Deep Learning Segmentation Model for Hair Area Extraction
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Solution Overview
Problem
Current image processing technologies face challenges in accurately segmenting hair areas due to reliance on geometrical face location, diverse hair shapes and styles, and unreliable hair color statistical information, leading to reduced accuracy and robustness.
Innovation Solution
An image processing method and system that uses a segmentation model with deep learning to obtain feature parameters from a calibration area, enabling precise segmentation by selecting a calibration area within the image, such as hair, and generating a segmentation result with high accuracy and robustness across various scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometrical face location and statistical information methods are used for hair segmentation, then the processing speed is fast, but the accuracy rate and robustness are reduced
Solution Approach 1:
The patent replaces traditional mechanical/statistical methods (geometrical face location, color probability calculation) with a deep learning-based segmentation model. This substitution enables the system to automatically learn complex hair features and patterns, significantly improving segmentation accuracy while maintaining acceptable processing speeds through optimized model architecture.
Solution Approach 2:
The patent transforms the segmentation approach from using fixed statistical parameters (color histograms, geometric constraints) to using dynamic feature parameters learned from data. The segmentation model processes input images through multiple layers to generate probability maps, where parameters are adaptively adjusted based on learned patterns rather than predetermined statistical distributions.
2Device complexity
If traditional hair segmentation methods are used, then the device complexity is low, but the reliability and robustness are reduced
Solution Approach 1:
The patent divides the hair segmentation task into multiple processing stages: input image preparation, feature extraction through convolutional layers, probability map generation, and final segmentation output. This multi-stage segmentation approach allows each component to specialize in specific features, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces probability maps as an intermediary representation between the segmentation model and final hair segmentation results. These probability maps encode uncertain boundary information and allow for flexible post-processing, serving as a mediator that enhances robustness without requiring overly complex direct segmentation algorithms.
3Device complexity
If statistical information of hair color and shape is used, then the processing is simple, but the accuracy is reduced due to diverse hair styles and colors
Solution Approach 1:
The patent performs preliminary feature extraction and learning during the model training phase, where the segmentation model learns to handle diverse hair styles and colors from training data. This preliminary action embeds knowledge about hair variability into the model parameters, enabling accurate segmentation without complex runtime processing for each hair type.
Solution Approach 2:
The patent designs a universal segmentation model that can handle multiple hair types, styles, and colors through a single unified architecture. The model processes diverse input images through the same feature extraction and probability generation pipeline, making it multi-functional without requiring separate specialized algorithms for different hair categories.
Data Source
AI summary
This disclosure provides an image processing method and processing system. The method includes obtaining an image and selecting a calibration area of the image. The method also includes reading a feature parameter corresponding to the calibration area from a preset segmentation model. The method further includes segmenting the image by using the feature parameter, to generate a segmentation result corresponding to the calibration area. This image segmentation by using the feature parameter obtained from the segmentation model provides a high accuracy segmentation rate and can be applied in wide scenarios.