Image Capture Control Apparatus User-Triggered Automation
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Solution Overview
Problem
Conventional imaging devices lack the ability to automatically capture images in a condition suitable for the user, as they rely solely on pre-set timing or sensor data without considering user-specific triggers or preferences.
Innovation Solution
An image capture control apparatus and method that detects manual image acquisition operations, acquires observation information, sets a threshold value based on this information, and controls automatic image data acquisition using a processor and memory, enabling automatic capturing based on user-defined triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic capturing is performed based on pre-set timing or sensor data, then capturing can be automated without user intervention, but the capturing conditions do not match user-specific triggers or preferences
Solution Approach 1:
The system performs preliminary learning by detecting manual image acquisition operations and acquiring observation information in advance. This preliminary action enables the system to establish user-specific trigger patterns before automatic capturing begins, allowing the automated system to adapt to user preferences without requiring real-time user intervention during actual capturing operations.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring manual image acquisition operations and using this information to refine automatic capturing triggers. The observation information from manual operations feeds back into the system to adjust and optimize the automatic capturing conditions, creating a closed-loop system that improves adaptability while maintaining automation.
2Measurement precision
If the system learns and adapts to user-specific trigger operations, then capturing accuracy and relevance improve, but the system complexity increases
Solution Approach 1:
The system performs self-learning by automatically detecting manual image acquisition operations and generating its own training data from user behavior. This self-service approach allows the system to improve trigger detection accuracy without requiring external training datasets or complex manual configuration, reducing the practical complexity despite the sophisticated learning capabilities.
Solution Approach 2:
The system changes parameters by establishing threshold values for observation information based on learned user behavior patterns. By dynamically adjusting these parameters based on accumulated observation data, the system achieves high trigger detection accuracy while maintaining a relatively simple underlying architecture that relies on parameter optimization rather than structural complexity.
Data Source
AI summary
An image capture control apparatus includes a processor and a memory. The processor executes a program stored in the memory to perform operations including: detecting a manual image acquisition operation in which a user instructs the image capture control apparatus to capture or record an image to a storage unit according to an instruction; acquiring observation information relevant to a trigger for automatically acquiring image data on the basis of the detection of the manual image acquisition operation; setting a threshold value for automatically acquiring the image data on the basis of the observation information acquired by detecting the manual image acquisition operation; and performing control for automatically acquiring the image data on the basis of the threshold value of the observation information.