Camera Settings Configuration via Neural Network Clustering
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Solution Overview
Problem
Commercial cameras' automatic modes often produce less than desired photographs due to suboptimal settings configurations, which fail to capture the full potential of the camera's capabilities.
Innovation Solution
A user device, such as a camera or camera accessory, stores a neural network trained on a large dataset of images and settings, allowing it to automatically configure camera settings by analyzing a test image, clustering similar images, and selecting environment-specific settings for optimal image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic mode is enabled in commercial cameras, then ease of operation is improved, but image quality deteriorates due to suboptimal settings configurations
Solution Approach 1:
The camera system automatically analyzes captured images using a neural network and self-adjusts camera settings (aperture, shutter speed, ISO) without user intervention. The system evaluates image quality metrics and autonomously modifies parameters to optimize subsequent captures, enabling the camera to serve itself rather than relying on pre-programmed automatic modes or manual user input.
Solution Approach 2:
The system implements a feedback loop where captured images are analyzed by a neural network to evaluate quality, and this evaluation feedback is used to adjust camera settings for subsequent image capture. The neural network continuously refines settings based on actual image quality metrics, creating an adaptive system that learns from each capture to improve future results.
2Manufacturing precision
If manual intervention is used to optimize camera settings, then image quality is improved, but ease of operation deteriorates due to complex settings adjustment
Solution Approach 1:
The camera system automatically analyzes captured images using a neural network and self-adjusts camera settings (aperture, shutter speed, ISO) without user intervention. The system evaluates image quality metrics and autonomously modifies parameters to optimize subsequent captures, enabling the camera to serve itself rather than relying on pre-programmed automatic modes or manual user input.
3Adaptability or versatility
If continuous internet connectivity is required for optimal settings, then adaptability is improved, but loss of energy deteriorates due to constant connectivity maintenance
Solution Approach 1:
The neural network model and camera settings optimization algorithms are downloaded and stored on the device beforehand. The system can perform image analysis and settings adjustment using the pre-loaded neural network without requiring active internet connectivity during operation, eliminating continuous energy consumption for connectivity while maintaining adaptability through the pre-trained model's environmental recognition capabilities.
Data Source
AI summary
In certain embodiments, a camera or camera accessory device may read, via a sensor, a test image related to an environment of the device, and the device may obtain a feature vector of the test image that indicates a set of features representing the test image. In some embodiments, the device may perform clustering of sets of camera settings based on the feature vector of the test image to determine clusters of camera settings sets. The device may select, from the clusters of camera settings sets, a cluster of camera settings sets based on scores related to the images similar to the test image. The device may determine environment-specific camera settings based on the cluster of camera settings sets and cause a camera adjustment based on the environment-specific camera settings.