In-Vehicle Camera Calibration Using Region-Based Feature Thresholds
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Solution Overview
Problem
Existing image processing devices face reduced calibration accuracy due to uneven distribution of feature points in captured images, leading to discarded calibration opportunities for images with fewer feature points than a threshold value.
Innovation Solution
An image processing device that divides captured images into regions, sets individual threshold values for each region to evenly distribute feature points, and performs matching and relative parameter calculation based on these points to improve calibration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a captured image is determined on the basis of a distance distribution of feature points and only images with sufficient feature points are extracted for calibration, then the reliability of calibration results is improved, but the productivity of calibration process deteriorates due to discarded calibration opportunities
Solution Approach 1:
The captured image is divided into multiple divided regions, and feature point detection is performed independently in each region. This segmentation allows the system to accumulate feature points from multiple regions even when the total number in the entire image is below the threshold, thereby resolving the contradiction between reliability and productivity by enabling calibration with previously discarded images.
Solution Approach 2:
Different threshold values are set for different divided regions based on their local characteristics. This local quality approach allows regions with fewer feature points to contribute to the overall calibration process, increasing the number of usable calibration images while maintaining the reliability of calibration results through region-specific optimization.
2Device complexity
If feature points are detected without region division using a uniform threshold, then the device complexity is reduced, but the manufacturing precision of calibration deteriorates due to uneven feature point distribution
Solution Approach 1:
The image is segmented into multiple divided regions with feature point detection performed in each region independently. This segmentation enables uniform distribution of feature points across the image by localizing detection to specific regions, thereby improving calibration accuracy while maintaining relatively simple processing through modular region-based operations.
Solution Approach 2:
Different threshold values are applied to different divided regions based on their local image characteristics. This local quality approach optimizes feature point detection in each region individually, ensuring uniform overall distribution and high calibration accuracy without requiring complex global processing algorithms.
3Reliability
If a high threshold value is used for feature point detection to ensure quality, then the reliability of detected feature points is improved, but the quantity of detectable feature points decreases leading to discarded calibration opportunities
Solution Approach 1:
The image is divided into multiple regions, and feature point detection is performed independently in each region with region-specific threshold values. This segmentation allows the system to accumulate a sufficient quantity of high-quality feature points from multiple regions, resolving the contradiction by enabling both high reliability and adequate quantity for calibration.
Solution Approach 2:
Different threshold values are set for different divided regions based on their local characteristics. This local quality approach allows each region to contribute high-quality feature points appropriate to its characteristics, and the cumulative effect across all regions ensures sufficient quantity for calibration while maintaining high reliability.
Data Source
AI summary
An image processing device includes: a region dividing unit that divides each of a plurality of captured images captured by an in-vehicle camera into a plurality of divided regions; a feature point detection unit that detects, as a feature point, a portion exceeding a threshold value for each of the divided regions; a threshold value setting unit that sets the threshold value for each of the divided regions so as to evenly distribute the feature points detected in each of the divided regions; a feature point matching unit that performs matching by associating the feature points detected in the plurality of captured images with each other between the plurality of captured images; and a relative parameter calculation unit that calculates a relative parameter on the basis of the feature points associated between the plurality of captured images.