Image Processing Apparatus False Anomaly Exclusion
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Solution Overview
Problem
Existing image processing systems often detect false abnormalities in print products, leading to over-detection and increased calculation costs due to unnecessary post-processing for non-existent issues.
Innovation Solution
An image processing apparatus with an abnormality detection unit and an exclusion processing unit that identifies overlapping detection areas of different types of abnormalities and excludes them based on specific brightness conditions, reducing false positives and associated processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If abnormality detection is performed to identify issues in print products, then detection capability is improved, but false abnormalities are detected leading to increased calculation costs
Solution Approach 1:
The patent applies preliminary action by performing brightness information extraction and overlap determination before the main post-processing stage. The system pre-identifies potential false abnormalities by checking brightness conditions and detection area overlaps, thereby filtering out false positives before they incur full post-processing costs.
Solution Approach 2:
The patent extracts and separates the brightness information processing from the main abnormality detection flow. By extracting brightness data and using it as a filtering criterion, the system isolates false abnormalities based on their brightness characteristics, allowing them to be excluded from further processing.
2Reliability
If detection sensitivity is increased to detect all abnormalities, then detection coverage is improved, but over-detection occurs with non-abnormal images
Solution Approach 1:
The patent applies local quality by examining specific local characteristics of detected abnormalities - specifically their brightness information and spatial relationships. Instead of treating all detections uniformly, the system analyzes local brightness properties to distinguish true abnormalities from false ones, excluding only those that fail the brightness criterion.
Solution Approach 2:
The patent introduces brightness information as an intermediary criterion between the detection stage and the post-processing stage. This intermediary brightness check acts as a filter that mediates between sensitive detection and false positive reduction, allowing the system to maintain high detection sensitivity while eliminating obvious false abnormalities.
3Productivity
If post-stage processing is performed for all detected abnormalities, then analysis completeness is improved, but calculation resources are wasted on false abnormalities
Solution Approach 1:
The patent performs preliminary filtering action before the main post-processing pipeline. By checking brightness conditions and detection area overlaps in advance, the system pre-identifies which abnormalities should be excluded from post-processing, thereby reducing calculation resource consumption on false positives while maintaining complete analysis of true abnormalities.
Solution Approach 2:
The patent applies partial action by performing post-processing only on a subset of detected abnormalities that pass the brightness filter, rather than processing all detections. This partial processing approach reduces unnecessary calculation resources while maintaining sufficient analysis coverage for genuine abnormalities.
Data Source
AI summary
The image processing apparatus includes: an abnormality detection unit configured to detect one or more abnormalities included in a target image and an abnormality exclusion processing unit configured to exclude a specific abnormality from the detected one or more abnormalities. In a case in which a detection area of a certain abnormality among the detected one or more abnormalities and a detection area of another abnormality overlap with each other and a type of the certain abnormality and a type of the another abnormality are different from each other, when brightness information of one of the certain abnormality and the another abnormality satisfies a specific condition, the abnormality exclusion processing unit excludes the one of the certain abnormality and the another abnormality from the detected one or more abnormalities.