Autonomous Imaging Control Using Neural Feedback for Image Retakes
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Solution Overview
Problem
Conventional imaging systems lack the ability to improve with learning, resulting in inefficient correction of errors in acquired images, as traditional image processing algorithms do not adapt or improve over time.
Innovation Solution
An autonomous imaging system that utilizes a neural network or prediction model trained with a large dataset of images and corresponding action information to autonomously control image capturing devices, determining whether to accept, discard, or retake images based on the analysis of captured images, thereby improving image quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image processing algorithms are used, then the imaging system can operate with simple processing, but errors in images cannot be effectively corrected and the system cannot improve with learning
Solution Approach 1:
The imaging system employs a neural network that autonomously analyzes captured images, determines whether to accept or retake images, and controls the imaging device without human intervention. This self-service capability enables the system to automatically improve image quality through learning from training data while maintaining operational simplicity for users.
Solution Approach 2:
The patent replaces traditional mechanical/image processing algorithms with an intelligent neural network system. This substitution enables the system to learn from training data, automatically detect image quality issues, and make intelligent decisions about retaking images, thereby improving reliability without requiring users to understand complex processing mechanisms.
2Productivity
If conventional imaging systems are used, then the system structure remains simple, but correction of image errors becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis of captured images using a neural network before final image acceptance or retake decisions are made. This preliminary action allows the system to quickly identify potential image quality issues and automatically determine corrective actions, significantly reducing the time required for error correction compared to conventional manual review processes.
Solution Approach 2:
The imaging system implements a feedback loop where the neural network continuously analyzes image quality, compares it against learned standards from training data, and automatically controls the imaging device to retake images when necessary. This real-time feedback mechanism eliminates time-consuming manual error correction by enabling automated, rapid iteration until acceptable images are obtained.
3Adaptability or versatility
If a neural network is introduced to enable learning and autonomous control, then image quality and system adaptability improve, but device complexity increases
Solution Approach 1:
The patent introduces a neural network as an intermediary component between the image capturing device and the control system. This intermediary enables learning and adaptive behavior while maintaining a relatively simple user interface and operational workflow. The neural network handles the complexity of learning and decision-making internally, allowing the system to gain adaptability without significantly increasing user-facing complexity.
Data Source
AI summary
The present disclosure pertains to autonomous control of an imaging system. In some embodiments, training information including at least a plurality of images and action information are received. The plurality of images and action information are provided to a prediction model to train the prediction model. Further, an image capturing device is controlled to capture an image of a portion of a living organism, the image is processed, via the prediction model, to determine an action to be taken with respect to the image, and the determined action is taken with respect to the image.