Multi-Camera Image Alignment Using AI-Generated Transform Parameters
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Solution Overview
Problem
Supervised learning-based deep learning models require large amounts of ground truth image data and struggle to provide consistent outputs when using cameras with different properties, such as varying color, scale, and distortion, which is particularly challenging for autonomous mobility devices.
Innovation Solution
An image transformation AI model using adversarial training generates aligning parameters to transform image data from cameras with different properties, including crop parameters and projection matrices, to align and adjust brightness, enabling consistent inference performance across multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If supervised learning-based deep learning models use ground truth image data from multiple cameras with different properties, then the model can process diverse camera inputs, but the output consistency deteriorates due to differences in color, scale, and distortion
Solution Approach 1:
The patent applies preliminary action by transforming image data from multiple cameras with different properties into a unified coordinate system and applying consistent processing parameters before feeding them into the deep learning model. This preprocessing step ensures that all camera inputs are standardized in advance, eliminating variations in color, scale, and distortion that would otherwise cause inconsistent outputs.
Solution Approach 2:
The patent changes parameters by adjusting image data through coordinate transformations and applying correction factors for color, scale, and distortion. By modifying these parameters to align all camera inputs to a common reference frame, the system maintains output consistency while preserving the ability to process diverse camera inputs.
2Device complexity
If a single deep learning model processes image data from cameras with different geometric properties, then device complexity is reduced, but manufacturing precision deteriorates due to alignment issues
Solution Approach 1:
The patent introduces an intermediary coordinate transformation system that acts as a mediator between cameras with different geometric properties and the single deep learning model. This intermediary layer performs the necessary alignment and normalization operations, allowing the model to remain simple while achieving precise image alignment through the transformation pipeline.
3Reliability
If multiple ground truth image datasets are collected for training, then model performance improves, but economic cost increases due to the enormous amount of data required
Solution Approach 1:
The patent applies universality by creating a unified coordinate system and transformation framework that can handle image data from any camera with different properties. This universal approach allows the system to use a single ground truth dataset effectively for training, as the transformation framework can adapt any camera input to match the training data distribution, reducing the need for separate datasets for each camera type.
Data Source
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AI summary
A method for generating aligned image data through an aligning parameter generated by an image transformation artificial intelligence (AI) model includes, through an encoder of the image transformation AI model, generating at least one or more aligning parameters from a first camera property and a second camera property related to a first camera and a second camera respectively. The method also includes, through an image transformer of the image transformation AI model, transforming, based on the at least one aligning parameter and a brightness parameter, first image data photographed by the first camera to be aligned with second image data photographed by the second camera. The method also includes training the encoder and a discriminator of the image transformation AI model by adversarial training. The image transformation AI model discriminates between the transformed first image data and the second image data.