Particle Position Estimation via Dynamic Dictionary Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing microscopic particle position estimation technologies face challenges in accurately estimating particle positions on images with varying densities due to the need for extensive and computationally heavy dictionaries at all possible densities, which are impractical to prepare and store.
Innovation Solution
An image processing device and method that estimates particle density in clusters, synthesizes a dictionary image from existing images with Fibonacci or power-of-two densities, and matches this synthesized image with the input image to determine particle positions, reducing the need for extensive dictionary storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dictionaries are prepared at all particle densities to enable accurate position estimation for clusters of varying densities, then particle position estimation accuracy is improved, but computational resources and storage requirements increase significantly
Solution Approach 1:
The patent implements a dynamic dictionary generation system that creates dictionaries on-demand based on the particle density of input clusters. Instead of maintaining static dictionaries for all possible densities, the system estimates the density of each input cluster and generates an appropriate dictionary dynamically, allowing the dictionary to adapt to varying input conditions without exhaustive pre-computation
Solution Approach 2:
The patent changes the parameter of particle density to generate different dictionaries. By estimating the particle density of input clusters and generating dictionaries specific to those density values, the system provides accurate position estimation for varying densities without needing to store dictionaries for all possible density values, thus reducing storage requirements while maintaining precision
2Measurement precision
If dictionaries are prepared at all particle densities to match input images with varying densities, then matching accuracy for different density clusters is improved, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary density estimation on input clusters before dictionary generation. By estimating the particle density first and then generating only the necessary dictionary for that specific density, the system avoids the time-consuming process of preparing and searching through exhaustive dictionaries for all possible densities, thus reducing processing time while maintaining matching accuracy
Solution Approach 2:
The system dynamically generates dictionaries based on the estimated density of each input cluster rather than using static pre-computed dictionaries. This dynamic approach allows the system to process each cluster with an appropriately-matched dictionary without the overhead of maintaining and processing exhaustive dictionaries for all possible density values
3Adaptability or versatility
If exhaustive dictionaries at all densities are stored and processed, then complete coverage of varying particle densities is achieved, but device complexity and computational overhead increase
Solution Approach 1:
The patent uses particle density as a controlling parameter to generate appropriate dictionaries. By estimating the density of each input cluster and generating a dictionary matched to that specific density value, the system achieves complete coverage of varying particle densities without the complexity of maintaining exhaustive dictionaries for all possible density values
Solution Approach 2:
The system performs self-service by automatically estimating the particle density of input clusters and generating the appropriate dictionary without external intervention or pre-computation. This self-adaptive approach allows the system to handle varying particle densities with a simple on-demand dictionary generation mechanism rather than a complex pre-configured system
Data Source
AI summary
An image processing device according to an aspect of the present invention includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: extract a cluster image including a cluster of particles from a particle image; estimate particle density of the cluster image; determine numbers based on the particle density, and synthesize a second dictionary image from first dictionary images having densities each being equal to the numbers, the first dictionary images being images of particles of which positions is obtained; match the second dictionary image with the partial image, and determine positions of particles in the cluster image based on a result of the matching and the positions of particles of the first dictionary images; and output the determined positions of particles.