Mouth Image Processing via Pixel Classification
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Solution Overview
Problem
Current methods for processing mouth images, particularly for special effects like beautification, lack precision in identifying and processing specific features such as teeth, mouth illumination, and lips, leading to suboptimal results.
Innovation Solution
A method and apparatus that utilize a pre-trained mouth detection model to classify pixel points in a mouth image into classes representing teeth, mouth illumination, and lips, with processing based on probability thresholds to enhance precision and accuracy, involving a training process with machine learning to determine the most accurate class for each pixel point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed position region is taken as mouth image for special effect processing, then the processing can be applied to mouth images, but the precision in identifying specific features (teeth, mouth illumination, lips) is insufficient
Solution Approach 1:
The patent divides the mouth image into multiple pixel points and classifies each pixel point into different categories (teeth, lips, mouth illumination, etc.) based on its characteristics. This segmentation allows precise identification and processing of specific features within the mouth image, resolving the contradiction between precision and complexity by organizing the complex task into manageable classification categories.
Solution Approach 2:
The patent applies different processing strategies to different regions of the mouth image based on their classified characteristics. Each pixel point is processed according to its specific category (e.g., teeth region, lip region, illumination region), allowing localized optimization of processing precision for each feature type while maintaining overall system coherence.
2Manufacturing precision
If pixel level processing is performed based on class probabilities, then processing accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent pre-trains a detection model to classify pixel points into different categories before actual processing. This preliminary classification action establishes a framework that guides subsequent processing operations, improving accuracy while managing computational complexity by preparing classification rules in advance rather than computing everything in real-time.
Solution Approach 2:
The patent uses probability thresholds as parameters to determine which pixel points to process. By adjusting these threshold parameters, the system can optimize the balance between processing accuracy and computational complexity, selecting only the most relevant pixel points for detailed processing based on their class probabilities.
Data Source
AI summary
The embodiments of the present disclosure disclose a method and apparatus for processing a mouth image. A specific embodiment of the method includes: obtaining a mouth image to be processed (201); inputting the mouth image into a pre-trained mouth detection model to obtain an output result information, wherein the output result information is used for representing a probability that a content displayed by a pixel point in the mouth image belongs to a class in a target class set (202), the target class set comprising at least one of a first class for representing teeth, a second class for representing mouth illumination, and a third class for representing lips; and processing the mouth image according to the output result information to obtain a processed mouth image (203). The method achieves pixel point-level processing of the mouth image.