Inference Model for Target Identification Guidance
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Solution Overview
Problem
Current machine learning technologies face challenges in efficiently guiding image acquisition systems to identify and photograph specific targets, particularly in dynamic environments, where existing methods struggle to provide accurate and timely feedback for optimal image capture.
Innovation Solution
A learning device and image processing system that generates an inference model using a series of time-sequentially obtained images, with the model being trained on difference data or teacher data that indicates successful or unsuccessful access to the target, allowing the system to provide real-time guidance for image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to identify targets in images, then the ability to recognize specific targets is improved, but the system cannot provide timely guidance for optimal image capture in dynamic environments
Solution Approach 1:
The system pre-processes and stores difference data representing ideal image capture scenarios during the learning phase. When a live image is captured, the system compares it against this pre-prepared difference data to quickly determine whether the target is properly framed, eliminating the need for complex real-time analysis and enabling immediate feedback guidance.
Solution Approach 2:
The image recognition process is segmented into separate comparison operations against different types of pre-stored difference data (e.g., difference data indicating proper target access vs. difference data indicating improper access). This segmentation allows the system to quickly match specific patterns without performing comprehensive real-time analysis, improving response speed while maintaining accuracy.
2Reliability
If complex machine learning models are trained on large datasets, then the reliability of target identification is improved, but the device complexity and processing requirements increase
Solution Approach 1:
Instead of performing complex real-time machine learning inference, the system creates simplified copies of learning results in the form of difference data representing ideal capture scenarios. These difference data copies are stored and used for rapid comparison during actual image capture, maintaining the reliability benefits of trained models while dramatically reducing processing complexity and device requirements.
Solution Approach 2:
The essential information needed for guidance is extracted from complex machine learning models and stored as difference data representing key characteristics of proper target capture. This extraction separates the heavy computational training phase from the lightweight execution phase, allowing high reliability from comprehensive training while keeping runtime device complexity low.
Data Source
AI summary
An image pickup system includes an input/output modeling section 24, the input/output modeling section 24 creating, as a population, an image group obtained when a specific target is photographed, (access image), and generating an inference model by using, as teacher data, sequential images selected from the image group created as the population, based on whether the specific target can be accessed, wherein each image of the image group is associated with date and time information and/or position information, and the input/output modeling section 24 generates an inference model for determining based on the date and time information and/or the position information whether a process to the specific target is good or bad.