Intel corporation (20240333904). GENERATION OF OPTICAL FLOW MAPS BASED ON FOREGROUND AND BACKGROUND IMAGE SEGMENTATION simplified abstract
Contents
GENERATION OF OPTICAL FLOW MAPS BASED ON FOREGROUND AND BACKGROUND IMAGE SEGMENTATION
Organization Name
Inventor(s)
Niloufar Pourian of Los Gatos CA (US)
GENERATION OF OPTICAL FLOW MAPS BASED ON FOREGROUND AND BACKGROUND IMAGE SEGMENTATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240333904 titled 'GENERATION OF OPTICAL FLOW MAPS BASED ON FOREGROUND AND BACKGROUND IMAGE SEGMENTATION
Simplified Explanation: This patent application discloses methods and apparatus for generating optical flow maps based on foreground and background image segmentation. The technology involves creating reference optical flow maps from background images and combining them with optical flow maps from input images to produce a final optical flow map.
- The patent application describes a process to generate optical flow maps based on image segmentation.
- It involves creating reference optical flow maps from background images and combining them with input images.
- The technology utilizes stereo pairs of images to enhance the accuracy of the optical flow maps.
- An alpha matte is used to segment the input images into foreground and background regions.
- The final optical flow map is a result of combining the reference map with the input map using the alpha matte.
Potential Applications: This technology can be applied in various fields such as computer vision, robotics, augmented reality, and autonomous vehicles. It can improve object tracking, scene understanding, and depth estimation in these applications.
Problems Solved: The technology addresses the challenge of accurately generating optical flow maps in complex scenes with foreground and background elements. It improves the precision of motion estimation and object tracking by incorporating image segmentation.
Benefits: - Enhanced accuracy in optical flow map generation - Improved object tracking and motion estimation - Better scene understanding and depth estimation - Increased efficiency in computer vision tasks
Commercial Applications: Title: Advanced Optical Flow Mapping Technology for Computer Vision Systems This technology can be utilized in the development of smart surveillance systems, autonomous drones, and virtual reality applications. It can also enhance the performance of self-driving cars and robotic systems by providing more accurate motion tracking and scene analysis capabilities.
Prior Art: Readers interested in exploring prior art related to optical flow mapping and image segmentation techniques can refer to research papers, patents, and academic publications in the fields of computer vision, image processing, and machine learning.
Frequently Updated Research: Researchers are continuously exploring new algorithms and methodologies to improve optical flow mapping accuracy and efficiency. Stay updated on the latest advancements in computer vision and image processing to leverage cutting-edge technologies in this domain.
Questions about Optical Flow Mapping Technology: 1. What are the key challenges in optical flow mapping technology? 2. How does image segmentation enhance the accuracy of optical flow maps?
Original Abstract Submitted
methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to generate optical flow maps based on foreground and background image segmentation are disclosed. example apparatus disclosed herein are to generate a reference optical flow map based on a stereo pair of background images corresponding to a first camera field-of-view and a second camera field-of-view, and generate a first optical flow map based on a stereo pair of input images corresponding to the first camera field-of-view and the second camera field-of-view. disclosed example apparatus are also to combine the reference optical flow map and the first optical flow map based on an alpha matte to generate a second optical flow map associated with the stereo pair of input images, the alpha matte representative of segmentation of at least one of the stereo pair of input images into foreground and background regions.
