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 slow due to the need to capture a 2-dimensional intensity image of substantial size for each line of physical coordinates, making them inefficient for industrial applications.
Innovation Solution
The system employs a method that accumulates pixel signals before and during the application of control signals, aggregates and digitizes output signals using a sampling function based on random basis and filtering functions, and encodes filtered image signals to reduce the number of required samples, thereby improving throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 2-dimensional intensity image is captured for each line of physical coordinates using conventional digital camera, then complete scene information is obtained, but the image capture time increases up to 100 times longer than necessary
Solution Approach 1:
The patent extracts only the essential information needed for 3D measurement by using a sparse sampling matrix that selects specific pixel rows rather than capturing the entire 2D image. This extraction approach obtains sufficient depth information while discarding redundant data, achieving fast 3D imaging without complete scene capture.
Solution Approach 2:
The system performs partial action by capturing only a subset of pixel rows (M rows out of N total rows, where M << N) rather than the full image. This partial sampling is sufficient for 3D measurement when combined with compressive sensing reconstruction, eliminating the need to capture and process the complete 2D intensity image.
2Loss of information
If all pixel signals are readout and converted to digital signals in conventional systems, then complete image data is obtained, but the processing complexity and time increase significantly
Solution Approach 1:
The patent extracts only the necessary pixel signals for 3D reconstruction by using a sparse sampling matrix that selects M specific rows out of N total rows. This extraction eliminates redundant signal readout and conversion, reducing processing complexity while maintaining measurement accuracy through compressive sensing.
Solution Approach 2:
The system performs partial signal processing by converting only a subset of pixel signals to digital form rather than all signals. This partial digitization approach, combined with random basis function sampling, provides sufficient information for 3D reconstruction with significantly reduced processing complexity.
3Measurement precision
If conventional digital camera captures full 2D intensity images for 3D line scan, then all scene information is recorded, but the throughput is too slow for industrial applications
Solution Approach 1:
The patent extracts only the essential depth information by using a sparse sampling matrix that selects specific pixel rows for measurement. This extraction approach obtains sufficient 3D data while eliminating redundant information, enabling high-speed acquisition suitable for industrial throughput requirements.
Solution Approach 2:
The system performs partial image acquisition by capturing only M rows out of N total rows (where M << N) rather than complete 2D images. This partial sampling, when combined with compressive sensing reconstruction algorithms, provides accurate depth measurements at speeds suitable for industrial production environments.
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 reduces the time required to capture 3D images, achieving a substantial speed improvement over conventional methods by minimizing redundant signal readout and conversion, making it suitable for faster 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
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Figure 1b
Figure 2a
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.