17832400. GENERATING MASK INFORMATION simplified abstract (NVIDIA Corporation)
Contents
- 1 GENERATING MASK INFORMATION
GENERATING MASK INFORMATION
Organization Name
Inventor(s)
Seung Wook Kim of Toronto (CA)
Karsten Julian Kreis of Vancouver (CA)
Antonio Torralba Barriuso of Somerville MA (US)
GENERATING MASK INFORMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17832400 titled 'GENERATING MASK INFORMATION
Simplified Explanation
The abstract describes apparatuses, systems, and techniques for annotating images using neural models. In one embodiment, neural networks generate mask information from labels of objects within images identified by other neural networks.
- Neural networks are used to generate mask information from object labels in images.
- The technology involves multiple neural networks working together to identify and annotate objects within images.
Potential Applications
This technology could be applied in various fields such as:
- Image recognition and classification
- Object detection in autonomous vehicles
- Medical imaging for identifying and analyzing anomalies
Problems Solved
This technology helps in:
- Automating the process of image annotation
- Improving accuracy and efficiency in object detection
- Enhancing the capabilities of neural networks in image analysis
Benefits
The benefits of this technology include:
- Faster and more accurate image annotation
- Enhanced object detection capabilities
- Improved performance of neural networks in image analysis tasks
Potential Commercial Applications
The potential commercial applications of this technology include:
- Image editing software with advanced annotation features
- Security systems for object detection and tracking
- Medical imaging software for diagnostic purposes
Possible Prior Art
One possible prior art in this field is the use of convolutional neural networks for image segmentation and object detection. Researchers have been exploring similar techniques to improve the accuracy and efficiency of image annotation and analysis.
Unanswered Questions
How does this technology compare to existing image annotation methods?
This article does not provide a direct comparison with traditional image annotation methods or other neural network-based approaches. It would be interesting to see a performance comparison in terms of accuracy, speed, and scalability.
What are the limitations of this technology in real-world applications?
The article does not address the potential limitations or challenges of implementing this technology in practical scenarios. Understanding the constraints and drawbacks of the system would be crucial for assessing its suitability for different use cases.
Original Abstract Submitted
Apparatuses, systems, and techniques to annotate images using neural models. In at least one embodiment, neural networks generate mask information from labels of one or more objects within one or more images identified by one or more other neural networks.
