Dynamic Threshold Adjustment for Image Similarity Determination
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Solution Overview
Problem
Existing image processing methods face challenges in accurately determining image similarity due to a small number of feature points, leading to degraded accuracy and susceptibility to noise, especially when the number of feature points is low.
Innovation Solution
An image processing method that identifies pixel regions with adjacent pixels of the same value, sets threshold values to determine the number of feature points, and adjusts these values to ensure a sufficient number of feature points for accurate similarity determination, while also dividing the document image into regions to set appropriate threshold values and change the region from which feature points are extracted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centroids are used as feature points to achieve high-accuracy image similarity determination, then noise susceptibility is reduced and stability is improved, but the number of feature points becomes small causing accuracy degradation
Solution Approach 1:
The patent dynamically adjusts the threshold value for determining pixel regions based on the number of extracted feature points. When the number of feature points is small, the threshold is lowered to include more pixel regions and increase the number of feature points. When the number of feature points is sufficient, the threshold is raised to maintain high-accuracy determination. This parameter adjustment resolves the contradiction by adapting the system behavior to the current state.
Solution Approach 2:
The patent implements a dynamic threshold adjustment mechanism where the threshold value changes based on the number of extracted feature points. The system transitions from a static threshold to a dynamic one that adapts to the input image characteristics, allowing the system to optimize between noise filtering and feature point sufficiency in real-time.
2Device complexity
If a fixed threshold value is used to identify pixel regions, then processing simplicity is maintained, but accuracy degrades when the number of feature points is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors the number of extracted feature points and adjusts the threshold value accordingly. This closed-loop control ensures that the threshold adapts to the actual number of feature points extracted, maintaining high accuracy while keeping the base processing logic simple and straightforward.
Data Source
AI summary
The number of pixels in an identified pixel region is counted, a feature point of the pixel region is extracted and the number of the feature points is counted when the number of the pixels counted has been determined to be equal to or higher than a first threshold value, whether the counted number of the feature points is equal to or lower than a second threshold value is determined, features is calculated based on the feature point extracted from the pixel region when the number of the feature points has been determined to be above the second threshold value, and the first threshold value is changed when the number of the feature points has been determined to be equal to or lower than the second threshold value. Image similarity determination process can be stably performed without any degradation in determination accuracy.