Region-Based Learning Model Selection for Geometric Estimation
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Solution Overview
Problem
Existing learning models for estimating geometric information require significant time and labor to prepare, making it difficult to cover all scenes effectively.
Innovation Solution
An information processing apparatus and method that sets a setting region and acquires a learning model corresponding to that region from a plurality of pre-learned models, allowing for efficient selection and use of relevant models for image processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a learning model is prepared to cover all scenes, then the coverage and versatility are improved, but the time and labor required for preparation increase significantly
Solution Approach 1:
The patent divides the learning models into multiple versions, each trained for specific scenes or conditions. Instead of creating one comprehensive model for all scenes, the system segments the modeling task into multiple specialized models that can be prepared more quickly and deployed selectively based on the current scene requirements.
Solution Approach 2:
The patent prepares multiple learning model versions in advance, each optimized for different scenes. By performing the preliminary action of creating specialized models for various scenarios beforehand, the system avoids the need to train a single all-encompassing model, thereby reducing the time and labor investment while maintaining comprehensive scene coverage.
2Measurement precision
If multiple learning models are prepared for different regions, then the accuracy for specific regions is improved, but the device complexity increases
Solution Approach 1:
The patent incorporates a selection mechanism that determines which learning model version to use based on the current scene characteristics. This feedback loop allows the system to automatically choose the most appropriate pre-prepared model for the given situation, improving accuracy without requiring manual intervention or complex management overhead.
Data Source
AI summary
Of a plurality of learning models learned to output geometric information corresponding to a captured image, a learning model corresponding to a setting region is acquired.