Image Processing System Skeleton Extraction Noise Correction
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Solution Overview
Problem
Conventional image comparison methods are ineffective when dealing with images of poor quality, such as those scanned, due to noise points that register as content differences, leading to reduced image quality.
Innovation Solution
An image processing system that includes modules for image selection, conversion, skeleton extraction, covering, and variance correction, which converts color images to monochromic, extracts skeletons, and corrects allowable variances by comparing and adjusting pixel values to maintain central values and delete noise points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pixel-by-pixel comparison methods are used, then the comparison process is simple and fast, but the accuracy is poor when noise points are present
Solution Approach 1:
The patent segments the image comparison process into multiple stages: skeleton extraction to identify structural elements, variance identification to locate differences, and noise point detection to filter false positives. This segmentation allows the system to focus computational resources on structurally significant areas rather than comparing every pixel, thereby improving accuracy without proportionally increasing overall complexity
Solution Approach 2:
The patent introduces an intermediary verification mechanism that acts as a mediator between raw pixel comparison and final match determination. This intermediary layer analyzes the context of detected variances, cross-references them with skeleton structures, and applies noise filtering algorithms to validate whether detected differences represent actual content variations or scanning artifacts, thus resolving the accuracy-complexity contradiction
2Reliability
If noise points are treated as content differences, then all pixel variations are captured, but false positives increase and reliability decreases
Solution Approach 1:
The patent converts the harmful effect of noise points into a beneficial filtering process. By deliberately designing the system to detect and analyze variance points, it then uses the structural information from skeleton extraction to distinguish between meaningful content differences and noise artifacts. The variance correction module leverages the presence of detected variances to apply intelligent filtering, transforming the problem of noise detection into an opportunity for improved reliability
Solution Approach 2:
The patent implements a feedback mechanism where the results of variance identification feed into the noise point detection process, which in turn refines the final comparison results. The system continuously refines its understanding of what constitutes a true difference versus noise by analyzing the relationship between detected variances and skeleton structures, creating a self-correcting process that improves reliability while maintaining information integrity
Data Source
AI summary
An image processing system and method for comparing and correcting two monochromic images A2 and B2 that extracts a skeleton of objects in the monochromic image A2 to generate a skeleton image A3 and extracts a skeleton of objects in the monochromic image B2 to generate a skeleton image B3. The system and method then covers the skeleton image A3 with the monochromic image B2 to generate a covered image A4, and covers the skeleton image B3 with the monochromic image A2 to generate a covered image B4. The system and method further corrects allowable variances in the covered images A4 and B4, and outputs the covered images whose variances have been corrected.