Image Classifier Sensitivity Measurement via Intermediate Image Generation
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Solution Overview
Problem
Mass-produced product quality control using image classifiers faces challenges in accurately distinguishing between 'OK' and 'not OK' products due to sensitivity issues related to changes in input images, such as noise and lighting conditions, leading to incorrect classifications and potential rejection of flawless products or failure to detect defects.
Innovation Solution
A method involving the creation of intermediate images with reduced information content or poorer signal-to-noise ratios, generated by trained generators, to increase classification uncertainty, allowing for small interventions to change class assignments while maintaining realistic images, and using these modifications to evaluate and improve the sensitivity of image classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the image classifier is trained to be robust against changes in input image, then the reliability of classification is improved, but the sensitivity to detect actual defects deteriorates
Solution Approach 1:
The patent applies partial action by introducing intermediate images with partial degradation (reduced information content or added noise) rather than complete transformation. This allows the classifier to learn robustness to specific types of perturbations while maintaining sensitivity to actual defects. The degradation is controlled and partial, enabling the system to distinguish between meaningful defects and benign variations.
2Reliability
If the information content of the input image is reduced to increase robustness, then the reliability against noise is improved, but the measurement precision of defect detection worsens
Solution Approach 1:
The patent applies preliminary action by pre-processing input images to create intermediate representations with reduced information content or added noise before classification. This preliminary transformation allows the classifier to learn invariant features that are robust to noise and lighting variations, while still preserving the essential defect characteristics needed for accurate detection.
3Measurement precision
If the image classifier is made highly sensitive to changes, then the detection precision is improved, but the reliability increases false rejections worsens
Solution Approach 1:
The patent introduces intermediate images as mediators between the original input and the classifier decision. These intermediate representations serve as a buffer that filters out benign variations while preserving defect signals. The classifier operates on these stabilized intermediate representations, which reduces false rejections while maintaining detection precision through the selective preservation of important features.
Data Source
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AI summary
Method (100) for measuring the sensitivity (2*) of an image classifier (2), which assigns an input image (1) to one or more classes (3a-3c) of a given classification, against changes to the input image (1), comprising the steps: • the input image (1) is mapped by at least one given operator (4) to an intermediate image (5) (110) which has a lower information content and/or a worse signal-to-noise ratio compared to the input image (1); • at least one generator (6) is provided (120) which is trained to produce realistic images that are assigned by the image classifier (2) to a specific class (3a-3c) of the given classification; with this generator (6) a modification (7) of the input image (1) is generated from the intermediate image (5) (130).