Stereo Vision Brightness Equalization and Calibration Feedback
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Solution Overview
Problem
Stereo vision systems face challenges in achieving accurate depth maps due to differences in brightness values between left and right images from stereo cameras, leading to misalignment and reduced accuracy in stereo matching, especially under varying lighting conditions and slight camera torsion.
Innovation Solution
A stereo vision system and control method that extract color information to smooth and equalize brightness values, perform image rectification using prestored calibration parameters, and adjust these parameters based on matching degree measurements to ensure accurate alignment and depth mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brightness values of left and right images are not equalized, then stereo matching calculation is simpler, but depth map accuracy deteriorates due to misalignment
Solution Approach 1:
The patent applies preliminary action by performing brightness equalization on left and right images before stereo matching. The system extracts color information from both images, generates histograms, smooths them, and equalizes brightness values in advance, ensuring that alignment calculations are performed on equally bright images, thereby improving depth map accuracy without adding complex hardware
Solution Approach 2:
The patent changes the brightness parameter of the images by extracting color information and applying histogram smoothing and equalization. This parameter transformation ensures that both left and right images have equalized brightness values, allowing for more accurate stereo matching and depth map generation while maintaining software-based implementation
2Measurement precision
If many images are captured to improve rectification accuracy, then alignment precision improves, but processing time increases
Solution Approach 1:
The patent implements feedback by measuring the matching degree of the depth map and using this measurement to adjust and optimize calibration parameters. This feedback mechanism allows the system to achieve high rectification accuracy with fewer images by iteratively improving parameter settings based on matching degree measurements, thereby reducing processing time while maintaining precision
Solution Approach 2:
The patent applies partial action by capturing only the necessary number of images from various angles rather than excessive images. The system extracts apexes and lines from these sufficient images to achieve accurate rectification, avoiding the time consumption of processing unnecessary additional images while maintaining high alignment precision
3Measurement precision
If hardware devices are used to control brightness and alignment, then matching accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical hardware systems with software-based image processing. Instead of using hardware devices to physically adjust brightness and alignment, the system uses software to extract color information, generate histograms, smooth them, and equalize brightness values digitally. This substitution maintains stereo matching accuracy while eliminating complex hardware requirements
Solution Approach 2:
The patent changes image parameters (brightness values, color information) through software processing rather than hardware adjustment. By extracting color information from images and applying histogram-based equalization, the system achieves accurate brightness control and alignment without requiring additional hardware devices, thereby reducing device complexity
4Reliability
If calibration parameters are not adjusted, then processing speed is faster, but matching degree deteriorates under changing environments
Solution Approach 1:
The patent applies periodic action by measuring the matching degree at intervals and adjusting calibration parameters accordingly. The system performs stereo matching, measures the matching degree of the resulting depth map, and uses this measurement to optimize calibration parameters in a periodic feedback loop. This ensures high reliability under changing environments while maintaining reasonable processing efficiency through structured periodic optimization
Data Source
AI summary
Provided are a stereo vision system and a control method thereof. A stereo vision system includes an image information extracting unit receiving left and right images of the left and right stereo cameras to extract color information for a brightness control of the images from the received images, an image preprocessing unit performing a process for reducing noises of the left and right images using the color information and a prestored calibration parameter, a stereo matching unit performing stereo matching of the left and right images processed by the image preprocessing unit through an algorithm to obtain a depth map, and a matching result measuring unit receiving the depth map obtained by the stereo matching unit to measure a matching degree, and changing the prestored calibration parameter according to a result of the measurement.