Electronic Image Comparison for Material Difference Detection
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Solution Overview
Problem
Current image comparison methods fail to account for perceptually similar images that have minor differences, leading to incorrect evaluation of large differences, and do not consider a variety of types of alterations between images, emphasizing minute differences that may not materially affect the image's substantive information or readability.
Innovation Solution
A system and method for comparing images by calculating a logical match percentage, highlighting large differences while negating small ones, using weighted calculations and converting images to grayscale for rapid comparison, and analyzing unmatched areas in matrices to determine significant differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If raw data processing is used to compare images by measuring pixel differences, then the comparison is simple and fast, but it incorrectly evaluates perceptually similar images as very different due to minor object repositioning
Solution Approach 1:
The patent segments the image comparison process into multiple analysis dimensions including object detection, text recognition, and graphical element identification. By dividing the image into meaningful components rather than comparing raw pixels, the system can evaluate perceptual similarity more accurately while maintaining computational efficiency through targeted analysis of specific regions and elements.
2Difficulty of detecting and measuring
If edge detection, corner detection, or blob detection methods are used to identify specific differences, then those specific difference types can be detected, but a variety of other alteration types are missed and minute differences are overemphasized
Solution Approach 1:
The patent implements a universal image comparison framework that integrates multiple detection methodologies including object detection, text recognition, graphical element analysis, and layout comparison. This multi-functional approach enables the system to detect various types of alterations simultaneously while weighing them appropriately, preventing overemphasis of minute differences and capturing a comprehensive view of image similarities and differences.
3Device complexity
If all pixel differences are counted to determine image similarity, then the calculation is straightforward, but perceptually slight differences result in large numbers of altered pixels making images appear very different
Solution Approach 1:
The patent applies local quality assessment by evaluating differences in semantically meaningful regions rather than uniformly across all pixels. The system identifies key regions containing objects, text, and graphical elements, then weights differences in these regions according to their perceptual importance. This approach preserves perceptual similarity information while maintaining computational tractability through focused analysis of critical image regions.
Data Source
AI summary
Methods, system, and media for comparing a set of images to determine the existence and location of any differences between the image set. The differences may be located using image comparison techniques such as SURF and Blob Detection, as well as through techniques used to identify areas of data sliding and match probabilities. A logical match probability, as well as a physical match probability, may be included in an output report with a result image highlighting the differences between the comparison images in the image set.