Optical Topographic Imaging via Multi-Directional Illumination
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Solution Overview
Problem
Current optical fingerprinting methods rely on surface measurements and are limited in accurately capturing topographical features, especially when dealing with complex objects like biometric samples and machined barcodes, which require more comprehensive imaging techniques to analyze surface gradients and authenticity.
Innovation Solution
The method involves using multiple light sources to illuminate an object from different directions, capturing scattered light with a camera, and applying machine learning filters to generate topographic images, which can include albedo analysis and authenticity verification by integrating surface gradients and applying transformations to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple light sources are used to illuminate the object from different directions, then the measurement precision of topographical features is improved, but the device complexity increases
Solution Approach 1:
The imaging system divides the illumination task into multiple independent light sources positioned at different locations, with each light source contributing to capturing specific surface gradient information. This segmentation allows the complex task of capturing complete topography to be broken down into manageable components that can be processed independently and then combined.
Solution Approach 2:
The system transitions from single-direction illumination to multi-directional illumination by adding spatial dimension to the lighting arrangement. By positioning light sources at different locations around the object, the system captures surface gradients from multiple angles, enabling reconstruction of three-dimensional topography from two-dimensional images.
2Reliability
If machine learning filters are applied to filter generated images, then the reliability of topographical analysis is improved, but the processing time increases
Solution Approach 1:
The machine learning filters are trained in advance on labeled topographical data to recognize and filter out artifacts before the actual imaging process. This preliminary training allows the system to quickly and accurately identify and remove spurious information during image processing without requiring time-consuming real-time analysis.
Solution Approach 2:
The machine learning algorithms provide feedback during image processing by continuously evaluating generated images against trained models and adjusting filtering operations accordingly. This feedback mechanism enables the system to adaptively refine image quality and improve topographical analysis reliability while optimizing processing efficiency.
3Measurement precision
If surface gradients are integrated to generate topography, then the measurement precision of surface features is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system replaces complex mechanical 3D scanning methods with an optical approach that uses multiple light sources and cameras to capture surface gradients. By substituting mechanical measurement systems with optical field-based measurement, the system achieves comparable or superior precision while simplifying the overall measurement process and reducing mechanical complexity.
Solution Approach 2:
The imaging system is designed to perform multiple functions: capturing surface gradients, generating topography, and detecting artifacts all within a single integrated framework. This multi-functionality reduces the need for separate specialized equipment and simplifies the overall measurement process while maintaining high precision.
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 more accurate and reliable imaging of topographical features, improving the analysis of biometric samples and machined barcodes by capturing detailed surface information and authenticity through enhanced image processing and machine learning techniques.
Implementation Method 1
Each of a plurality of light sources directly illuminates the object from a different illumination direction... an image is generated from light scattered from the object
Implementation Method 2
an image is generated from light scattered from the object with a camera maintained in a stable configuration
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Methods and devices of studying a predefined portion of an object having a feature of interest are disclosed. The feature of interest defines a class of objects that includes the object. Light sources directly illuminate the object from different illumination directions. The light sources are maintained in a stable configuration relative to the object. For each illumination direction, an image is generated from light scattered from the object with a camera maintained in a stable configuration relative to the light sources. A methodology derived from machine learning for the class of objects is applied to filter the generated images are filtered for a characteristic consistent with the feature of interest. Surface gradients are determined from the filtered images and integrated to generate a topography of a surface of the object.