Computational Imaging Device Corner Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional imaging systems are not optimized for robust corner detection, which is crucial for computer vision tasks like SLAM, navigation, and augmented reality, and they lack the design flexibility and operational robustness provided by computational imaging devices.
Innovation Solution
A computational imaging device with an engineered pupil function, such as a phase mask or high-order separable pupil function, is used to enhance corner detection capabilities, allowing for improved performance in corner detection, localization, and other computer vision tasks by modulating the phase of wavefronts and extending the operational range of imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional imaging systems are used, then the system structure is simple and easy to manufacture, but corner detection robustness and operational range are insufficient
Solution Approach 1:
The patent modifies the pupil function parameters of the imaging system by introducing engineered phase masks (cubic phase, high-order separable pupil functions) to change the optical transfer function. This parameter change enables the system to maintain corner detection capability across a broader range of defocus conditions, thereby improving reliability without requiring fundamental structural changes to the imaging system.
Solution Approach 2:
The patent introduces an intermediary computational processing stage that works in conjunction with the engineered pupil function. This intermediary computational imaging approach processes the captured images to enhance corner detection performance, allowing the system to achieve improved robustness while maintaining relatively simple optical hardware structure.
2Adaptability or versatility
If traditional imaging systems are used, then the device complexity is low, but depth of field and operational range are limited
Solution Approach 1:
The patent changes the optical parameters by implementing engineered pupil functions with specific phase profiles (cubic phase masks, high-order separable functions). These parameter changes extend the depth of field and allow the imaging system to maintain adequate corner detection performance across a wider range of object distances, thereby increasing operational range and adaptability.
Solution Approach 2:
The patent creates a dynamic imaging system where the effective depth of field can be adjusted through computational processing combined with the engineered pupil function. This dynamic approach allows the system to adapt to different focusing conditions and maintain operational effectiveness across varying distances, enhancing versatility without requiring multiple physical lenses or complex mechanical adjustments.
3Measurement precision
If computational imaging devices with engineered pupil functions are used, then corner detection and depth of field are improved, but device complexity increases
Solution Approach 1:
The patent improves measurement precision for corner detection by carefully engineering the pupil function parameters. Specific phase mask designs (cubic phase with optimized coefficients, high-order separable functions) are selected to maximize corner detection accuracy while minimizing unnecessary complexity. The parameter optimization focuses on achieving the minimum required performance threshold rather than maximizing complexity.
Solution Approach 2:
The patent applies local quality optimization by designing pupil functions that specifically enhance the detection of corner features while maintaining other imaging qualities. The engineered phase masks are tailored to create point spread functions that preserve corner information, applying complexity only where needed for the specific measurement task rather than uniformly across all imaging parameters.
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 computational imaging device achieves improved corner detection and increased depth of field, maintaining the minimum required number of keypoints even under defocus conditions, thereby enhancing the operational range and robustness for applications like autonomous vehicles and augmented reality.
Implementation Method 1
A computational imaging device with an engineered pupil function, such as a phase mask or high-order separable pupil function, is used to enhance corner detection capabilities, allowing for improved performance in corner detection, localization, and other computer vision tasks by modulating the phase of wavefronts
Implementation Method 2
An imaging lens forms an optical image of an object in a photodetector array
Data Source
AI summary
A computational imaging device and method are described, wherein the computational imaging device is operable to perform corner detection and other computer vision tasks more efficiently and/or more robustly than traditional imaging devices. In addition, methods are described operable to jointly optimize the computational imaging device and the corner detection task.