18405922. 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 18405922 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 between the images.
- The first correspondence and the images are then used to derive a second correspondence of the common features.
Potential Applications
This technology could be applied in various fields such as medical imaging, satellite imagery analysis, and computer vision for object recognition.
Problems Solved
This technology helps in accurately aligning and comparing images, which is crucial in tasks like image stitching, 3D reconstruction, and image classification.
Benefits
The use of neural networks for image registration can improve the efficiency and accuracy of image processing tasks, leading to better results in various applications.
Potential Commercial Applications
Potential commercial applications of this technology include medical imaging software, remote sensing applications, and automated quality control systems in manufacturing.
Possible Prior Art
Prior art in image registration includes traditional methods like feature matching, optical flow, and template matching. However, the use of neural networks for image registration is a novel approach that offers advantages in terms of accuracy and automation.
Unanswered Questions
How does this technology compare to traditional image registration methods?
The article does not provide a direct comparison between this neural network-based approach and traditional methods like feature matching or optical flow. It would be helpful to understand the specific advantages and limitations of each approach.
What are the computational requirements for training and using the neural networks in this method?
The article does not delve into the computational resources needed for training and utilizing the neural networks for image registration. Understanding the computational costs involved could be crucial for implementing this technology in real-world applications.
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.
