Driven by the rapid advancement in big data and high performance computing, deep learning architectures and algorithms based on Convolution Neural networks have recently made many performance breakthroughs in a variety of visual tasks, including image classification, image recognition, image segmentation, object localization and detection. Dr. Lei Huang's research interest mainly focuses on applying deep learning technology to solve challenges facing our society, and to improve human life.
Current research projects include applying deep learning for medical image analysis and diagnosis and for automatic road crack detection and classification. Her research implements data preparation, model design and training, performance evaluation and improvement in state-of-the-art deep learning platforms. Dr. Huang is also interested in data security, especially for image and video data, in cloud computing and storage.
Rommel Fernandes, Lei Huang, and Gustavo Vejarano, "Non-Audible Speech Classification Using Deep Learning Approaches" in 6th Annual Conference on Computational Science & Computational Intelligence (CSCI'19), Las Vegas, NV, USA, Dec. 2019
Vignesh Mohanraj, Hossein Asghari, and Lei Huang, “Improved Automatic Road Crack Detection and Classification” in 2018 First International Conference on Image, Video Processing and Artificial Intelligence (IVPAI'18), Shanghai, China, August 2018
Daniel Oleas and Lei Huang, "Non-dominant Object Recognition using Convolutional Neural Networks” in International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV'18), Las Vegas, NV, July 2018
L. Huang, "Efficient Integration of Image Encryption with Compression Using Optimal Entropy Coder", in Proceedings of the 19th International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV'15), July, 2015.