Object Annotation Correction in Image Data Analysis
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Solution Overview
Problem
Existing methods for quality control of object markings in images do not allow for automated quantification of deviations from ideal positions or sizes, preventing automatic correction of object markings and analysis of error structures in image data sets.
Innovation Solution
A method and analysis unit that read image data to detect and correct object markings by determining distances between marking sections and object parts, using threshold values to assess correctness and output signals for incorrect definitions, allowing for automatic adjustment of object markings to ideal positions and sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quality control of object markings is performed, then accuracy of marking evaluation can be maintained, but productivity and automation level are reduced
Solution Approach 1:
The system performs self-validation by automatically detecting objects and their parts, then verifying whether object markers correctly encompass them. The analysis unit independently evaluates marking accuracy without requiring manual intervention, achieving both high accuracy and automated operation.
Solution Approach 2:
The system generates feedback signals indicating whether object markers are correctly defined based on automated detection results. This feedback mechanism enables continuous quality control and automatic correction of marking errors, maintaining accuracy while enabling high-speed automated processing.
2Productivity
If automated object marking correction is implemented, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The analysis method is divided into distinct functional modules: object detection, object part detection, distance calculation, threshold comparison, and signal generation. This segmentation allows each module to be independently optimized and maintained, reducing overall system complexity while enabling automated correction.
Solution Approach 2:
The analysis unit acts as an intermediary between image data and quality control decisions. It receives image data, performs automated analysis, and outputs correction signals, simplifying the overall system architecture by centralizing the complex analysis functions in a dedicated component.
3Measurement precision
If detailed distance measurements and threshold comparisons are performed, then measurement precision of marking deviations is improved, but loss of time in processing increases
Solution Approach 1:
The system performs distance measurements and threshold comparisons only for critical object parts that require precise marking validation. By focusing computational resources on essential measurements rather than exhaustive analysis of all image elements, the system achieves high precision for marking deviations while minimizing processing time.
Data Source
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AI summary
Disclosed is an analysis method for checking at least one object label in image data, wherein: the analysis method reads data of at least one image from a memory; the image displays a pre-defined object; a pre-defined object portion of the pre-defined object is specified and an object label that encloses the pre-defined object is defined for said pre-defined object; the analysis method detects the pre-defined object without an object portion in the data of the image and if the analysis method does not detect the pre-defined object without an object portion in the image, issues a first signal indicating that the object label is incorrectly defined.