Nec corporation (20240135674). ESSENTIAL MATRIX GENERATION APPARATUS, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM simplified abstract
Contents
- 1 ESSENTIAL MATRIX GENERATION APPARATUS, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ESSENTIAL MATRIX GENERATION APPARATUS, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM - 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
ESSENTIAL MATRIX GENERATION APPARATUS, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM
Organization Name
Inventor(s)
ESSENTIAL MATRIX GENERATION APPARATUS, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135674 titled 'ESSENTIAL MATRIX GENERATION APPARATUS, CONTROL METHOD, AND COMPUTER-READABLE MEDIUM
Simplified Explanation
The patent application describes an apparatus for generating an essential matrix, which represents an epipolar constraint between points in two images. The apparatus detects feature point pairs in two images, then derives point pairs based on these feature points to generate the essential matrix.
- Detecting three or more feature point pairs from a first image and a second image
- Detecting derived point pairs for each feature point pair
- Generating an essential matrix representing an epipolar constraint using the detected feature point pairs and derived point pairs
Potential Applications
This technology can be applied in various fields such as computer vision, robotics, and augmented reality for accurate 3D reconstruction, object tracking, and scene understanding.
Problems Solved
- Accurate matching of points in different images - Estimation of camera motion and structure from images - Enabling 3D reconstruction and object tracking
Benefits
- Improved accuracy in matching and tracking objects across images - Efficient generation of essential matrix for epipolar constraint - Enhanced performance in computer vision tasks
Potential Commercial Applications
- Autonomous vehicles for navigation and obstacle avoidance - Surveillance systems for tracking and monitoring objects - Augmented reality applications for overlaying digital information on the real world
Possible Prior Art
Prior art in this field includes various methods for feature detection, matching, and camera calibration in computer vision and robotics. Some examples include SIFT (Scale-Invariant Feature Transform) and RANSAC (Random Sample Consensus) algorithms for robust feature matching.
Unanswered Questions
How does this technology compare to existing methods for essential matrix generation?
This article does not provide a direct comparison with existing methods or algorithms for essential matrix generation. It would be helpful to know the advantages or limitations of this apparatus compared to traditional techniques.
What are the computational requirements of this apparatus for real-time applications?
The article does not mention the computational complexity or speed of the apparatus for real-time applications. Understanding the computational requirements would be crucial for implementing this technology in real-world systems.
Original Abstract Submitted
an essential matrix generation apparatus performs: detecting three or more feature point pairs from a first image and a second image; detecting, for each of two or more of the feature point pairs, a derived point pair that is a pair of a derived point separated by a first distance in a first direction from a point on the first image included in the feature point pair and a derived point separated by a second distance in a second direction from a point on the second image included in the feature point pair; generating an essential matrix representing an epipolar constraint on a point on the first image and a point on the second image by using the detected feature point pairs and derived point pairs.
