Pressure-Area Image Analysis Using Grayscale Pixel Clustering
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Solution Overview
Problem
Conventional computer aided engineering (CAE) systems face challenges in determining the area under pressure of image data without prior knowledge, leading to increased complexity and resource consumption, and lack precision in outputting detailed pressure areas due to manual estimation.
Innovation Solution
An analysis method and system that transforms images into grayscale, clusters pixels using a K-means algorithm, and determines pressure states to quantify areas under pressure, reducing manual estimation errors and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CAE software is used to perform mechanics analysis, then analysis can be performed, but the area under pressure cannot be determined in advance and manual estimation is required, which reduces measurement precision
Solution Approach 1:
The patent applies preliminary action by performing image preprocessing and pixel clustering before the actual pressure analysis. The system pre-processes the image data, transforms it to grayscale, clusters pixels by intensity values, and identifies the area under pressure in advance, eliminating the need for manual estimation during the analysis phase and improving measurement precision.
Solution Approach 2:
The patent replaces the manual estimation mechanism with an automated image processing and machine learning system. By substituting human manual determination with algorithmic image analysis, pixel clustering, and automated area calculation, the system eliminates subjectivity and improves measurement precision while reducing analysis complexity.
2Productivity
If manual estimation is used to determine areas under pressure, then analysis can proceed, but calculation resources are overly consumed and analysis efficiency is reduced
Solution Approach 1:
The system performs preliminary image processing and pixel clustering before the main analysis, organizing the data into structured groups. This preliminary organization enables more efficient processing during analysis by eliminating the need for repeated manual interventions and resource-intensive operations, thereby improving productivity while optimizing resource usage.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically determine areas under pressure through automated image processing, pixel intensity clustering, and area calculation algorithms. The system serves itself by eliminating the need for manual estimation, thereby improving analysis efficiency and reducing the computational overhead associated with human-in-the-loop processes.
3Device complexity
If the area under pressure is determined after image positions are determined, then analysis can be performed, but the complexity of analysis increases
Solution Approach 1:
The patent resolves this contradiction by performing preliminary image processing and pixel clustering before area determination. The system pre-processes images, transforms them to grayscale, clusters pixels by intensity, and identifies pressure areas in advance. This preliminary organization of data reduces analysis complexity while maintaining high measurement precision through automated algorithms.
4Loss of information
If conventional software output options are used, then total area under pressure can be obtained, but detailed areas corresponding to different pressure zones cannot be output
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple pixel clusters based on intensity values, where each cluster represents a distinct pressure zone. The system segments the image data, assigns pressure states to each cluster, and outputs detailed area information for different pressure zones, thereby preserving complete pressure area detail information while managing software complexity through modular image processing.
Data Source
AI summary
An analysis method for an image data under pressure, includes inputting an image; transforming the image into a grayscale image; clustering each pixel of the grayscale image to obtain a plurality of image data groups; determining a pressure state of the image according to the plurality of image data groups; and determining an area under pressure of the image according to the pressure state of the image.