Samsung display co., ltd. (20240205382). METHOD OF EVALUATING CROSSTALK AND CROSSTALK EVALUATION DEVICE PERFORMING THE SAME simplified abstract
Contents
METHOD OF EVALUATING CROSSTALK AND CROSSTALK EVALUATION DEVICE PERFORMING THE SAME
Organization Name
Inventor(s)
YOUNGSANG Ha of Yongin-si (KR)
METHOD OF EVALUATING CROSSTALK AND CROSSTALK EVALUATION DEVICE PERFORMING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240205382 titled 'METHOD OF EVALUATING CROSSTALK AND CROSSTALK EVALUATION DEVICE PERFORMING THE SAME
Simplified Explanation:
This patent application describes a method for evaluating crosstalk in multi-view 3-dimensional images using deep learning techniques.
- The method involves receiving three multi-view 3-dimensional images, classifying one of them as normal or defective to create a learning model, and then using this model to evaluate the third image for crosstalk.
Key Features and Innovation:
- Utilizes deep learning for crosstalk evaluation in multi-view 3-dimensional images.
- Classifies images as normal or defective to create a learning model.
- Provides an automated method for crosstalk assessment.
Potential Applications:
- Quality control in manufacturing processes.
- Medical imaging for detecting abnormalities.
- Virtual reality and augmented reality applications.
Problems Solved:
- Efficient and accurate evaluation of crosstalk in multi-view 3-dimensional images.
- Automation of image classification for defect detection.
Benefits:
- Improved accuracy in identifying crosstalk issues.
- Time-saving and cost-effective evaluation process.
- Enhanced quality control in various industries.
Commercial Applications:
Automated Crosstalk Evaluation in Multi-View 3-Dimensional Images: Enhancing Quality Control and Efficiency
Questions about Crosstalk Evaluation in Multi-View 3-Dimensional Images:
1. How does deep learning improve crosstalk evaluation in multi-view 3-dimensional images? 2. What are the potential commercial applications of this technology?
Original Abstract Submitted
a method of evaluating crosstalk includes receiving a first multi-view 3-dimensional image, a second multi-view 3-dimensional image, and a third multi-view 3-dimensional image, performing a deep learning on a crosstalk evaluation for classifying the first multi-view 3-dimensional image into a normal image or a defective image to generate a learning model, and inputting the third multi-view 3-dimensional image different from the first multi-view 3-dimensional image into the learning model to perform the crosstalk evaluation of the third multi-view 3-dimensional image.
