Adaptive Threshold Edge Detection for Varying Brightness
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Solution Overview
Problem
Existing edge detection methods, such as the Canny filter, face challenges in varying brightness environments and fast image processing, as they rely on fixed thresholds that are not adaptive to changing conditions, leading to poor detection results in environments like night, foggy, or rainy conditions.
Innovation Solution
An adaptive threshold edge detection method that calculates dynamic upper and lower thresholds based on the distribution of intensity values in the image, using a histogram divided into bins to determine the mean and variance, allowing for real-time adjustment of thresholds for improved edge detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed thresholds are used in edge detection, then the detection process is simple and fast, but the detection accuracy deteriorates in varying brightness environments
Solution Approach 1:
The patent applies dynamics by transitioning from fixed thresholds to adaptive thresholds that dynamically adjust based on local image characteristics. The threshold value is no longer static but changes according to the statistical properties (mean and standard deviation) of pixel intensities in the neighborhood of each pixel, enabling the edge detector to adapt to varying brightness conditions while maintaining detection speed through efficient local computations.
Solution Approach 2:
The patent implements parameter changes by modifying the threshold parameter based on local image statistics. Instead of using a constant threshold, the method calculates the mean and standard deviation of pixel intensities in a local window and uses these parameters to adaptively set the threshold, thereby improving edge detection accuracy across different brightness conditions without significantly increasing computational complexity.
2Measurement precision
If adaptive thresholds are used to improve edge detection in varying environments, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing into local neighborhoods around each pixel. Instead of computing global statistics for the entire image, the method calculates mean and standard deviation only within a local window, significantly reducing computational complexity while still capturing local brightness variations. This localized approach enables adaptive thresholding without the prohibitive computational cost of global analysis.
Solution Approach 2:
The patent implements partial action by computing threshold adaptation only where necessary - in the local neighborhood of each pixel being evaluated. Rather than processing the entire image with full adaptive thresholding, the method applies statistical calculations only to relevant local regions, reducing overall computational burden while maintaining detection accuracy in varying brightness conditions.
3Productivity
If fixed thresholds are used, then the processing is computationally efficient, but false edges increase in adverse weather conditions
Solution Approach 1:
The patent addresses reliability by dynamically changing the threshold parameter based on local image characteristics. In adverse weather conditions where brightness varies significantly, the adaptive threshold adjusts to local statistics, preventing fixed thresholds from either suppressing real edges or accepting false edges. This parameter adaptation maintains high detection reliability while preserving processing efficiency through localized computations.
Data Source
AI summary
Systems and methods are provided for detecting edges in an image. In an example method, the image is smoothed using a filter. A gradient magnitude and angle are determined for each pixel of the image. A non-maximum suppression is performed on the resulting image data. A double threshold with an upper and a lower threshold are applied to the resulting image data to determine the edges in the image. The upper and the lower thresholds are calculated automatically using a distribution of intensity values of the image. In example systems, an image is acquired by a camera, or other image acquisition units. An image processing unit is configured to detect the edges in the image using an adaptive threshold edge detection method.