Machine Vision Parameter Estimation Using Multiple Examples
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Solution Overview
Problem
Current machine vision systems require manual and time-consuming refinement of object recognition models, leading to suboptimal performance and increased execution times due to the use of single-image or CAD-based parameter estimation, which can result in unstable model points and inefficient search processes.
Innovation Solution
A method for automating the refinement of object recognition models by using multiple images to identify stable model points and estimating level-specific parameters, including combining and decision functions to update model contours and parameters, thereby improving model robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual refinement of object recognition models is performed, then model accuracy can be improved, but time consumption and complexity increase significantly
Solution Approach 1:
The system automatically refines model parameters by processing multiple example images and computing optimal values without user intervention. The refinement process serves itself by using the input images to generate improved model parameters, eliminating the need for manual user refinement while maintaining or improving accuracy.
Solution Approach 2:
The system performs preliminary refinement by processing example images before actual object recognition tasks. By pre-computing optimal model parameters from example images, the system prepares the model in advance, avoiding time-consuming manual refinement during operational use.
2Ease of manufacture
If single-image parameter estimation is used, then setup process is simple, but model robustness and detection accuracy deteriorate
Solution Approach 1:
The system uses multiple example images to estimate model parameters, making the parameter estimation process more universal and adaptable to various lighting conditions, object variations, and environmental factors. This multi-image approach creates a more robust model that performs reliably across different scenarios while maintaining ease of setup through automated processing.
Solution Approach 2:
The system combines information from multiple example images to generate optimized model parameters. By merging data from multiple sources, the system creates a more comprehensive and robust model representation that captures various object appearances and conditions, improving reliability without complicating the setup process.
3Measurement precision
If manual model parameter optimization is performed, then detection accuracy can be improved, but execution time increases due to complex processing
Solution Approach 1:
The system performs parameter optimization in advance by processing example images before actual detection tasks. The computationally intensive optimization is done preliminarily during model creation, allowing fast execution during operational use without sacrificing detection accuracy.
Solution Approach 2:
The system creates an optimized model representation by processing example images and generating refined parameters that capture essential object characteristics. This optimized model copy can then be used for fast detection without requiring repeated complex processing of original images.
4Extent of automation
If automated parameter estimation is implemented, then user interaction is minimized, but parameter optimality may be compromised without expert tuning
Solution Approach 1:
The automated parameter estimation system serves itself by using multiple example images to compute optimal parameters without requiring expert user intervention. The system independently analyzes the example images and generates optimized model parameters, achieving both high automation and parameter optimality simultaneously.
Solution Approach 2:
The system uses feedback from multiple example images to iteratively refine parameter estimates. By processing multiple examples and using their collective information, the automated system achieves parameter optimality that would otherwise require expert tuning, while maintaining full automation.
Data Source
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AI summary
The present disclosure provides methods that estimate or improve various parameters of an object recognition model to improve runtime, accuracy and robustness while minimizing the required user interaction to optimize these values. In one aspect of the invention, methods are defined to generate object recognition models with refined contours. A further method is defined by the invention to estimate level-specific parameters for the object recognition algorithms.