Image Processing Apparatus Orange Peel Evaluation
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Solution Overview
Problem
Existing image processing techniques for evaluating the state of a surface, such as those with orange peel defects, have a low correlation with subjective visual evaluations, particularly when the surface has low gloss image clarity, low specular reflectivity, high diffuse reflectivity, or large illumination image widths, leading to inaccurate evaluation results.
Innovation Solution
An image processing apparatus that calculates an orange peel evaluation value using a weighted sum of the variation amount of the edge position and luminance, adjusted by factors such as gloss image clarity, specular reflectivity, diffuse reflectivity, and illumination image width, to improve correlation with subjective evaluations, incorporating equations that reduce the contribution of edge position variation in specific conditions and add luminance variation as a weighted factor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only edge position variation is used for evaluation, then the evaluation method is simple, but the correlation with subjective visual evaluation is low
Solution Approach 1:
The patent combines multiple evaluation parameters (edge position variation and edge luminance variation) into a unified evaluation formula. This merging of parameters improves the correlation with subjective visual evaluation while maintaining a relatively simple evaluation framework, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent introduces additional parameters (edge luminance variation) and adjusts their weights dynamically based on surface properties (gloss image clarity, specular reflectivity, diffuse reflectivity). This parameter expansion and adaptive weighting improves measurement precision without excessively increasing complexity.
2Adaptability or versatility
If edge position variation is used for evaluation, then the method works for general surfaces, but it fails to accurately evaluate surfaces with low gloss image clarity, low specular reflectivity, high diffuse reflectivity, or large illumination image widths
Solution Approach 1:
The patent makes the evaluation method dynamic by adjusting the weight of edge position variation based on surface properties. When gloss image clarity is low, specular reflectivity is low, diffuse reflectivity is high, or illumination image width is large, the weight of edge position variation is reduced. This dynamic adaptation maintains versatility across different surface types while improving precision for challenging surfaces.
Solution Approach 2:
The patent applies different evaluation strategies to different surface conditions. Instead of using a fixed evaluation method, it adjusts the contribution of each parameter (edge position vs. edge luminance) based on local surface properties, ensuring accurate evaluation for each specific surface type.
3Measurement precision
If the weight of edge position variation is increased, then the evaluation is more sensitive to orange peel, but it produces abnormal evaluation values for surfaces with low gloss image clarity, low specular reflectivity, high diffuse reflectivity, or large illumination image widths
Solution Approach 1:
The patent dynamically adjusts the weight of edge position variation based on surface properties. The weight is set to a first value (higher sensitivity) when surface conditions are favorable, and to a second value (lower sensitivity) when surface conditions are challenging (low gloss, low specular reflectivity, high diffuse reflectivity, or large illumination width). This dynamic weighting maintains both sensitivity and reliability across different conditions.
Solution Approach 2:
The patent preemptively adjusts the evaluation parameters based on predicted surface conditions. By evaluating surface properties first and then adjusting the weight of edge position variation accordingly, it prevents the generation of abnormal evaluation values before they occur, ensuring reliable results across all surface types.
Data Source
AI summary
An image processing apparatus includes an acquisition unit configured to acquire image data obtained by imaging an object having a surface on which an illumination image is generated, a calculation unit configured to calculate a variation amount of an edge position of the illumination image and a variation amount of an edge luminance of the illumination image based on the image data, and an evaluation unit configured to evaluate a state of the surface of the object based on the variation amount of the edge position and the variation amount of the edge luminance.