Dark Circle Detection via Color Clustering and HSV Segmentation
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Solution Overview
Problem
Current methods are inadequate for effectively detecting and evaluating dark circles in facial images, which are a significant concern for users, particularly females, as they fail to accurately identify and assess the severity of dark circles.
Innovation Solution
A method and apparatus that involve obtaining a to-be-processed image, extracting the dark circle region of interest, performing color clustering to determine n types of colors, recognizing the dark circle region based on these colors, and obtaining an evaluation result, including features like contrast, area, and variance, using pattern recognition to assess the severity of dark circles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used for dark circle detection, then the detection process is simple, but the detection precision and evaluation accuracy are insufficient
Solution Approach 1:
The patent segments the dark circle detection process into multiple distinct stages: skin region segmentation using color space transformation, dark circle region extraction through contour analysis, and severity evaluation based on feature extraction. This segmentation allows each stage to be optimized independently, improving overall detection precision while managing complexity through modular processing
Solution Approach 2:
The patent transforms the image from RGB color space to HSV color space to enhance the separation of skin regions from background. This parameter change in color representation improves the accuracy of skin region segmentation and subsequent dark circle detection, demonstrating how changing computational parameters can enhance measurement precision
2Adaptability or versatility
If simple color thresholding is used, then the processing speed is fast, but the ability to distinguish different types of dark circles is limited
Solution Approach 1:
The patent applies different processing strategies to different regions within the dark circle area. By extracting local features such as color histograms, texture patterns, and geometric characteristics from specific sub-regions, the system can distinguish between pigmented, vascular, and structural dark circles with higher accuracy without requiring complex global analysis
Solution Approach 2:
The patent utilizes color analysis in HSV space to differentiate dark circle types based on their characteristic color profiles. Vascular dark circles exhibit different hue and saturation values compared to pigmented or structural dark circles, allowing the system to classify types efficiently through color特征 extraction without requiring complex imaging
3Measurement precision
If detailed feature extraction is performed to evaluate dark circle severity, then the evaluation accuracy improves, but the computational time increases
Solution Approach 1:
The patent extracts a selective set of key features (color histograms, texture metrics, geometric properties) rather than analyzing all possible image characteristics. This partial action approach focuses computational resources on the most discriminative features for severity evaluation, achieving high accuracy while avoiding the time cost of exhaustive feature analysis
Solution Approach 2:
The patent performs preliminary skin region segmentation and dark circle region extraction before conducting detailed severity evaluation. This preliminary action isolates the area of interest and pre-processes the image data, so that subsequent feature extraction and evaluation operations can be performed more efficiently on a reduced dataset
Data Source
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AI summary
This application provides a method and a dark circle detection and evaluation apparatus. The method includes: obtaining a to-be-processed image; extracting a dark circle region of interest from the to-be-processed image; performing color clustering on the dark circle region of interest to obtain n types of colors in the dark circle region of interest, where n is a positive integer; recognizing a dark circle region in the dark circle region of interest based on the n types of colors; and obtaining a dark circle evaluation result based on the dark circle region. The method can effectively detect and evaluate a dark circle region in an image.