Brightness-Based Edge Blur Modeling for Sub-Pixel Edge Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing edge modeling methods fail to accurately account for the occurrence of humps and gutters in edge profiles, which affects sub-pixel level precision in edge position determination.
Innovation Solution
A method for setting edge blur parameters based on brightness contrast, involving target sheets with varying brightness regions, image capture, and kriging-based plane fitting to estimate background and foreground edge blur parameters, allowing for stable sub-pixel precision edge position determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single edge blur parameter is used for edge modeling, then the modeling process is simple, but the edge position determination precision deteriorates due to inability to account for brightness contrast variations
Solution Approach 1:
The patent divides the edge modeling process into two segments: background edge blur parameter estimation and foreground edge blur parameter estimation. This segmentation allows different blur parameters to be applied to different brightness regions, improving edge position precision while maintaining manageable complexity through systematic processing
Solution Approach 2:
The patent applies local quality by using different edge blur parameters for different regions of the image based on brightness contrast. Background regions use one blur parameter while foreground regions use another, allowing the model to adapt to local characteristics and improve overall measurement precision
2Ease of operation
If traditional edge modeling methods are used, then the process is straightforward, but humps and gutters appear in edge profiles causing instability in sub-pixel precision
Solution Approach 1:
The patent incorporates feedback mechanisms by using captured images to estimate edge blur parameters, which are then used to refine the edge model. This iterative process accounts for actual image characteristics and reduces the occurrence of humps and gutters, improving reliability while maintaining operational ease through automated parameter estimation
Solution Approach 2:
The patent changes parameters by estimating different edge blur parameters based on brightness contrast rather than using fixed values. This dynamic parameter adjustment adapts the model to actual image conditions, eliminating humps and gutters and ensuring stable sub-pixel precision
Data Source
Figure 1
Figure 2A~2B
Figure 2C~2D
AI summary
Provided is a method for setting an edge blur according to a brightness value including a first step for providing a plurality of target sheets on each of which a reference pattern for detecting a reference line and a grid pattern for detecting an edge position are provided, and in which changes in brightness values included in the grid patterns are different from each other, a second step for capturing images of the target sheets to obtain target sheet images, a third step for analyzing a reference pattern for each corresponding target sheet to estimate the reference line, and analyzing the grid pattern to extract an edge profile provided in the grid pattern, a fourth step for calculating a gradient of a brightness difference between adjacent pixels on the basis of the edge profile, and acquiring a background edge blur parameter and a foreground edge blur parameter on the basis of the gradient according to brightness contrast present in the image, and a fifth step for generating background edge blur prediction information and foreground edge blur prediction information for each pixel value, which respectively correspond to brightness values on a basis of distributions of the background edge blur parameter and the foreground edge blur parameter. According to the present invention, a resolution pattern of a camera is accurately understood according to a contrast by predicting two edge parameters according to a brightness value, and a more accurate edge position having sub-pixel level precision can be determined through an edge blur having the predicted edge parameters, and thus, there is an effect of accurately and swiftly recognizing the edge position in camera resolution analysis, geographic information construction, autonomous mobile robot, or the like.