Image Analysis Computing Device for Object Change Assessment
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Solution Overview
Problem
Current image analysis methods for assessing object changes are inaccurate due to variations in image acquisition conditions and material properties, leading to inconsistencies in comparing real-time images with older images.
Innovation Solution
An image analysis computing device and method that accounts for differences in image acquisition by using machine learning to classify and identify changes in objects made of various materials, employing techniques like keypoint detection and descriptor matching to improve accuracy and robustness against image position, alignment, and material variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image comparison methods are used to assess object changes, then the process is simple and fast, but the accuracy is poor due to variations in image acquisition conditions and material properties
Solution Approach 1:
The patent segments the image comparison process into multiple stages: initial comparison to identify potential changes, material classification to determine material type, and secondary comparison using material-appropriate metrics. This segmentation allows the system to handle different materials with appropriate methods, improving accuracy without overwhelming complexity at any single stage
Solution Approach 2:
The patent changes the comparison parameters based on material type. For metallic objects, it uses metrics sensitive to metallic surface changes; for non-metallic objects, it uses different metrics appropriate for those materials. This dynamic parameter adjustment resolves the contradiction by adapting the measurement approach to the specific material being examined
2Measurement precision
If traditional image comparison is performed without accounting for material properties, then the processing is quick and simple, but the results are inaccurate due to different mechanical behaviors of materials
Solution Approach 1:
The patent performs preliminary material classification before executing the detailed change detection process. By identifying the material type early in the workflow, the system can select the appropriate comparison metrics and algorithms in advance, avoiding the need for time-consuming trial-and-error approaches and enabling accurate material-specific analysis
3Measurement precision
If image position, alignment, and orientation variations are not corrected, then the comparison process is faster, but the assessment accuracy deteriorates
Solution Approach 1:
The patent extracts and corrects position, alignment, and orientation variations as separate preprocessing steps before performing the actual change detection. By isolating these geometric corrections from the main comparison task, the system efficiently handles transformations without compromising the core analysis speed or accuracy
Data Source
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AI summary
A method, non-transitory computer readable medium, and an image analysis computing device (14) that retrieves (302), based on a captured version of an object in a received image, training images which display related versions of the object and items of data related to the related versions of the object of the training images. Keypoints which are invariant to changes in scale and rotation in the captured version of the object in the received image and in the related versions of the object in the training images are determined (306). Changes to the object in the received image based on any of the determined keypoints in the related version of the object which do not match the determined keypoints in the captured version of the object are identified (316). The identified changes in the captured version of the object in the received image are provided (324).