Automatic Elementary Pattern Calculation in Periodic Digital Images
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Solution Overview
Problem
Current methods for detecting defects and characterizing dimensional variability in digital images with periodic patterns are computationally complex and require prior knowledge, making them inefficient and operator-dependent, especially when dealing with noisy images from microscopy.
Innovation Solution
A method that automatically calculates an elementary pattern in digital images using a calculation processor to determine periods of periodic repetition, project to an orthonormal reference frame, and interpolate to find an elementary reference pattern, allowing for self-adaptive defect detection and variability analysis without requiring parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template matching or Fourier transform methods are used for defect detection, then measurement precision is improved, but device complexity increases and prior knowledge is required
Solution Approach 1:
The system automatically determines the elementary pattern and periodicity parameters from the input image itself through autocorrelation analysis, eliminating the need for external prior knowledge or manual parameter specification. The algorithm self-adapts to the image content by identifying the fundamental repeating unit and using it as the basis for defect detection.
Solution Approach 2:
The method performs preliminary autocorrelation analysis to identify the elementary pattern and periodicity before proceeding to defect detection. This preliminary characterization of the image structure enables subsequent defect detection to be performed more efficiently without requiring complex preprocessing or parameter specification.
2Measurement precision
If template matching or Fourier transform methods are used, then measurement precision is improved, but ease of operation deteriorates due to requiring expert knowledge
Solution Approach 1:
The system automatically determines the elementary pattern and periodicity parameters from the input image itself through autocorrelation analysis, eliminating the need for external prior knowledge or manual parameter specification. The algorithm self-adapts to the image content by identifying the fundamental repeating unit and using it as the basis for defect detection.
3Measurement precision
If conventional methods are used for noisy microscopy images, then measurement precision deteriorates, but device complexity remains high
Solution Approach 1:
The method extracts the essential periodic structure and elementary pattern from the noisy image through autocorrelation analysis, separating the signal (periodic pattern) from the noise. By focusing on the fundamental repeating unit and its mathematical properties rather than attempting to process the entire noisy image directly, the method achieves robust defect detection even in noisy microscopy images.
Data Source
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AI summary
This process includes steps implemented by a computing processor, of: - obtaining (30) an initial digital image, - determining (32) at least two periods of periodic repetition of patterns in the initial digital image, each period being defined by a basis vector having an associated direction and length, the basis vectors forming an initial reference frame, - determining (42) a projection matrix allowing passage from the initial reference frame to an orthonormal reference frame, - calculating (44) a projected reference pattern, in the orthonormal reference frame, using the projection matrix to match a plurality of pixels of the initial digital image and a pixel of said projected reference pattern, - calculating (46) an elementary reference pattern, in the initial reference frame, from the projected reference pattern in the orthonormal reference frame.