Multipoint Video Tool Lighting Adjustment for 3D Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Precision machine vision inspection systems face challenges in accurately determining Z-height measurements across surfaces, particularly for complex shapes, due to issues like underexposure and overexposure, leading to low contrast and high image noise, which complicates reliable Z-height measurement and reconstruction.
Innovation Solution
A multipoint video tool using depth from focus operations that adjusts lighting parameters to acquire multiple autofocus image stacks with different exposure levels, allowing for well-exposed images across subregions, and utilizes a user-friendly interface for non-expert operators to program general-purpose machine vision inspection systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single light setting is used for imaging, then the imaging process is simple and fast, but underexposure and overexposure occur leading to low contrast and high image noise
Solution Approach 1:
The system dynamically adjusts lighting parameters across multiple image stacks, transitioning from static single-setting imaging to dynamic multi-setting imaging. The lighting conditions are varied systematically to capture different exposure levels, allowing the system to adapt to varying surface reflectivity and angles while maintaining measurement precision.
Solution Approach 2:
The invention changes the lighting parameters (illumination intensity, angle, or duration) across multiple image stacks to optimize exposure for different subregions. By varying these parameters and selecting the best-exposed images, the system achieves accurate Z-height measurements without sacrificing imaging speed significantly.
2Measurement precision
If multiple image stacks with different lighting parameters are acquired, then exposure quality across subregions is improved, but the complexity of the imaging process increases
Solution Approach 1:
The imaging process is segmented into multiple passes, each targeting different subregions or exposure requirements. Instead of attempting to capture all subregions perfectly in a single pass, the system divides the work into multiple image stacks with optimized lighting for specific areas, then combines the results.
Solution Approach 2:
The system performs preliminary imaging passes to identify subregions that require additional imaging with different lighting parameters. This preliminary action guides subsequent imaging efforts, ensuring that only necessary additional images are captured, thereby reducing overall complexity while maintaining precision.
3Measurement precision
If multiple image stacks with different lighting parameters are acquired, then exposure quality across subregions is improved, but the time required for imaging increases
Solution Approach 1:
The multiple image stacks are acquired in a continuous sequence without interrupting the imaging process. The system maintains continuous operation by systematically varying lighting parameters between stacks rather than stopping to analyze and plan each subsequent image, thereby minimizing idle time and maintaining productivity.
Solution Approach 2:
The system acquires more image stacks than the minimum single stack, but selects a subset of the best-exposed images for final processing. This partial use of acquired data allows the system to invest in additional imaging time upfront while maintaining efficient processing by not analyzing all captured images.
4Ease of operation
If conventional autofocus methods are used, then the system is simple to operate, but reliable Z-height measurement is compromised for complex surfaces
Solution Approach 1:
The system performs self-correction by automatically identifying and compensating for exposure problems in different subregions. The multi-image stack approach with varied lighting enables the system to self-diagnose and self-correct measurement reliability issues without requiring operator intervention or expertise in optimizing lighting for complex surfaces.
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
The solution provides reliable and accurate Z-height measurements by ensuring optimal exposure across the region of interest, improving the precision and reliability of surface shape inspection in machine vision systems, even for complex surfaces with varying angles and reflectivity.
Implementation Method 1
a first image stack is acquired using darkness limiting lighting parameters that satisfy a darkness limiting criterion for image pixels in the region of interest and that are the same for each image in the first image stack, and a second image stack is acquired using brightness limiting lighting parameters that satisfy a brightness limiting criterion for image pixels in the region of interest and that are the same for each image in the second image stack
Data Source
AI summary
A method of automatically adjusting lighting conditions improves the results of points from focus (PFF) 3D reconstruction. Multiple lighting levels are automatically found based on brightness criteria and an image stack is taken at each lighting level. In some embodiments, the number of light levels and their respective light settings may be determined based on trial exposure images acquired at a single global focus height which is a best height for an entire region of interest, rather than the best focus height for just the darkest or brightest image pixels in a region of interest. The results of 3D reconstruction at each selected light level are combined using a Z-height quality metric. In one embodiment, the PFF data point Z-height value that is to be associated with an X-Y location is selected based on that PFF data point having the best corresponding Z-height quality metric value at that X-Y location.