Accessory Apparatus Learning Model Transfer for Image Capture
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Solution Overview
Problem
Existing image capture systems with interchangeable lenses face challenges in efficiently acquiring and utilizing optimal neural network models for image processing, especially when network connections are unavailable, leading to large model scales and reduced user-friendliness due to the need for selecting appropriate models for each lens type.
Innovation Solution
An image capture apparatus and accessory apparatus system where the accessory apparatus stores and transmits a learning model appropriate for its type to the image capture apparatus, allowing the image processing unit to use the optimal model for image processing, even in environments without network connections, and simplifying the user experience by eliminating the need for manual model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single neural network model is used for image processing with various lens types, then the model scale becomes too large, but using separate models for each lens type increases device complexity and storage requirements
Solution Approach 1:
The patent divides the neural network model into a common portion (shared across all lens types) and a lens-specific portion (unique to each lens type). This segmentation allows the system to maintain adaptability to various lens types while reducing the overall model scale by avoiding redundancy in the common portions.
Solution Approach 2:
The common portion of the neural network model serves multiple functions by being applicable to all lens types. This universal component reduces the need for separate complete models for each lens type, thereby reducing device complexity and storage requirements while maintaining versatility.
2Adaptability or versatility
If network connection is required to acquire learning models, then model acquisition is enabled, but image processing cannot be performed in environments without network connections
Solution Approach 1:
The lens apparatus stores the learning model locally in its storage unit before connection to the image capture apparatus. This preliminary action ensures that the model is available immediately upon connection, eliminating the need for network connections during model acquisition and ensuring reliable image processing availability.
3Adaptability or versatility
If manual model selection is required for each lens type, then appropriate models can be selected, but user-friendliness is reduced and camera cannot be used just after lens connection
Solution Approach 1:
The system automatically identifies the connected lens type and retrieves the corresponding learning model without requiring manual user selection. The image capture apparatus autonomously handles model acquisition and selection based on lens identification information, significantly improving ease of operation and user-friendliness.
Solution Approach 2:
The system uses feedback from lens identification information to automatically select and load the appropriate learning model. This feedback mechanism eliminates the need for manual model selection by the user, as the system autonomously responds to lens connection with the appropriate model retrieval and application.
4Adaptability or versatility
If multiple learning models are stored in the image capture apparatus, then various lens types can be supported, but storage capacity requirements increase
Solution Approach 1:
The patent segments the neural network model into common and lens-specific portions, storing only the lens-specific portions in the image capture apparatus while keeping the common portion in the lens apparatus. This segmentation significantly reduces the storage capacity requirements compared to storing complete separate models for each lens type.
Solution Approach 2:
The lens apparatus acts as an intermediary that stores the common portion of the model and provides lens-specific portions to the image capture apparatus as needed. This intermediary approach allows multiple lens types to be supported without requiring all models to be stored simultaneously in the image capture apparatus, reducing storage capacity requirements.
Data Source
AI summary
An image capture apparatus comprises a connection unit that connects to an accessory apparatus, an image processing unit that executes image processing using a learning model, and a control unit that acquires a learning model to be used by the image processing unit in image processing from the accessory apparatus connected to the image capture apparatus.