Abstract:
This paper presents a deep learning model specifically designed to effectively classify display Mura images. The model leverages advanced deep learning techniques and com...Show MoreMetadata
Abstract:
This paper presents a deep learning model specifically designed to effectively classify display Mura images. The model leverages advanced deep learning techniques and computer vision methods to identify and categorize various types of Mura based on their unique digital signatures and visual patterns. It aims to provide fast and accurate classification results, enabling real-time processing of large-scale image data. The model is expected to significantly enhance content management and user experience with display Mura, and it can be innovatively applied across various fields of image classification.
Date of Conference: 14-16 October 2024
Date Added to IEEE Xplore: 28 November 2024
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Deep Learning Techniques ,
- Adaptive Threshold ,
- Adaptive Thresholding Technique ,
- Image Classification ,
- Deep Learning Models ,
- User Experience ,
- Accurate Classification Results ,
- Neural Network ,
- Model Performance ,
- Training Data ,
- Validation Data ,
- Convolutional Neural Network ,
- Mobile Devices ,
- Sigmoid Function ,
- Manufacturing Process ,
- Feature Maps ,
- Manufacturing Industry ,
- Convolution Operation ,
- Loss Model ,
- Training Loss ,
- Sobel Operator ,
- Varying Lighting Conditions ,
- Validation Loss ,
- Validation Accuracy ,
- Pivotal Component ,
- Liquid Crystal Display ,
- Amorphous Thin Films ,
- Amorphous Silicon ,
- Loss Value
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Deep Learning Techniques ,
- Adaptive Threshold ,
- Adaptive Thresholding Technique ,
- Image Classification ,
- Deep Learning Models ,
- User Experience ,
- Accurate Classification Results ,
- Neural Network ,
- Model Performance ,
- Training Data ,
- Validation Data ,
- Convolutional Neural Network ,
- Mobile Devices ,
- Sigmoid Function ,
- Manufacturing Process ,
- Feature Maps ,
- Manufacturing Industry ,
- Convolution Operation ,
- Loss Model ,
- Training Loss ,
- Sobel Operator ,
- Varying Lighting Conditions ,
- Validation Loss ,
- Validation Accuracy ,
- Pivotal Component ,
- Liquid Crystal Display ,
- Amorphous Thin Films ,
- Amorphous Silicon ,
- Loss Value
- Author Keywords
