Multi-Camera ROI Matching for Vehicle Image Training Data
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Solution Overview
Problem
Electronic devices mounted on vehicles face challenges in obtaining clear images of the external environment due to noise or blur caused by low luminance and dynamic environments.
Innovation Solution
The electronic device obtains images from multiple cameras with different shutter speeds, identifies corresponding images, and sets regions of interest to generate training data for enhancing image clarity using artificial intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used to capture images in vehicle environments, then the device complexity is low, but the image quality deteriorates due to noise and blur caused by low luminance and dynamic conditions
Solution Approach 1:
The patent combines multiple cameras with different shutter speeds into a unified image processing system. The first camera (fast shutter speed) and second camera (slow shutter speed) work together to capture complementary image data, which are then processed through ROI identification and matching to produce enhanced training data that reduces noise and blur effects
2Manufacturing precision
If multiple cameras with different shutter speeds are used to improve image quality, then the image clarity improves, but the device complexity increases
Solution Approach 1:
The patent applies local quality by identifying and processing regions of interest (ROI) differently from other image regions. Instead of processing entire images, the system focuses computational resources on specific ROIs that contain important objects or features, setting ROIs in both the first and second images based on their respective characteristics and then matching them
Solution Approach 2:
The patent segments the image processing task into distinct stages: ROI identification in the first image, ROI setting in the second image based on the first ROI, and matching corresponding ROIs between images. This segmentation reduces the overall computational complexity by breaking down the complex task of comparing entire images into manageable steps
3Reliability
If images from multiple cameras are processed to generate training data, then the training data quality improves for de-noise and de-blur, but the processing time increases
Solution Approach 1:
The patent extracts only the essential regions of interest from the full images for processing. By identifying ROIs in the first image and corresponding ROIs in the second image, the system extracts only the relevant portions needed for training data generation, discarding unnecessary image data and reducing processing time
Data Source
AI summary
An electronic device may obtain first images from a first camera. The electronic device may obtain, through the communication circuitry, second images from a second camera of which a shutter speed is slower than a shutter speed of the first camera. The electronic device may identify a first image and a second image obtained at corresponding times from the first images and the second images. The electronic device may set a first region of interest (ROI) of an object with a specified type in the first image. The electronic device may set a second ROI at the same position as the first ROI in the second image. The electronic device may generate the first image with the first ROI set and the second image with the second ROI set as training data.