Nvidia corporation (20240161281). NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR simplified abstract
Contents
- 1 NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR
Organization Name
Inventor(s)
Wentao Zhu of Mountain View CA (US)
Andriy Myronenko of San Mateo CA (US)
NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161281 titled 'NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR
Simplified Explanation
The patent application describes a method using neural networks to perform registration among images by identifying common features and deriving correspondences between the images.
- Neural networks are trained to indicate registration of common features in at least two images.
- The networks simulate a registration process to generate a first correspondence.
- The first correspondence and the images are input into the neural network to derive a second correspondence of the common features.
Potential Applications
This technology could be applied in:
- Medical imaging for aligning images from different modalities.
- Satellite imaging for aligning images taken at different times or angles.
Problems Solved
This technology solves the following problems:
- Automating the image registration process, which can be time-consuming and prone to errors.
- Improving the accuracy of aligning images for various applications.
Benefits
The benefits of this technology include:
- Increased efficiency in image registration tasks.
- Enhanced accuracy in aligning images for analysis or comparison.
Potential Commercial Applications
Potential commercial applications of this technology include:
- Software tools for image processing and analysis.
- Medical imaging systems for diagnostic purposes.
Possible Prior Art
One possible prior art for image registration techniques is the use of feature-based methods that rely on identifying key points in images and matching them to perform registration.
What are the limitations of using neural networks for image registration?
Neural networks may require a large amount of training data to accurately identify common features in images. Additionally, the performance of neural networks in image registration tasks may vary depending on the complexity of the images.
How does this technology compare to traditional image registration methods?
Traditional image registration methods may rely on manual intervention or predefined algorithms, while this technology leverages neural networks to automatically learn and derive correspondences between images.
Original Abstract Submitted
apparatuses, systems, and techniques to perform registration among images. in at least one embodiment, one or more neural networks are trained to indicate registration of features in common among at least two images by generating a first correspondence by simulating a registration process of registering an image and applying the at least two images and the first correspondence to a neural network to derive a second correspondence of the features in common among the at least two images.
