Image Matching Model Generation for Diverse Workpiece Detection
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Solution Overview
Problem
Existing methods struggle to efficiently generate matching models for a variety of similar yet differently sized workpieces, such as IC chips, due to the significant number of processes required and the impracticality of manually selecting models for each chip during processing.
Innovation Solution
An image processing apparatus that generates a matching model dynamically based on captured images, using common and individual parameters to reduce the load on operators, allowing for efficient model generation and matching processing for each type of workpiece.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If matching models are generated for all types of workpieces in advance, then comprehensive coverage of workpiece varieties is achieved, but the number of processes and operator burden increase significantly
Solution Approach 1:
The system performs preliminary classification of workpieces based on captured images to identify workpiece types and their characteristics before matching processing. This preliminary action enables the system to automatically select or generate appropriate matching models without requiring manual preparation for all possible workpiece types in advance.
Solution Approach 2:
The image processing apparatus automatically generates or selects matching models based on captured images of the actual workpieces being processed. The system serves itself by using the captured image data to create the necessary matching models on-demand, eliminating the need for operators to manually prepare and select models for each workpiece type.
2Device complexity
If a single matching model is used for similar workpieces, then the number of processes is reduced, but accuracy decreases due to size and orientation differences
Solution Approach 1:
The system applies different matching models to different regions or categories of workpieces based on their specific characteristics such as size and orientation. Instead of using a single uniform model, the system tailors the matching model selection to the local characteristics of each workpiece type, thereby maintaining high accuracy while managing the number of models through automated classification.
Solution Approach 2:
The system dynamically selects or generates matching models based on the actual workpieces captured in real-time. Rather than statically preparing all possible models in advance, the system adapts its model selection or generation process according to the specific workpieces being processed, optimizing the balance between model diversity and process complexity.
3Adaptability or versatility
If matching models are generated dynamically for each workpiece, then accuracy and adaptability improve, but processing time increases
Solution Approach 1:
The system performs preliminary classification and analysis of workpiece characteristics from captured images before generating or selecting matching models. This preliminary action enables faster model generation by pre-identifying workpiece types and their key features, reducing the time required for dynamic model creation while maintaining adaptability.
Solution Approach 2:
The system combines multiple functions into an integrated process: capturing images, classifying workpieces, selecting or generating matching models, and performing matching processing all in one automated flow. This merging of functions reduces overall processing time by eliminating manual intervention and sequential delays between separate operations.
Data Source
AI summary
An image processing apparatus acquires a captured image, sets a plurality of parameters for generating a matching model, generates a matching model for detecting a target object based on the acquired captured image and the set plurality of parameters, and identifies an item of a common parameter for common use by a plurality of target objects and an item of an individual parameter to be determined for each of the plurality of target objects from the plurality of parameters in a case where the plurality of parameters is set and a plurality of matching models is generated for each of the plurality of target objects.