17990366. METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR ACQUIRING IMAGE simplified abstract (Dell Products L.P.)
Contents
- 1 METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR ACQUIRING IMAGE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR ACQUIRING IMAGE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Image Acquisition Technology
- 1.13 Original Abstract Submitted
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR ACQUIRING IMAGE
Organization Name
Inventor(s)
METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR ACQUIRING IMAGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17990366 titled 'METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR ACQUIRING IMAGE
Simplified Explanation
The patent application describes a method for acquiring an image using a capsule neural network model to distill an original image set and generate a distilled image set. The method involves acquiring features of images and determining similarities to find matching images.
- Acquiring images through a capsule neural network model
- Distilling original image set to generate a distilled image set
- Acquiring features of images and determining similarities
- Finding matching images based on similarities
Key Features and Innovation
- Utilizes a capsule neural network model for image acquisition
- Distills images to create a new set for analysis
- Matches images based on similarities in features
- Improves image acquisition process through neural network technology
Potential Applications
- Image recognition and classification systems
- Medical imaging for diagnosis and analysis
- Surveillance and security systems for identifying individuals
- Quality control in manufacturing processes
Problems Solved
- Enhances image acquisition accuracy
- Improves image matching capabilities
- Streamlines image analysis processes
- Increases efficiency in image-based tasks
Benefits
- Higher accuracy in image acquisition
- Faster image matching and analysis
- Enhanced performance in various applications
- Improved decision-making based on image data
Commercial Applications
Advanced Image Recognition Technology for Enhanced Security and Analysis This technology can be applied in security systems, medical imaging, and manufacturing processes to improve efficiency and accuracy in image-related tasks.
Prior Art
No prior art information available at this time.
Frequently Updated Research
No frequently updated research available at this time.
Questions about Image Acquisition Technology
How does the capsule neural network model improve image acquisition compared to traditional methods?
The capsule neural network model enhances image acquisition by distilling images and extracting features to find similarities for matching, resulting in more accurate and efficient image analysis.
What are the potential limitations of using a capsule neural network model for image acquisition?
The potential limitations of using a capsule neural network model for image acquisition may include computational complexity, training data requirements, and model interpretability challenges.
Original Abstract Submitted
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for acquiring an image. The method includes distilling an original image set through a capsule neural network model to generate a distilled image set, wherein the distilled image set includes a plurality of distilled images. The method further includes acquiring a first feature of a first image through the capsule neural network model. The method further includes acquiring a plurality of distilling features of the plurality of distilled images respectively through the capsule neural network model. The method further includes determining a plurality of similarities between the first feature and the plurality of distilling features respectively. The method further includes acquiring at least one original image matching the first image based on the plurality of similarities.
