Imaging Support Control for Subject-Based Region Division
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Solution Overview
Problem
Existing imaging technologies struggle to accurately control focusing and subject identification in imaging systems, particularly in complex scenes with multiple subjects, leading to suboptimal image capture and processing.
Innovation Solution
An imaging support apparatus and method that utilizes machine learning to identify and classify subjects within an imaging range, dividing regions based on subject types and adjusting focus and control settings accordingly, including custom type control for optimal imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging control methods are used, then the imaging system is simple to operate, but the accuracy of focus control and subject identification deteriorates in complex scenes
Solution Approach 1:
The patent introduces an imaging support apparatus as an intermediary component between the image sensor and the control system. This mediator performs machine learning-based subject identification and division method determination, enabling accurate focus control in complex scenes without requiring the main imaging system to handle all processing complexity directly.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based focus control mechanisms with a machine learning-based system. The processor uses trained models to automatically identify subjects and determine division methods, substituting complex mechanical adjustment systems with intelligent algorithms that adapt to various imaging scenarios.
2Measurement precision
If machine learning-based subject identification is implemented, then subject identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by using a trained machine learning model that has been pre-trained on extensive datasets. This pre-training allows the system to perform rapid subject identification during actual imaging operations without requiring extensive real-time computation, thus reducing processing time while maintaining high accuracy.
Solution Approach 2:
The patent segments the image processing task by dividing the imaging range into multiple regions based on subject identification. The processor determines a division method that partitions the image into manageable sections, allowing parallel processing and reducing the computational burden on the system while maintaining accurate subject identification.
3Manufacturing precision
If region division based on subject type is performed, then imaging control precision improves, but the complexity of control algorithms increases
Solution Approach 1:
The patent applies local quality by determining different division methods for different regions of the image based on subject type. Instead of applying a uniform control strategy across the entire image, the system analyzes local characteristics of each region and applies appropriate division methods tailored to specific subjects, thereby improving imaging control precision without requiring overly complex global algorithms.
Data Source
AI summary
There is provided an imaging support apparatus including a processor and a memory connected to or built into the processor. The processor is configured to acquire a type of a subject based on an image obtained by capturing an imaging range, which includes the subject, with an image sensor, and output information indicating a division method for dividing a divided region, where the subject is divided to be identifiable from other regions in the imaging range, according to the acquired type.