Thermal-Depth Fusion Imaging Alignment via Visible Light Intermediary
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Solution Overview
Problem
Current thermal-depth fusion methods fail to provide robust, user-friendly, and environmentally independent authentication solutions due to limitations in data alignment and integration of diverse imaging modalities, leading to inaccuracies and usability issues in various environmental conditions.
Innovation Solution
The integration of machine learning techniques for per-pixel transformations and generative data fusion aligns thermal and depth images, establishing a geometric transformation that minimizes deformation errors, allowing for the creation of unique biometric signatures based on thermal and depth data, even in partial occlusions and imperfect conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional thermal-depth fusion methods are used for alignment, then the process is simpler, but alignment accuracy and robustness deteriorate due to ambiguities in identifying corresponding points between thermal and depth images
Solution Approach 1:
The patent introduces visible light images as an intermediary modality to bridge the alignment between thermal and depth images. The visible light image serves as a common reference that both thermal and depth images can be aligned to, resolving the correspondence ambiguity problem. This three-step alignment process (thermal to visible, depth to visible, then thermal to depth) significantly improves alignment accuracy compared to direct thermal-depth matching.
2Reliability
If simple superposition of thermal and depth data is used, then the processing is faster, but the reliability of biometric authentication deteriorates under environmental changes and occlusions
Solution Approach 1:
The patent fuses thermal and depth data into a three-dimensional point cloud representation, adding spatial dimensionality to the authentication process. By mapping thermal values onto 3D depth points and using visible light image guidance, the system creates a multi-dimensional biometric signature that is more robust to environmental variations and occlusions compared to simple 2D superposition.
3Adaptability or versatility
If thermal imaging is used as a segmentation prior, then human figure identification becomes easier, but the adaptability to different environmental conditions and occlusions is limited
Solution Approach 1:
The patent merges thermal segmentation capabilities with depth-based 3D reconstruction and visible light image guidance. By combining multiple imaging modalities rather than relying solely on thermal segmentation, the system achieves better adaptability to different environmental conditions and occlusion scenarios while maintaining automated operation.
Data Source
AI summary
An imaging system is provided. The imaging system includes a 3D image capture device, which is configured to capture a depth image of an object, and a thermal image capture device, which is configured to capture a thermal image of the object. The imaging system also includes a processing system, which is coupled with the 3D image capture device and the thermal image capture device. The processing system is configured to process the depth image and the thermal image to produce a thermal-depth fusion image by aligning the thermal image with the depth image, and assigning a thermal value derived from the thermal image to a plurality of points of the depth image.