Subject-Aware Imaging Support for Multi-Region Focus Control
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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 the type of subject and divide regions within an imaging range, adjusting focus and control settings based on the subject type and probability of classification, enabling precise focus adjustment and region identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional focus detection methods are used in complex scenes with multiple subjects, then the imaging system can capture images, but the accuracy of subject identification and focus control deteriorates
Solution Approach 1:
The imaging range is divided into multiple divided regions, and subjects are identified and classified within each region separately. This segmentation allows the system to handle complex scenes with multiple subjects by processing each region independently, improving subject identification accuracy without overwhelming the system
Solution Approach 2:
A trained model serving as an intermediary is introduced to classify subjects and determine division methods. This intermediary component analyzes images and provides structured information about subject types, positions, and appropriate division strategies, enabling accurate focus control in complex scenes without requiring complex manual processing
2Measurement precision
If a single focus detection region is used, then the device structure is simple, but the ability to identify and focus on specific subjects in complex scenes deteriorates
Solution Approach 1:
The imaging range is automatically divided into multiple divided regions based on subject detection and classification. Each divided region is associated with specific subjects, allowing precise focus detection for each subject while maintaining an organized structure that manages complexity through systematic region assignment
Solution Approach 2:
Different division methods are applied to different divided regions based on the types of subjects detected in each region. This allows the system to optimize focus detection for each specific subject type and region, improving overall detection precision while adapting the complexity of region division to local requirements
3Productivity
If manual focus adjustment is used, then the system is simple to implement, but the productivity and efficiency of imaging operations deteriorates
Solution Approach 1:
The imaging system performs automatic subject identification, classification, and focus control without requiring manual intervention. The trained model automatically analyzes images, determines subject types, divides regions, and controls focus, enabling high-speed continuous imaging and improving productivity while the systematic approach manages the complexity of automatic operations
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.