Sight Ray Image Representation for Camera-Invariant CNN Input
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Solution Overview
Problem
Conventional computer vision algorithms struggle with domain adaptation due to camera distortions, requiring extensive retraining and labeling when switching between different camera types, limiting their applicability and efficiency.
Innovation Solution
Transforming image data into a sight ray representation and converting it into a grid-based format, followed by normalization and application of a convolutional neural network (CNN) with dropout, enabling robustness to camera distortions and allowing reuse of trained models across different camera types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional computer vision algorithms are adapted to different camera distortions through domain adaptation training, then the model can handle multiple camera types, but extensive retraining and labeling are required for each new camera type
Solution Approach 1:
The patent introduces an intermediary representation called 'sight ray representation' that acts as a mediator between the image data and the machine learning model. This representation transforms image data into a camera-agnostic format by representing pixels as 3D rays in space, thereby decoupling the model from camera-specific distortions and eliminating the need for extensive retraining when switching between camera types
Solution Approach 2:
The patent changes the parameter representation of image data from 2D pixel coordinates to 3D ray parameters (origin, direction, color). This parameter transformation converts camera-distorted 2D images into a unified 3D sight ray space, allowing the same machine learning model to process images from different camera types without retraining
2Reliability
If conventional computer vision algorithms are retrained on images with different camera distortions using special training procedures, then domain-invariant features can be learned, but the process requires extensive labeling and computational resources
Solution Approach 1:
The patent applies preliminary action by performing sight ray transformation on the input image data before it enters the machine learning model. This preprocessing step converts all camera distortions into a unified 3D representation, so that the model receives pre-normalized data and does not require complex domain adaptation training procedures to achieve domain invariance
Solution Approach 2:
The patent replaces the mechanical approach of retraining models with different domain adaptation procedures by substituting it with a mathematical transformation (sight ray representation). This substitution eliminates the need for iterative training processes and complex labeling requirements while achieving the same goal of domain invariance
Data Source
AI summary
A method for processing image data for the application of a machine learning model. The method includes: ascertaining image data, wherein the image data result from image acquisition with a camera; transforming the image data into a sight ray representation; providing an input for the machine learning model based on the image data in the sight ray representation.