Automated Tessellated Image Template Boundary Identification
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Solution Overview
Problem
Current template matching techniques for tessellated images require manual identification and selection of template images, leading to time-consuming and inconsistent results, and do not allow for dynamic updating, which hampers automation and quality control in industrial applications.
Innovation Solution
The use of template unit boundary identification techniques to automatically determine the size and boundaries of pattern units in tessellated images, enabling the selection and updating of template images through pixel analysis line segments and convolution processing, facilitating automated template image retrieval and dynamic adaptation to pattern anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and selection of template images is used, then template matching can be performed, but the process becomes time-consuming and inconsistent
Solution Approach 1:
The system performs self-service by automatically selecting template images from the source image without human intervention. The template selection process is automated through algorithms that identify and extract appropriate template regions, eliminating the need for manual operator selection and ensuring consistent, repeatable results across different operations.
Solution Approach 2:
The system performs preliminary actions by pre-processing the source image to identify potential template regions before the actual template matching operation. This includes analyzing image characteristics, detecting pattern boundaries, and pre-selecting candidate templates, which prepares the system in advance and reduces the time required during the main matching process.
2Productivity
If manual template selection is repeated for each template matching process, then template matching can be performed, but operator time and consistency issues are propagated
Solution Approach 1:
The system achieves universality by creating a reusable template selection mechanism that can be applied across multiple template matching operations. Once the template selection algorithm is established, it serves as a universal tool that automatically selects appropriate templates for different matching tasks without requiring repeated manual intervention, thereby improving productivity and eliminating consistent time losses.
3Adaptability or versatility
If traditional template matching is used, then pattern units can be located, but the system cannot dynamically update templates to accommodate pattern anomalies
Solution Approach 1:
The system implements dynamics by enabling dynamic template updating capabilities. Templates are not static but can be adjusted and re-selected based on the actual patterns detected in the source image. This allows the system to adapt to pattern anomalies such as fuzzy borders, distortions, or variations in the tessellated image while maintaining reliable and accurate template matching through continuous optimization.
Data Source
AI summary
Systems and methods which obtain template images corresponding to pattern units of tessellated images using a template unit boundary identification technique are described. Template unit boundary identification techniques of embodiments operate to analyze a sample of a tessellated image to determine a template unit size for the template image, use the determined template unit size to define pixel analysis line segments for identifying template unit boundaries in the tessellated image, and select a template image based on the template unit boundaries identified using the pixel analysis line segments. One or more sample area images may be selected for use in determining a template unit size. Pixel analysis line segments, configured based upon a determined template unit size, may be used for identifying boundaries of a template unit. Moreover, dynamic template image updating may be provided.