18311476. TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES - 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
TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Lingzhi L. Allen of Redmond WA (US)
Wolfgang M. Pauli of Seattle WA (US)
TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18311476 titled 'TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES
Simplified Explanation
The abstract describes a patent application for a machine learning model that uses a set of geometric shapes to estimate spatial parameters of objects identified in image data. The model updates its learning rate in response to external events and integrates updates to the spatial parameters over time to generate spatial estimates of the objects.
- Machine learning model utilizes geometric shapes for object identification in image data
- Learning rate of the model is adjusted based on external events
- Spatial parameters of objects are estimated by fitting them to geometric shapes
- Updates to spatial parameters are integrated over time
- Spatial estimates of objects in image data are generated
Potential Applications
This technology could be applied in various fields such as:
- Object recognition in autonomous vehicles
- Medical imaging for identifying and tracking specific structures
- Surveillance systems for monitoring and tracking objects in real-time
Problems Solved
This technology addresses the following issues:
- Efficient and accurate object identification in complex image data
- Real-time tracking and estimation of spatial parameters of objects
- Integration of external events to update learning rate for improved performance
Benefits
The benefits of this technology include:
- Enhanced object recognition and tracking capabilities
- Improved accuracy and efficiency in spatial parameter estimation
- Adaptability to changing environments and external events
Potential Commercial Applications
A potential commercial application for this technology could be:
- Development of advanced security systems for object detection and tracking in high-security areas
Possible Prior Art
One possible prior art for this technology could be:
- Existing machine learning models that use geometric shapes for object recognition and spatial estimation
Unanswered Questions
How does this technology compare to existing object recognition systems in terms of accuracy and efficiency?
This article does not provide a direct comparison between this technology and existing object recognition systems. Further research or testing would be needed to determine the performance differences.
What are the potential limitations or challenges of integrating external events to update the learning rate of the machine learning model?
The article does not address the potential limitations or challenges of this aspect of the technology. Additional analysis or experimentation may be required to identify and address any issues that may arise.
Original Abstract Submitted
A set of geometric shapes to be applied by a machine learning model to objects identified in image data is defined. A learning rate of the machine learning model is updated in response to external events. The machine learning model is used to estimate spatial parameters for each of the objects identified in the image data. The spatial parameters are estimated by fitting the objects to the set of geometric shapes. Updates to the spatial parameters are temporally integrated. A spatial estimate of the objects identified in the image data is generated.
