Nvidia corporation (20240161282). NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR simplified abstract

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NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR

Organization Name

nvidia corporation

Inventor(s)

Wentao Zhu of Mountain View CA (US)

Daguang Xu of Potomac MD (US)

Andriy Myronenko of San Mateo CA (US)

Ziyue Xu of Reston VA (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.