Nvidia corporation (20240161282). 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 20240161282 titled 'NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR
Simplified Explanation
The patent application describes apparatuses, systems, and techniques for image registration using neural networks. The neural networks are trained to identify common features among images and generate correspondences between them.
- Neural networks are trained to indicate registration of features in common among at least two images.
- The neural networks simulate a registration process to generate a first correspondence between the images.
- 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 can be applied in:
- Medical imaging for aligning images from different modalities.
- Satellite imaging for aligning images taken at different times for change detection.
Problems Solved
This technology solves the following problems:
- Manual image registration processes are time-consuming and prone to errors.
- Automating the image registration process can improve accuracy and efficiency.
Benefits
The benefits of this technology include:
- Improved accuracy in aligning images.
- Time-saving by automating the registration process.
Potential Commercial Applications
This technology can be commercially applied in:
- Healthcare industry for medical image registration software.
- Remote sensing industry for satellite image processing tools.
Possible Prior Art
One possible prior art is the use of traditional image registration algorithms based on feature matching and transformation estimation.
What are the limitations of this technology in real-world applications?
This technology may face limitations in:
- Dealing with large-scale image datasets efficiently.
- Handling complex image transformations accurately.
How does this technology compare to existing image registration methods?
This technology offers:
- Improved accuracy through neural network-based registration.
- Automation of the registration process for increased efficiency.
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.
