Distification Method for 2D and 3D Image Ensemble Prediction
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Solution Overview
Problem
The incompatibility and disparate nature of various image and video file formats used by different manufacturers create challenges for computer systems to analyze and coordinate 2D and 3D imagery, leading to difficulties in interoperability and accuracy in predictive modeling and classification tasks.
Innovation Solution
The Distification method transforms unstructured 3D imagery into a standardized format by generating a 2D image matrix with associated feature vectors, allowing for improved compatibility and accuracy in predictive models, enabling the integration of 2D and 3D imagery for enhanced prediction and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different image and video file formats are used by different manufacturers, then device compatibility and manufacturer independence are improved, but system interoperability and data coordination become more difficult
Solution Approach 1:
The patent introduces a standardized intermediate data structure that acts as a mediator between different image and video formats. This intermediate structure enables uniform processing and analysis of diverse formats without requiring direct compatibility between all format pairs, thus maintaining manufacturer independence while improving system interoperability.
Solution Approach 2:
The patent transforms various image and video formats into a unified parameter-based representation. By converting diverse formats into standardized parameters and data structures, the system can process different formats consistently, resolving the interoperability issues caused by format diversity.
2Measurement precision
If 3D image data is stored with high granularity (tens-of-thousands of points), then image detail and precision are improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the dense 3D point cloud data into meaningful groups or clusters based on spatial relationships and features. This segmentation reduces the complexity of processing individual points while preserving the detailed information, as operations can be performed on segmented groups rather than individual points.
Solution Approach 2:
The patent extracts only the essential features and characteristics from the dense 3D point data that are necessary for predictive modeling. By taking out and retaining only the critical information, the system maintains measurement precision while reducing processing complexity through feature selection and dimensionality reduction.
3Adaptability or versatility
If 3D points are stored in unstructured sequences, then data flexibility and storage efficiency are improved, but comparative analysis between multiple 3D images becomes difficult
Solution Approach 1:
The patent applies preliminary structuring and normalization to 3D point sequences before they are used for comparative analysis. By organizing the data in advance into consistent formats and coordinate systems, the system maintains data flexibility for storage while enabling accurate comparative analysis when needed.
Data Source
AI summary
Systems and methods are described for generating an enhanced prediction from a 2D and 3D image-based ensemble model. In various embodiments, a computing device can be configured to obtain one or more sets of 2D and 3D images and to standardize each of the 2D and 3D images to allow for comparison and interoperability. Corresponding 2D3D image pairs can be determined from the standardized 2D and 3D pairs where the 2D and 3D images correspond based on a common attribute, such as a similar timestamp or time value. The enhanced prediction can use separate underlying 2D and 3D prediction models where the 2D and 3D images of a 2D3D pair are each input to the respective underlying 2D and 3D prediction models to generate respective 2D and 3D predict actions.