Polarization Image Processing for Surface Roughness Measurement
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Solution Overview
Problem
The challenge is to accurately recognize the state of a target object in a captured image, especially under varying light conditions, where changes in silhouette due to brightness and lighting can lead to incorrect identification and reduced accuracy in information processing.
Innovation Solution
An information processing apparatus that acquires data of polarization images in multiple azimuths, derives the phase angle providing maximum polarization luminance, and evaluates surface roughness based on the change in specular reflectance, generating data to improve the accuracy of target object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image capture methods are used under varying light conditions, then the imaging process remains simple, but the recognition accuracy of the target object deteriorates due to changes in silhouette color and luminance distribution
Solution Approach 1:
The patent changes the parameter of light polarization state by capturing images at multiple polarization angles (0°, 45°, 90°, 135°) instead of relying on fixed conventional imaging. This allows the system to extract polarization information that remains stable under varying illumination conditions, thereby improving recognition accuracy without requiring complex hardware modifications beyond the imaging device
Solution Approach 2:
The patent introduces polarization information as an intermediary parameter between the target object and the recognition system. By capturing polarization images at multiple angles and deriving polarization luminance and phase angle, the system creates a stable intermediate representation that is less sensitive to lighting variations, thus improving recognition accuracy
2Measurement precision
If multiple polarization images are captured at different azimuths to improve recognition accuracy, then recognition accuracy improves, but the time and complexity of the imaging process increase
Solution Approach 1:
The patent uses periodic action by capturing polarization images at discrete, regularly spaced azimuths (0°, 45°, 90°, 135°) rather than continuous scanning. This periodic sampling approach efficiently captures the essential polarization characteristics needed for surface roughness measurement while minimizing acquisition time
Solution Approach 2:
The patent applies partial action by selecting specific critical azimuths (0°, 45°, 90°, 135°) that provide sufficient polarization information for accurate surface roughness measurement, rather than capturing images at all possible angles. This selective approach achieves the required measurement precision with reduced time investment
3Reliability
If polarization images are used to distinguish specular and diffuse reflections, then the impact of lighting variations is reduced, but the data processing complexity increases
Solution Approach 1:
The patent replaces complex mechanical or computational methods for separating specular and diffuse reflections with a simpler polarization-based approach. By utilizing the inherent polarization differences between specular and diffuse reflected light and applying polarization synthesis processing, the system achieves reliable separation with relatively simple data processing
Solution Approach 2:
The patent changes the parameter space by working in the polarization domain rather than directly in the intensity domain. By deriving polarization luminance and phase angle from polarization images and using these transformed parameters for surface roughness evaluation, the system achieves more reliable results with manageable processing complexity
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 enables high-accuracy recognition of the target object's state by distinguishing between specular and diffuse reflections, effectively mitigating the impact of lighting variations and improving the reliability of subsequent information processing tasks.
Implementation Method 1
data of polarization images in a plurality of azimuths
Implementation Method 2
change in the specular reflectance
Data Source
AI summary
An information processing apparatus determines a plurality of sampling points in a range of a target object's silhouette having different specular reflectances in polarization images in a plurality of azimuths, extracts polarization luminance at the sampling points in question, and derives change in luminance relative to the polarization azimuth, thus acquiring, as a phase angle, the azimuth that provides the highest luminance. Then, the information processing apparatus evaluates a characteristic of the change in phase angle relative to the change in the specular reflectance, thus identifying a subject's surface roughness. Further, the information processing apparatus generates data to be output by performing a process according to the surface roughness and outputs the generated data.