Deflection Metric Encoding for 3D Projection Data
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Solution Overview
Problem
Existing methods for providing 3D information to machine learning classifiers for object classification from 2D projections are inefficient due to reliance on multiple data channels, increasing computational load and reducing convergence during training.
Innovation Solution
A method that encodes a deflection metric indicative of the angle between the principal axis of a projection model and a projection ray for each point in the image data, allowing the machine learning classifier to infer 3D information with minimal additional data channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data channels (x-, y-, z-coordinates, pitch, yaw) are provided to the machine learning classifier, then 3D positional information is improved, but computational load increases and training convergence deteriorates
Solution Approach 1:
The patent extracts only the essential 3D positional information needed for classification by providing a single deflection metric data channel instead of multiple coordinate channels. This extraction principle reduces computational complexity while maintaining the critical 3D spatial context needed for accurate object classification.
Solution Approach 2:
The patent transforms multiple 3D coordinate dimensions (x, y, z, pitch, yaw) into a single angular dimension represented by the deflection metric. This dimensional transformation consolidates redundant information into one efficient data channel that preserves 3D spatial relationships without the computational overhead of multiple channels.
2Measurement precision
If multiple data channels (x-, y-, z-coordinates, pitch, yaw) are provided to the machine learning classifier, then 3D positional information is improved, but training convergence deteriorates
Solution Approach 1:
The patent extracts only the essential 3D positional information needed for classification by providing a single deflection metric data channel instead of multiple coordinate channels. This extraction principle reduces computational complexity while maintaining the critical 3D spatial context needed for accurate object classification.
Solution Approach 2:
The patent changes the parameter representation from multiple Cartesian coordinates and angular measurements to a single deflection metric parameter. This parameter transformation simplifies the input data structure, enabling faster processing and improved training convergence while preserving the necessary 3D spatial information.
3Measurement precision
If 3D information is provided through multiple data channels, then object classification accuracy is improved, but data structure complexity increases
Solution Approach 1:
The patent merges multiple 3D information channels (coordinates, angles, pitch, yaw) into a single unified deflection metric data channel. This consolidation maintains comprehensive 3D spatial information while simplifying the data structure to a single array that integrates seamlessly with the image data for efficient processing.
Data Source
AI summary
A computer-implemented method for encoding projection properties associated with image data comprising the steps of determining a principal axis of a projection model for obtaining the image data from a scene, determining, for each point in the image data, a deflection metric indicative of an angle between the principal axis and a projection ray through said point, and encoding the deflection metric for each point in the image data as projection data.