization Threshold Calculation via Local Histogram Statistics
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Solution Overview
Problem
Existing image processing techniques face challenges in determining an optimal threshold for binarization, especially in noisy images with varying contrast and intensity, leading to loss of information or artifacts due to sensitivity to threshold values.
Innovation Solution
A method that determines a first threshold through user input or iterative adjustment, calculates statistics from the image histograms, and uses a third-degree polynomial curve to compute a second threshold based on mean, standard deviation, and variance, allowing for adaptive binarization by comparing each pixel to this threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single global threshold is determined for the entire image, then the binarization process is simple and fast, but information loss occurs due to variations in background intensity across the image
Solution Approach 1:
The patent divides the image into multiple local regions and calculates separate thresholds for each region based on local histogram statistics. This segmentation approach allows each region to be binarized according to its own characteristics, preventing information loss due to global intensity variations while maintaining computational efficiency through localized processing.
Solution Approach 2:
The patent applies local adaptive thresholding where each pixel's threshold is determined by the statistical properties (mean, standard deviation, variance) of its local neighborhood. This ensures that regions with different background intensities and noise levels receive appropriate local thresholds, preserving edge information and preventing artifacts while adapting to local image characteristics.
2Object-affected harmful factors
If the threshold is set to be sensitive to noise removal, then noise can be effectively reduced, but edge detection accuracy deteriorates due to loss of meaningful edges
Solution Approach 1:
The patent dynamically adjusts the threshold value based on local image statistics (mean, standard deviation, variance) rather than using a fixed or globally adaptive threshold. This parameter change approach allows the threshold to adapt to local noise levels and intensity variations, effectively removing noise while preserving meaningful edges through statistically-driven local adaptation.
Solution Approach 2:
The patent uses local histogram statistics as feedback to iteratively refine the threshold calculation. By computing mean, standard deviation, and variance from local regions and using these statistics to determine the threshold, the system creates a feedback loop that adapts to local image characteristics, effectively distinguishing noise from meaningful edges based on local statistical patterns.
3Measurement precision
If statistical measures are used to determine local or global threshold, then binarization accuracy improves, but device complexity increases due to multiple calculations
Solution Approach 1:
The patent segments the image into local regions and calculates statistics independently for each region, which reduces the overall computational complexity compared to analyzing the entire image globally. This segmentation allows parallel processing of multiple smaller regions, making the statistical threshold calculation more efficient while maintaining high binarization accuracy through local adaptation.
Solution Approach 2:
The patent employs an automated threshold calculation system that self-adjusts based on local histogram statistics without requiring manual intervention. The system automatically computes mean, standard deviation, and variance for each local region and determines the optimal threshold, eliminating the need for complex manual tuning while achieving high binarization accuracy through self-adaptive statistical analysis.
Data Source
AI summary
A method, computing device, and associated computer readable storage media containing instructions for binarizing a grayscale image by manually determining a first threshold that yields optimal binarization values to one or more images in a set of images, calculating the histograms of each of the images determined using the first threshold, calculating a set of statistical parameters such as the mean, standard deviation and variance of each histogram, determining a second threshold as a function of the set of statistical parameters, and comparing each pixel of the grayscale image to the second threshold. The second threshold T may be a function of the mean m, standard deviation s and variance v and is calculated by fitting a third degree polynomial curve T=a0+a1m+a2s+a3v, where the coefficients A=[a0 a1 a2 a3]T are found using a minimum mean square error algorithm. Grayscale values above the second threshold are assigned a first binarization value and grayscale values below the second threshold are assigned a second binarization value.