Dynamic Saturation Thresholding for Image Segmentation in Setting Sun Conditions
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Solution Overview
Problem
Conventional image segmentation methods fail to accurately segment images captured in setting sun conditions, leading to unclear and inaccurate segmentation of lawn and non-lawn regions, which affects the efficiency of intelligent lawn mowers.
Innovation Solution
An image segmentation method that determines a saturation segmentation threshold based on a preset condition, using either a first threshold range or a second threshold range, depending on whether the image is affected by the setting sun, with the first threshold range having a higher minimum value to accommodate the increased saturation of lawn regions in such conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation methods with fixed saturation thresholds are used, then the segmentation process is simple and fast, but the segmentation accuracy deteriorates under setting sun conditions causing misjudgment of lawn regions
Solution Approach 1:
The patent implements dynamic threshold adjustment by analyzing the hue histogram to detect setting sun conditions and automatically selecting between different saturation threshold ranges. Instead of using a fixed threshold, the system adapts the segmentation parameters based on the actual lighting conditions detected in the image, thereby maintaining high segmentation accuracy across varying environmental conditions without requiring manual intervention.
Solution Approach 2:
The patent changes the saturation threshold parameter based on detected lighting conditions. When setting sun conditions are detected through hue histogram analysis, the system switches from a first saturation threshold range to a second saturation threshold range. This parameter adaptation allows the segmentation algorithm to compensate for the yellowish color cast introduced by setting sun lighting, thereby maintaining accurate lawn region identification.
2Measurement precision
If different saturation threshold ranges are used for different lighting conditions, then segmentation accuracy under setting sun improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary analysis of the hue histogram to detect setting sun conditions before executing the main segmentation process. By pre-identifying the lighting conditions through hue distribution analysis, the system can proactively select the appropriate saturation threshold range, avoiding the need for trial-and-error segmentation attempts and reducing overall processing time despite the additional preliminary analysis step.
Solution Approach 2:
The patent segments the saturation threshold selection process into distinct ranges (first threshold range for normal conditions, second threshold range for setting sun conditions). This segmentation of the parameter space allows the system to efficiently select the appropriate threshold range based on hue histogram characteristics, reducing the computational burden compared to evaluating all possible threshold values.
Data Source
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AI summary
The present application relates to the field of image processing technology, and specifically discloses an image segmentation method and apparatus, a computer device, and a readable storage medium. The method includes: obtaining a hue histogram corresponding to a hue channel image based on an original image; determining a target parameter value based on the hue histogram; determining a saturation segmentation threshold as a first threshold range if the target parameter value satisfies a preset condition, otherwise determining the saturation segmentation threshold as a second threshold range, where a minimum value of the first threshold range is greater than a minimum value of the second threshold range; and performing image segmentation in combination with the first threshold range or the second threshold range. Captured images are not segmented by using a fixed saturation threshold range, but different saturation segmentation thresholds are used for different images to improve the clarity and accuracy of segmentation. Especially, lawn regions and non-lawn regions can be accurately segmented out from images that turn yellow under the influence of the setting sun.