Signals, Image Processing, and Computer Vision (SPCV)
The research in pattern recognition & neural networks, machine learning and image processing mainly focuses on exploring advanced pattern recognition and machine learning techniques for a variety of image processing applications. Current focus is on the use of convolutional neural network, deep learning, generative models, GANs, SVM and random forests mainly for biomedical images. Also, a variety of advanced techniques for Image fusion, tracking of objects in a video, and data compression have been developed.
RESEARCH TOPICS
- Mobile wireless sensor network
- Process control
- Real-time control
- Optimal control
- Pattern recognition
- Neural networks
- Machine learning
- Image processing
- Biomedical applications
students specializing in SPCV take classes such as
REquired core COURSES
Image Analysis and Computer Vision
Digital Signal Processing
Data Compression for Multimedia Communication
REcommended COURSES
Machine Learning
Random Signals and Optimal Filters
Wireless and Mobile Networks
Pattern Recognition and Neural Networks
FACULTY MEMBERS
Dr. Ezzatollah Salari (SPCV Leader)
Dr. Ezzatollah Salari’s current research is in the areas of pattern recognition, neural networks, and machine learning involving convolutional neural network, deep learning, generative models, GANs, SVM and random forests in a variety of image processing applications.
Dr. Junghwan Kim
Dr. Junghwan Kim’s research is in the areas of modeling and performance analysis of on-board processing satellite system and its architecture, anti-jamming techniques and spread spectrum system, cellular and mobile wireless network, advanced channel coding, multimedia broadcasting and physical layer (PHY)–based encryption.