Intel corporation (20240104380). HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS simplified abstract
Contents
- 1 HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS - 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 Original Abstract Submitted
HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS
Organization Name
Inventor(s)
Anthony Rhodes of Portland OR (US)
Manan Goel of Portland OR (US)
HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104380 titled 'HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS
Simplified Explanation
The patent application describes a technology that trains a neural network using video data to output pixel-level segmentation of objects depicted in the video. The technology involves determining a boundary loss function for the neural network and selecting weights based on this function.
- Neural network trained using video data for pixel-level segmentation of objects
- Boundary loss function used to determine weights for the neural network
- Tensor decomposition on initial feature set to obtain reduced feature set for segmentation
Potential Applications
This technology could be applied in various fields such as:
- Autonomous driving for object detection and tracking
- Medical imaging for identifying and segmenting specific areas in scans
- Surveillance systems for monitoring and analyzing activities in real-time
Problems Solved
This technology addresses the following issues:
- Accurate and efficient segmentation of objects in video data
- Improved performance of neural networks in pixel-level segmentation tasks
- Reduction of computational complexity in processing video data
Benefits
The benefits of this technology include:
- Enhanced accuracy and precision in object segmentation
- Faster processing and analysis of video data
- Improved performance of neural networks in pixel-level segmentation tasks
Potential Commercial Applications
The technology could find commercial applications in:
- Video analytics software for security and surveillance
- Medical imaging software for diagnostic purposes
- Autonomous vehicle systems for object detection and tracking
Possible Prior Art
One possible prior art for this technology could be the use of convolutional neural networks for image segmentation tasks. Another could be the application of tensor decomposition techniques in machine learning for feature extraction.
Unanswered Questions
How does this technology compare to existing methods for pixel-level segmentation in video data processing?
This article does not provide a direct comparison with other methods or technologies currently used for pixel-level segmentation in video data processing. It would be interesting to know how this technology stacks up against existing approaches in terms of accuracy, efficiency, and computational complexity.
What are the limitations or challenges of implementing this technology in real-world applications?
The article does not discuss any potential limitations or challenges that may arise when implementing this technology in practical scenarios. Understanding the obstacles or constraints faced in deploying this technology could provide valuable insights for its adoption and integration into various industries.
Original Abstract Submitted
methods, systems and apparatuses may provide for technology that trains a neural network by inputting video data to the neural network, determining a boundary loss function for the neural network, and selecting weights for the neural network based at least in part on the boundary loss function, wherein the neural network outputs a pixel-level segmentation of one or more objects depicted in the video data. the technology may also operate the neural network by accepting video data and an initial feature set, conducting a tensor decomposition on the initial feature set to obtain a reduced feature set, and outputting a pixel-level segmentation of object(s) depicted in the video data based at least in part on the reduced feature set.
