18508967. OBJECT REGION SEGMENTATION DEVICE AND OBJECT REGION SEGMENTATION METHOD THEREOF simplified abstract (Hyundai Motor Company)

OBJECT REGION SEGMENTATION DEVICE AND OBJECT REGION SEGMENTATION METHOD THEREOF

Organization Name

Hyundai Motor Company

Inventor(s)

Jae Hoon Cho of Seoul (KR)

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.

• The device includes a processor and storage that store the deep-learning network model.
• The model consists of three network models: one for generating a pseudo label, one for creating a confidence map for the pseudo label, and one for segmenting the object region in the image.
• The processor inputs an unlabeled image to generate a pseudo label, then uses this label to create a confidence map, and finally trains the network model to segment the object region based on the confidence map.

Key Features and Innovation

• Utilizes a deep-learning network model for object region segmentation.
• Includes three network models for different stages of the segmentation process.
• Trains the network model based on confidence levels of pixels in the image.

Potential Applications

• Image processing and analysis.
• Object recognition and tracking.
• Medical imaging for identifying specific regions of interest.

Problems Solved

• Efficient and accurate segmentation of object regions in images.
• Automation of the segmentation process.
• Improved object recognition and analysis.

Benefits

• Enhanced image processing capabilities.
• Faster and more precise object segmentation.
• Increased accuracy in identifying object regions.

Commercial Applications

• "Deep-learning Object Region Segmentation Device for Image Analysis"
• This technology can be used in industries such as healthcare, security, and autonomous vehicles for improved object detection and analysis.

Questions about Object Region Segmentation

How does the deep-learning network model improve object region segmentation compared to traditional methods?

The deep-learning network model can learn complex patterns and features in images, allowing for more accurate and efficient segmentation of object regions.

What are the potential limitations of using a deep-learning network model for object region segmentation?

Some limitations may include the need for large amounts of training data and computational resources, as well as potential biases in the training data that could affect the segmentation results.

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.