3D Line-Scan Imaging with Compressive Sensing for Faster Capture
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Solution Overview
Problem
Conventional machine vision systems for capturing 3D range images are inefficient due to the need to capture a 2-dimensional intensity image of substantial size for each line of physical coordinates, resulting in significantly longer capture times compared to intensity image acquisition, making them too slow for many industrial applications.
Innovation Solution
The system employs a method where pixel signals are accumulated and processed using uncorrelated control signals representative of random basis functions and filtering functions, allowing for the aggregation and digitization of output signals in a way that reduces the number of measurements required, thereby accelerating the image capture process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 2D intensity image capture is used for each line of physical coordinates, then complete image information is obtained, but capture time increases significantly (100 times longer than intensity image acquisition)
Solution Approach 1:
The patent extracts only the essential depth information from the scene by using a light plane to illuminate specific regions and capturing only the reflected light from those regions. Instead of capturing the entire 2D intensity image, the system extracts depth coordinates along the light plane, significantly reducing the data volume and capture time while maintaining the necessary measurement precision for 3D reconstruction.
Solution Approach 2:
The patent segments the imaging process by dividing the scene into multiple lines along the light plane direction. Each line is captured independently with reduced data requirements, and the complete 3D image is reconstructed by combining these segmented line measurements. This segmentation approach reduces the complexity and time required for each individual measurement while preserving overall image completeness.
2Measurement precision
If 2D intensity images of substantial size are captured for 3D line formation, then accurate depth information is obtained, but system throughput decreases
Solution Approach 1:
The system extracts only the necessary depth coordinates along the light plane rather than capturing complete 2D intensity images. By focusing measurement resources on extracting depth information specifically along the illuminated lines, the system maintains accurate depth measurement while reducing overall data processing requirements and improving throughput.
3Loss of information
If conventional laser-line based 3D image formation is used, then complete 3D range data is captured, but capture speed is too slow for industrial applications
Solution Approach 1:
The patent segments the 3D imaging process into multiple rapid line measurements along the light plane. Instead of capturing complete 2D images for each depth slice, the system captures one-dimensional line profiles sequentially and reconstructs the full 3D range data from these segmented measurements, achieving both data completeness and improved capture speed for industrial applications.
Solution Approach 2:
The system performs partial action by capturing only the essential line profile data needed for 3D reconstruction rather than complete intensity images. This partial measurement approach provides sufficient information for accurate 3D range data formation while dramatically reducing capture time to meet industrial speed requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the throughput of the vision system by reducing the number of samples needed to encode image information, potentially achieving speed improvements of the same order as the data reduction, making it more suitable for industrial machine-vision applications.
Implementation Method 1
accumulating a first pixel signal based on incoming light energy for each of a plurality of pixel elements of a pixel array, the pixel elements each including a light sensor
Data Source
AI summary
A machine vision system may perform compressive sensing by aggregating signals from multiple pixels. The aggregation of signals may be based on a sampling function. The sampling function may be formed of a product of a random basis, which may be sparse, and a filtering function.