Multi-Device Surface Imaging via Composite Map Aggregation
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Solution Overview
Problem
Current imaging technologies for surface structure analysis are costly and impractical for broad consumer adoption, and mechanisms for aggregating and aligning image data on a large scale for enhanced surface informatics-based detection are underdeveloped.
Innovation Solution
A method and system using smart devices with cameras and sensors to capture and align image data over time, utilizing positional and orientation metadata to construct composite maps, allowing for efficient aggregation and temporal analysis of multi-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging technology is used for surface structure analysis, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system divides the imaging task across multiple consumer devices (smartphones, tablets, cameras) distributed in space, with each device capturing a portion of the target surface. The central server then aggregates these segmented images to reconstruct the complete surface structure, replacing a single complex imaging system with multiple simple devices.
Solution Approach 2:
The patent utilizes consumer devices that serve multiple functions (photography, communication, computing) for the specialized task of surface imaging. These devices are not dedicated imaging instruments but versatile consumer electronics that can perform surface structure analysis when coordinated through the system, reducing the need for specialized expensive equipment.
2Productivity
If multiple sensor devices are used to aggregate image data, then productivity is improved, but device complexity increases
Solution Approach 1:
A central server acts as an intermediary that coordinates multiple distributed imaging devices. The server receives images from various consumer devices, aligns them using metadata (GPS coordinates, orientation data, timestamps), and aggregates them into a unified surface model. This intermediary manages the complexity of coordinating multiple devices while enabling high productivity through parallel data collection.
Solution Approach 2:
The system uses metadata from each captured image (positioning data, orientation, timestamp) as feedback to automatically align and register images from different devices. This feedback mechanism enables the system to correct for variations in device position and orientation, maintaining measurement precision while coordinating multiple devices efficiently.
3Loss of information
If image data is captured and aggregated from distributed devices, then loss of information is reduced, but measurement precision requirements increase
Solution Approach 1:
The system captures and stores metadata (GPS coordinates, orientation angles, timestamps) at the moment of image capture as a preliminary action. This pre-captured information is then used during the aggregation phase to automatically align images from different devices, ensuring that positional and temporal precision requirements are met without requiring post-capture calibration or adjustment.
Data Source
AI summary
At a central controlling system, a composite map is constructed based on a previous dataset that includes (A) images of a target obtained by imaging devices at a first time, and (B) respective meta data associated with the images. Imaging devices collect image data during a second time after the first time. In accordance with the composite map, the imaging devices obtain images of the target and associate meta data with the images. The images and respective meta data are communicated to the central controlling system. The positions and orientations of the imaging devices when the respective images were obtained, and when the images were obtained, are used to index the images against the target, thereby aggregating multi-dimensional data for the target. Temporal information about a characteristic of the target over time is then extracted from the aggregated multi-dimensional data.