Image Processing Subject Detection Model Selection
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Solution Overview
Problem
Existing subject detection techniques in image processing face accuracy issues due to differences in optical system characteristics between the time of learning and actual image capture, leading to suboptimal detection performance.
Innovation Solution
An image processing apparatus that selects a learning model based on the characteristics of the image to be processed, using multiple learning models tailored to specific image sensors or optical systems, ensuring accurate subject detection by matching the model to the image signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single learning model is used for subject detection, then the device complexity is reduced, but the detection accuracy deteriorates when optical system characteristics differ between learning and detection phases
Solution Approach 1:
The patent applies parameter changes by selecting different learning models based on the optical system characteristics parameters. The system changes the model selection parameter according to the image sensor type and optical system configuration, thereby adapting the detection accuracy to match the specific hardware characteristics without requiring a single complex universal model
Solution Approach 2:
The patent segments the learning models into multiple distinct models, each trained for specific optical system characteristics. Instead of using one general model, the system divides the detection task into multiple specialized models that can be selected based on the matching optical system type, improving accuracy for each specific configuration
2Measurement precision
If multiple learning models are stored for different image characteristics, then subject detection accuracy is improved, but the storage requirement and device complexity increase
Solution Approach 1:
The patent applies local quality by assigning specific learning models to specific local conditions or characteristics of the optical system. Each learning model is optimized for particular image sensor types or optical configurations, providing localized expertise rather than a general-purpose model, thereby improving detection accuracy for each specific hardware configuration
3Measurement precision
If a learning model is trained on image signals with specific optical characteristics, then detection accuracy for those characteristics is improved, but detection performance deteriorates when applied to images with different optical characteristics
Solution Approach 1:
The patent achieves universality by creating a multi-functional learning model selection system. The system can adapt to multiple different optical system characteristics by selecting the appropriate pre-trained model, making the overall detection system universally applicable across various image sensors and optical configurations rather than being limited to a single characteristic set
Data Source
AI summary
An image processing apparatus that is capable of improving subject detection accuracy with respect to image signals is disclosed. The image processing apparatus applies subject detection processing to an image by using a learning model generated based on machine learning. The image processing apparatus selects the learning model from a plurality of learning models stored in advance, in accordance with characteristics of the image to which the subject detection processing is to be applied.