18508967. OBJECT REGION SEGMENTATION DEVICE AND OBJECT REGION SEGMENTATION METHOD THEREOF simplified abstract (Kia Corporation)
Contents
OBJECT REGION SEGMENTATION DEVICE AND OBJECT REGION SEGMENTATION METHOD THEREOF
Organization Name
Inventor(s)
OBJECT REGION SEGMENTATION DEVICE AND OBJECT REGION SEGMENTATION METHOD THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 18508967 titled 'OBJECT REGION SEGMENTATION DEVICE AND OBJECT REGION SEGMENTATION METHOD THEREOF
Simplified Explanation: The patent application describes a device and method for segmenting object regions in images using a deep-learning network model.
Key Features and Innovation:
- Includes a first network model for generating a pseudo label, a second network model for generating a confidence map, and a third network model for segmenting the object region.
- Processor inputs an unlabeled image to generate a pseudo label, then uses it to create a confidence map for training the third network model.
- Trains the third network model using pixels with confidence levels above a threshold on the confidence map.
Potential Applications: This technology can be used in various fields such as medical imaging, autonomous vehicles, surveillance systems, and image editing software.
Problems Solved:
- Efficiently segmenting object regions in images.
- Improving accuracy and reliability of object recognition systems.
- Enhancing image processing capabilities in various industries.
Benefits:
- Enhanced image segmentation accuracy.
- Improved object recognition performance.
- Increased efficiency in image processing tasks.
Commercial Applications: The technology can be applied in industries such as healthcare, automotive, security, and graphic design for better image analysis and processing capabilities.
Prior Art: Prior research in deep learning models for image segmentation and object recognition can provide valuable insights into the development of this technology.
Frequently Updated Research: Stay updated on advancements in deep learning algorithms, image segmentation techniques, and object recognition models to enhance the performance of this technology.
Questions about Object Region Segmentation: 1. What are the potential challenges in implementing this technology in real-time applications? 2. How does this technology compare to traditional image segmentation methods in terms of accuracy and efficiency?
Original Abstract Submitted
An object region segmentation device and an object region segmentation method thereof are provided. The object region segmentation device includes a processor and storage. The storage stores a deep-learning network model for segmenting an object region in an image. The deep-learning network model includes a first network model for generating a pseudo label, a second network model for generating a confidence map for the pseudo label, and a third network model for segmenting the object region in the image. The processor inputs an unlabeled image to the first network model to generate the pseudo label, inputs the pseudo label to the second network model to generate the confidence map, and trains the third network model using a pseudo label corresponding to at least one pixel, a confidence level of which is greater than or equal to a threshold, on the confidence map.
