Hyundai motor company (20240177464). ACTIVE LEARNING DEVICE AND ACTIVE LEARNING METHOD simplified abstract
Contents
- 1 ACTIVE LEARNING DEVICE AND ACTIVE LEARNING METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ACTIVE LEARNING DEVICE AND ACTIVE LEARNING METHOD - 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
ACTIVE LEARNING DEVICE AND ACTIVE LEARNING METHOD
Organization Name
Inventor(s)
Geon Kang of Pyeongtaek-si (KR)
Jin Sol Kim of Hwaseong-si (KR)
ACTIVE LEARNING DEVICE AND ACTIVE LEARNING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177464 titled 'ACTIVE LEARNING DEVICE AND ACTIVE LEARNING METHOD
Simplified Explanation
The patent application describes an active learning device and method that extract features from input, segmentation, and depth images to label training images based on cosine distances among the features.
- The active learning device includes:
- An input device to receive an input image
- A controller to extract features from the input, segmentation, and depth images
- A determination process based on cosine distances among the features to label training images
Potential Applications
The technology could be applied in: - Image recognition systems - Autonomous vehicles - Medical imaging analysis
Problems Solved
The technology addresses: - Efficient labeling of training images - Improved feature extraction from multiple image sources
Benefits
The benefits of this technology include: - Enhanced accuracy in image labeling - Streamlined training process for machine learning models - Increased efficiency in data analysis tasks
Potential Commercial Applications
The technology could be commercially used in: - Software development for image processing - Data analysis tools for various industries - Educational platforms for machine learning training
Possible Prior Art
One possible prior art for this technology could be the use of similar feature extraction methods in image recognition systems.
Unanswered Questions
How does this technology compare to existing image labeling methods?
This article does not provide a direct comparison to existing image labeling methods. It would be beneficial to understand the specific advantages and limitations of this technology in comparison to traditional approaches.
What are the potential limitations of using cosine distances for feature extraction in image labeling?
The article does not address any potential limitations of using cosine distances for feature extraction. It would be important to explore the accuracy and robustness of this method in different scenarios to understand its practical implications fully.
Original Abstract Submitted
an active learning device and an active learning method are provided. the active learning device includes an input device configured to receive an input image, and a controller configured to extract a first feature from the input image, extract a second feature from a segmentation image corresponding to the input image, and extract a third feature from a depth image corresponding to the input image. the active learning device is also configured to determine the input image as a training image based on cosine distances among the first feature, the second feature and the third feature, and labels the training image.
