18405932. NEURAL NETWORK FOR IMAGE REGISTRATION AND IMAGE SEGMENTATION TRAINED USING A REGISTRATION SIMULATOR simplified abstract (NVIDIA Corporation)
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 Unanswered Questions
- 1.11 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 18405932 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 neural 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 various fields such as medical imaging, satellite image analysis, and computer vision for object recognition.
Problems Solved
This technology solves the problem of accurately aligning and registering images with common features, which is crucial for tasks like image stitching and 3D reconstruction.
Benefits
The benefits of this technology include improved accuracy and efficiency in image registration, leading to better image analysis and visualization.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of software tools for image processing and analysis in industries such as healthcare, remote sensing, and robotics.
Possible Prior Art
Prior art in image registration includes traditional methods such as feature-based registration and intensity-based registration, which may not be as efficient or accurate as the neural network-based approach described in this patent application.
Unanswered Questions
How does this technology compare to existing image registration methods?
The article does not provide a direct comparison with traditional image registration techniques, leaving the reader to wonder about the relative advantages and disadvantages of this neural network-based approach.
What are the limitations of using neural networks for image registration?
The article does not address any potential limitations or challenges that may arise when implementing neural networks for image registration, leaving room for further exploration of this topic.
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.
