Skin Color Segmentation Using Ambient Light Spectrum Correction
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Solution Overview
Problem
Existing image processing techniques using skin color models are prone to errors and low accuracy due to the direct use of RGB images from cameras, which are affected by CCD color filters, exposure control, and white balance, leading to inconsistencies in recognition processes.
Innovation Solution
A method that involves collecting the ambient light spectrum while capturing an image, determining RGB tristimulus values and skin color coordinates based on this spectrum and color filter absorption curves, and performing skin color segmentation using a skin color model, while optionally turning off the white balance function or applying inverse transformation to reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If RGB image directly outputted from camera is used for skin color segmentation, then processing is simple and fast, but accuracy is low due to CCD color filter, exposure control, and white balance effects
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing correction values in a lookup table before actual skin color segmentation. The system pre-processes the relationship between white balance parameters and skin color coordinates, creating a reference table that enables fast correction during runtime without real-time complex calculations, thus maintaining high processing speed while improving accuracy
Solution Approach 2:
The patent introduces an intermediary correction mechanism using a lookup table that mediates between the raw RGB image data and the final skin color segmentation results. This intermediary table stores pre-computed correction factors that account for camera-specific characteristics, allowing accurate color correction without direct real-time computation of complex color transformation equations
2Adaptability or versatility
If white balance function is enabled in camera, then image colors are adjusted for ambient light, but skin color coordinates become inconsistent leading to recognition errors
Solution Approach 1:
The patent applies preliminary anti-action by pre-calculating the effect of white balance on skin color coordinates and storing inverse correction values. The system anticipates the white balance transformation and prepares compensating correction factors in advance, which are then applied to counteract the white balance effect during skin color segmentation, thereby maintaining recognition reliability under varying ambient light conditions
Solution Approach 2:
The patent implements feedback by using the known white balance parameters to retrieve corresponding correction values from the lookup table. The system feeds back the white balance information into the correction process, automatically adjusting skin color coordinates based on the camera's white balance settings, thus maintaining consistent skin color recognition across different lighting conditions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of skin color segmentation by accounting for ambient light conditions and camera settings, reducing errors and improving the similarity of RGB values to the original image, thereby improving recognition processes.
Implementation Method 1
determining RGB tristimulus values corresponding to ambient environment of the captured image based on the collected ambient light spectrum and a color filter absorption curve, determining skin color coordinates based on the collected ambient light spectrum and a spectral absorption curve of a skin color model
Data Source
AI summary
A method for processing an image may include: collecting an ambient light spectrum while capturing an image (10); determining RGB tristimulus values corresponding to ambient environment of the captured image based on the collected ambient light spectrum and a color filter absorption curve (11); determining skin color coordinates based on the collected ambient light spectrum and a spectral absorption curve of a skin color model (12); and determining RGB values in the skin color model based on the RGB tristimulus values and the skin color coordinates (13).