3D Image Acquisition Parameter Tuning Under Thermal Constraints
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Solution Overview
Problem
Conventional stereo vision systems are limited in their ability to create accurate three-dimensional data reconstructions of objects and scenes, and existing machine vision systems face challenges in optimizing image acquisition parameters due to user-configurable and non-user-configurable settings, thermal considerations, and other operational constraints.
Innovation Solution
A system and method for determining optimal operational parameters for image acquisition in machine vision systems, including adjustable projector and imaging parameters, that enforce constraints to minimize noise and comply with mechanical and thermal limits, using a processor to adjust parameters based on user preferences and system constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stereo vision systems are used for 3D reconstruction, then the system structure is simple, but the measurement precision and reliability of 3D data are insufficient
Solution Approach 1:
The system performs preliminary optimization of acquisition parameters before actual image capture. The processor determines optimal projector and camera parameters (exposure time, frame rate, pattern speed) based on constraints, ensuring that when images are captured, the parameters are already optimized for maximum 3D reconstruction accuracy.
Solution Approach 2:
The system dynamically adjusts multiple parameters including projector illumination intensity, camera exposure time, frame rate, and pattern projection speed. This dynamic parameter optimization allows the system to adapt to different scenes and constraints, significantly improving measurement precision beyond fixed conventional stereo systems.
2Measurement precision
If image acquisition parameters are optimized to minimize noise in 3D data, then measurement precision improves, but the device complexity increases due to multiple adjustable parameters and constraints
Solution Approach 1:
The system performs self-optimization by automatically determining optimal acquisition parameters without user intervention. The processor autonomously analyzes constraints (thermal limits, mechanical limitations, illumination power) and calculates the best parameter combinations, making the system self-configuring and reducing operational complexity.
Solution Approach 2:
The system uses constraint feedback to iteratively refine parameter selection. The processor evaluates parameters against multiple constraints and adjusts them accordingly, creating a feedback loop that ensures optimal noise reduction while respecting system limitations.
3Reliability
If thermal constraints and illumination power limitations are enforced, then system reliability and safety improve, but the productivity and speed of image acquisition may be reduced
Solution Approach 1:
The system changes multiple parameters simultaneously to maintain productivity within thermal constraints. By adjusting projector power, camera exposure time, and frame rate in combination, the system finds optimal operating points that respect thermal limits while maximizing acquisition speed, rather than simply reducing speed to meet constraints.
Solution Approach 2:
The system can implement periodic illumination patterns and alternating acquisition cycles that allow thermal management while maintaining high average productivity. By using duty cycles and periodic patterns, the system achieves reliable operation without continuously reducing acquisition speed.
4Ease of operation
If user-configurable parameters are allowed for flexibility, then ease of operation improves, but the device complexity increases due to the need to manage both user-configurable and non-user-configurable parameters
Solution Approach 1:
The system provides self-service by automatically optimizing parameters that users configure. When users set high-level preferences or constraints, the processor autonomously determines the specific parameter values, shielding users from complexity while maintaining flexibility.
Solution Approach 2:
The system creates a universal parameter optimization framework that handles both user-configurable and system-managed parameters through a single integrated processor. This multi-functional approach unifies parameter management, allowing users to interact with a simplified interface while the system manages the full complexity of multiple parameter types.
Data Source
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AI summary
The techniques described herein relate to methods, apparatus, and computer readable media configured to determine parameters for image acquisition. One or more image sensors are each arranged to capture a set of images of a scene, and each image sensor comprises a set of adjustable imaging parameters. A projector is configured to project a moving pattern on the scene, wherein the projector comprises a set of adjustable projector parameters. The set of adjustable projector parameters and the set of adjustable imaging parameters are determined, based on a set of one or more constraints, to reduce noise in 3D data generated based on the set of images.