Image Processor With Position-Based Thresholds for Projected-Dot Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LiDAR systems face challenges in accurately detecting the positions of projected dots due to variations in dot intensity and position based on distance, leading to inaccuracies in depth measurement.
Innovation Solution
An image processor is employed to generate binary images using correction threshold information based on dot positions, and a noise-removing mechanism to enhance dot detection accuracy, utilizing a first and second processor to refine the input images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single threshold value is used for dot detection, then the detection process is simple, but detection accuracy deteriorates due to variations in dot intensity and position based on distance
Solution Approach 1:
The patent divides the detection space into multiple regions (first region for close distances, second region for far distances) and assigns different threshold values to each region. This allows the system to adapt the detection criteria locally based on distance, improving accuracy without significantly complicating the overall process.
Solution Approach 2:
The patent changes the threshold parameter based on distance conditions. When the distance is less than a reference value, a first threshold is used; when the distance is greater than or equal to the reference value, a second threshold is used. This dynamic parameter adjustment optimizes detection accuracy across different operating conditions.
2Measurement precision
If correction threshold information is predetermined for different positions, then dot position detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent pre-calculates and stores correction threshold information for different positions before actual dot detection. This preliminary preparation allows the image processor to quickly retrieve and apply appropriate thresholds during operation, improving detection accuracy while minimizing real-time computational complexity.
Solution Approach 2:
The patent segments the detection field into multiple positional regions and pre-determines correction thresholds for each segment. This segmentation approach allows complex correction logic to be organized into manageable, pre-computed segments, reducing the real-time processing burden on the image processor.
3Measurement precision
If noise removal is performed using multiple input images, then dot detection accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The patent extracts noise from the input image by comparing it with a reference image or by identifying and removing noise components that do not correspond to actual projected dots. This extraction approach removes noise efficiently without requiring extensive processing of multiple full images, thus improving detection accuracy while limiting time loss.
Solution Approach 2:
The patent applies noise removal selectively to regions or pixels where noise is most likely to affect detection accuracy, rather than processing the entire image uniformly. This partial action approach achieves sufficient noise removal for accurate dot detection while reducing overall processing time and computational complexity.
Data Source
AI summary
Disclosed is an image processor and a depth sensor including the same, and the image processor may include a first processor configured to generate a binary image based on an input image having projected dots and correction threshold information predetermined depending on positions of the projected dots, and a second processor configured to detect the projected dots based on the input image and the binary image.