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Prof. Mu-Yen Chen
Email: mychen119@gs.ncku.edu.tw
Affiliation: Department of Engineering Science, National Cheng Kung University, Tainan, 70101, Taiwan
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Research Interests: artificial intelligence, deep learning, machine learning, big data, image recognition
Summary
Living in the era of big data, we are witnessing of current dramatic growth of hybrid data which is a complex set of cross-media content, such as text, images, videos, audio, and time series sequential data.Recently, Deep Learning has shown immense success, leading to state-of-the-art results in various fields. The field of computer vision and image processing has seen advances continually thanks to innovation in deep learning. In the last decade, various deep learning algorithms have been introduced for unsupervised, supervised, and reinforcement learning algorithms and applications. Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Network (GNN), Long Short-Term Memory (LSTM), etc., are a few deep learning algorithms that achieve significant success in computer vision and image processing. However, applying deep learning to solve problems will encounter some challenges. To improve the performance of Deep Learning methods, the scalability of deep learning method systems is necessary, thus there is a need to develop new parallel and distributed deep learning approaches that can help to speed up the training process and make deep learning models suitable for big data.
This special issue aims to bring together all the potential research scholars worldwide to contribute and submit their original research articles that include algorithms, architecture, and empirical results for computer vision and image recognition applications using deep learning and AI-related technologies. The special issue will cover the following topics but not restricted:
• Analysis of deep neural network for real-time imaging application
• Deep Learning-based feature extraction for computer vision and image processing
• State-of-the-art neural computing for smart living, agriculture, healthcare, transportation, underwater, education, and sport applications using computer vision and image processing
• Challenges and opportunities of neural computing-based image analysis for computer vision and image processing algorithm
Keywords
Artificial Intelligence, Deep Learning, Machine Learning, Image Recognition, Image Processing, Computer Vision
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