Li Cheng, PhD

Area of Study / Keywords

Signal and Image Processing Software Engineering and Intelligent Systems

About

Li Cheng is professor with the Department of Electrical and Computer Engineering, University of Alberta. Prior to joining University of Alberta in year 2018, He worked at A*STAR, Singapore, TTI-Chicago, USA, and NICTA, Australia. He received his BSc degree in Computer Science from Jilin University China in 1996, M Eng degree from Nankai University Chin in 1999, and PhD in Computing Science from the University of Alberta in 2004. His research expertise is mainly on computer vision and applications. More info can be found here.

Research

Current research

• Visual human motion analysis, to look at humans and also animals and to interpret their poses and behaviors, such as pose estimation, tracking, and action recognition.
• Biomedical image understanding, including retinal fundus image analysis, microscopic image analysis, neuronal tracing, etc.
• Computer vision, robot vision, machine learning, including especially deep learning, and applications.
• Precision agriculture applications.

ECE 342 - Probability for Electrical and Computer Engineers

Deterministic and probabilistic models. Basics of probability theory: random experiments, axioms of probability, conditional probability and independence. Discrete and continuous random variables: cumulative distribution and probability density functions, functions of a random variable, expected values, transform methods. Pairs of random variables: independence, joint cdf and pdf, conditional probability and expectation, functions of a pair of random variables, jointly Gaussian random variables. Sums of random variables: the central limit theorem; basic types of random processes, wide sense stationary processes, autocorrelation and crosscorrelation, power spectrum, white noise. Prerequisite: MATH 209. Credit may be obtained in only one of ECE 342 or E E 387.

ECE 442 - Introduction to Multimedia Signal Processing

Human visual/audio perception and multimedia data representations. Basic multimedia processing concepts, multimedia compression and communications. Machine learning tools for multimedia signal processing, including principle component analysis and Gaussian mixture modeling. Applications to human-computer interaction, visual-audio, and visual-text processing. Prerequisites: ECE 220 or CMPUT 275, ECE 342, MATH 102 or equivalent knowledge. Credit may be obtained in only one of ECE 442 or E E 442.

ECE 740 - Advanced Topics in Signal and Image Processing

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