Component Orientation Data Creation via Difference Image Analysis
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Solution Overview
Problem
Conventional component orientation determination data creation is difficult and time-consuming, requiring experienced operators and involving repeated trial and error, especially for inexperienced operators.
Innovation Solution
Calculating difference images between component images in correct and alternate orientations to identify regions with significant brightness differences, optimizing imaging conditions, and including these in the component orientation determination data to facilitate easy and quick data creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If component orientation determination data is created by trial and error by an experienced operator, then the orientation determination accuracy is improved, but the time required for data creation increases and the ease of operation deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically capturing component images in multiple orientations, calculating difference images, and identifying determination regions before the operator needs to create orientation determination data. This preliminary automated processing eliminates the need for time-consuming trial and error by experienced operators while maintaining high orientation determination accuracy.
Solution Approach 2:
The manual trial-and-error process performed by experienced operators is replaced by an automated image processing system that captures images, calculates difference images, and identifies determination regions algorithmically. This substitution of mechanical/manual operations with automated computing operations significantly reduces data creation time while preserving orientation determination accuracy.
2Ease of operation
If component orientation determination data is created by trial and error by an inexperienced operator, then the ease of operation is improved (anyone can do it), but the orientation determination accuracy deteriorates
Solution Approach 1:
The system performs self-service by automatically executing the entire data creation process including image capture, difference image calculation, and determination region identification without requiring operator expertise. The automated algorithms independently identify optimal determination regions and parameters, enabling inexperienced operators to create accurate orientation determination data without manual trial and error.
Solution Approach 2:
The complex manual judgment and adjustment processes that require experienced operator skills are replaced by automated image processing algorithms. The system automatically captures images, calculates difference images, identifies determination regions, and optimizes parameters, transforming an expert-dependent manual process into an automated system accessible to anyone.
3Measurement precision
If multiple images in different orientations are captured to create determination data, then the orientation determination accuracy is improved, but the complexity of the device increases
Solution Approach 1:
The image processing system performs multiple functions using a unified approach: it captures images in different orientations, calculates difference images, identifies determination regions, and optimizes parameters all through the same automated workflow. This multi-functional integration handles various component types and orientations through a single universal process, managing complexity while maintaining high determination accuracy.
Solution Approach 2:
The system manages complexity by systematically varying imaging parameters (orientation angles, lighting conditions) and automatically adjusting them through the difference image calculation process. By treating parameter optimization as an automated computational task rather than a manual configuration process, the system handles multiple parameters without proportionally increasing operational complexity.
4Productivity
If automated image processing is used to determine component orientation, then the productivity is improved, but the difficulty of detecting and measuring deteriorates (for components without polarity marks)
Solution Approach 1:
The system exploits asymmetry in component appearance when viewed from different orientations by calculating difference images. Components without polarity marks often have subtle asymmetric features in their packaging, labeling, or physical structure that become apparent when comparing images from different orientations. The difference image calculation amplifies these asymmetric differences, enabling automated detection without requiring explicit polarity marks.
Solution Approach 2:
The system detects orientation by identifying regions where brightness or color values differ significantly between images captured in different orientations. By analyzing brightness distribution changes and color variations across multiple images, the automated processing can determine component orientation even without polarity marks, maintaining high productivity while overcoming the detection difficulty.
Data Source
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AI summary
A component orientation determination data creation device for creating component orientation determination data used in a component orientation determination system that determines an orientation of the component by comparing a brightness value of a determination region specified in the component orientation determination data within an image of the component captured by a camera with a determination threshold, wherein a difference image between an image of the component in a correct orientation and an image of the component in another orientation is calculated by acquiring multiple images of the component in different orientations by changing the orientation with respect to the camera of the component that is the target for the component orientation determination data creation. Also, a region within the difference image for which the brightness difference is maximized or equal to or greater than a specified value is obtained, and component orientation determination data that includes position information specifying the determination region within the region is calculated. As well as position information of the determination region, information of optimal imaging conditions, and a determination threshold are included in the component orientation determination data.