Image Analysis System for Particle Randomness Measurement
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Solution Overview
Problem
Current methods for measuring the positional degree of randomness of particles in images are either destructive, error-prone, or fail to provide accurate and intuitive results, especially when dealing with micro- or nano-sized particles in applications like anti-glare films or paint, where uniform distribution is crucial for quality and performance.
Innovation Solution
A system and method for image analysis that uses a detection unit to isolate particles, calculates static and dynamic degree of randomness values using Lennard-Jones potentials and implicit integration, and displays positional randomness, employing tools like scanning electron microscopes or optical microscopes to acquire images, and algorithms like Voronoi Tessellation and Delaunay triangulation to analyze particle distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement methods are used to determine particle distribution, then the process is simple, but the measurement is destructive or error-prone and lacks accuracy
Solution Approach 1:
The patent replaces conventional mechanical or chemical measurement methods with an image analysis-based computational approach. By capturing images of particle distributions and applying mathematical models (Lennard-Jones potentials, Voronoi tessellation, Delaunay triangulation), the system achieves non-destructive, high-precision measurement that eliminates errors associated with traditional methods while maintaining reliability through objective quantitative analysis
Solution Approach 2:
The patent creates a virtual model of the particle distribution system by capturing images and constructing computational representations. Instead of physically measuring or disturbing the actual particle arrangement, the system analyzes optical or electron microscope images to generate virtual particle positions and calculate distribution metrics, enabling repeated non-destructive measurements with high accuracy
2Measurement precision
If complex algorithms like Lennard-Jones potentials and implicit integration are used, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary processing by detecting particle positions from images and constructing Voronoi regions and Delaunay triangulations before applying the complex Lennard-Jones potential calculations. This pre-processing organizes the data structure efficiently, allowing the computationally intensive physics-based models to run more effectively and reducing the overall computational burden while maintaining high measurement precision
Solution Approach 2:
The patent introduces intermediate computational structures (Voronoi regions, Delaunay triangulations, virtual particle models) that serve as mediators between the raw image data and the final randomness measurements. These intermediaries simplify the computational pathway by breaking down the complex analysis into manageable stages, making the system more tractable while preserving measurement accuracy
Data Source
AI summary
A system for image analysis and a method thereof are disclosed. In one embodiment, the system includes a detector configured to receive an image of a sample, isolate particles from a background image of the sample image and detect positions of the isolated particles and a first operator configured to calculate a static degree of randomness values of the particles using Lennard-Jones potentials based on the detected positions. The system may further include a second operator configured to obtain a dynamic degree of randomness values of particles based at least in part on the sum of tensile forces between particles by implicit integration added until the particles reach a dynamic equilibrium, and calculate a positional degree of randomness of particles based at least in part on subtraction of the dynamic degree of randomness values from the static degree of randomness values.