Adaptive Gradient Weighting for Sub-Pixel Edge Detection
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Solution Overview
Problem
Current methods for determining edge locations in images with sub-pixel accuracy are inefficient, particularly for irregular curved edges, as they require significant computation and are not suitable for high-precision modeling in industries with precise product requirements.
Innovation Solution
A method that determines edge locations by calculating a power factor based on edge width and applying it to gradients, using adaptive weighting to minimize error propagation and achieve accurate sub-pixel precision with reduced computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polynomial approximation methods are used to determine edge location, then sub-pixel accuracy can be achieved, but the method is not suitable for irregular curved edges and increases computational complexity
Solution Approach 1:
The patent changes the parameter of gradient weighting by applying a power factor to the gradient values. Instead of using uniform or simple weighted gradients, the invention raises gradients to a power (typically between 1 and 2) to emphasize stronger gradient regions and suppress weaker ones, thereby improving edge location accuracy for irregular curved edges while maintaining computational efficiency
Solution Approach 2:
The patent introduces adaptive power factors that can be dynamically adjusted based on local image characteristics such as edge width and gradient strength. This dynamic adaptation allows the method to automatically optimize its behavior for different types of edges (sharp vs. blurred, straight vs. curved) without requiring complex polynomial fitting procedures
2Measurement precision
If kernel size is increased to improve edge detection accuracy, then measurement precision improves, but computational throughput decreases and computational time increases
Solution Approach 1:
The patent applies different power factors to different regions of the image based on local edge characteristics. By analyzing local gradient strength and edge width, the method assigns appropriate power factors to local regions, achieving high accuracy without requiring a large global kernel size. This local adaptation maintains computational throughput while improving precision
3Measurement precision
If adaptive weighting of gradients with power factors is applied, then sub-pixel accuracy is achieved with reduced computation, but requires determining optimal power factors through error propagation analysis
Solution Approach 1:
The patent performs preliminary error propagation analysis to determine optimal power factors before actual edge detection. By pre-calculating the relationship between power factors and error metrics for typical edge scenarios, the method establishes lookup tables or reference ranges that guide power factor selection during runtime, avoiding complex real-time optimization while maintaining high accuracy
Data Source
AI summary
Provided are a device and a method for determining an edge location based on the adaptive weighting of gradients (AWG). The method for determining an edge location according to an embodiment of the present invention includes the steps of determining, based on the edge width of an edge profile in an image, a power factor to be applied to a gradient of the edge profile in order to determine an edge location, calculating a difference value between values of adjacent pixels in the edge profile to generate the gradient, and applying the power factor determined based on the edge width to the gradient to determine the edge location.