Intel corporation (20240135750). INITIALIZER FOR CIRCLE DISTRIBUTION FOR IMAGE AND VIDEO COMPRESSION AND POSTURE DETECTION simplified abstract
Contents
- 1 INITIALIZER FOR CIRCLE DISTRIBUTION FOR IMAGE AND VIDEO COMPRESSION AND POSTURE DETECTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INITIALIZER FOR CIRCLE DISTRIBUTION FOR IMAGE AND VIDEO COMPRESSION AND POSTURE DETECTION - 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
- 1.9.1 Unanswered Questions
- 1.9.2 How does the mixed deterministic and iterative/stochastic approach improve circle distribution on a 2D surface using a polar coordinate system?
- 1.9.3 What are the potential limitations of using a non-linear expressive perceptron in a neural network for solving circle distribution and other problems?
- 1.10 Original Abstract Submitted
INITIALIZER FOR CIRCLE DISTRIBUTION FOR IMAGE AND VIDEO COMPRESSION AND POSTURE DETECTION
Organization Name
Inventor(s)
Pawel Tomkiewicz of Zukowo (PL)
Monica Lucia Martinez-canales of Los Altos CA (US)
INITIALIZER FOR CIRCLE DISTRIBUTION FOR IMAGE AND VIDEO COMPRESSION AND POSTURE DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135750 titled 'INITIALIZER FOR CIRCLE DISTRIBUTION FOR IMAGE AND VIDEO COMPRESSION AND POSTURE DETECTION
Simplified Explanation
The patent application describes an initializer for circle distribution on a 2D surface using a polar coordinate system, with applications in image compression, video compression, motion detection, and posture detection. The initializer can also be used for sphere distribution in a 3D shape, utilizing a mixed deterministic and iterative/stochastic approach.
- The initializer transitions from the polar coordinate system to a Cartesian coordinate system after parameters are initialized, enabling coverage of the user space.
- The method includes using the polar system in CPU units through an XNOR/AND architecture for neural network model compression.
- A neural network with a non-linear expressive perceptron (quadtron) is described for solving circle distribution and other problems, replacing the multiplication unit in a MAC architecture with a non-linear function.
Potential Applications
The technology can be applied in image compression, video compression, motion detection, posture detection, and sphere distribution in 3D shapes.
Problems Solved
The technology addresses the need for efficient circle distribution on a 2D surface and sphere distribution in 3D shapes, with applications in various fields such as image and video processing.
Benefits
The benefits of this technology include improved efficiency in circle and sphere distribution, enhanced image and video compression, accurate motion and posture detection, and optimized neural network model compression.
Potential Commercial Applications
Potential commercial applications of this technology include software development for image and video processing, surveillance systems, medical imaging, and artificial intelligence.
Possible Prior Art
Prior art in the field of image and video compression, motion detection, and neural network model compression may exist, but specific examples are not provided in the patent application.
Unanswered Questions
How does the mixed deterministic and iterative/stochastic approach improve circle distribution on a 2D surface using a polar coordinate system?
The mixed deterministic and iterative/stochastic approach allows for a more flexible and adaptive initialization process, leading to better coverage of the user space and improved efficiency in circle distribution.
What are the potential limitations of using a non-linear expressive perceptron in a neural network for solving circle distribution and other problems?
The use of a non-linear expressive perceptron may introduce complexity and computational overhead, potentially impacting the speed and performance of the neural network in certain applications.
Original Abstract Submitted
an initializer for circle distribution on a 2d surface using a polar coordinate system for image compression, video compression, motion detection, and posture detection. the initializer can also be used for sphere distribution in a 3d shape. the initializer uses a mixed deterministic and iterative/stochastic approach. using the polar coordinate system for initialization enables coverage of the user space, and after parameters are initialized, the method transitions to a cartesian coordinate system. methods for using the polar system in cpu units by applying an xnor/and architecture for neural network model compression are also described. the neural network includes a perceptron for supervised learning of binary classifiers. the unit responsible for multiplication in a mac architecture can be replaced with a non-linear expressive function. thus, a neural network having a non-linear expressive perceptron (quadtron) is described for solving circle distribution and other problems.
