northeastern-university


Teaching

SUNY at Buffalo, Computer Science and Engineering department, Buffalo, NY

-           Spring 2012, CSE 705 Seminar in Sparse Representation and Low-Rank Matrix Analytics [CourseWeb]

Overview: This is a seminar course covering the popular machine learning topics in sparse representation, low-rank matrix approximation and recovery. We will read and discuss latest papers with all the students involved. Guest lecturers will be invited to present some topics if funding is available for honoraria or expenses.

Prerequisites: Fundamental knowledge and some experiences of machine learning, image processing, and computer vision.

-           Fall 2011, CSE 456/556 Visualization [CourseWeb]

Overview: Introduction to relevant topics and concepts in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concept, animation techniques, pattern analysis, and computational methods. Tools and techniques for practical visualization. Elements of related fields including computer graphics, human perception, computer vision, imaging science, multimedia, human-computer interaction, computational science, and information theory. Covers examples from a variety of scientific, medical, interactive multimedia, and artistic applications. Hands-on exercises and projects.

Prerequisites: CSE250, basic programming skills, knowledge of fundamental data structures and algorithms.

-           Spring 2011, CSE 678 Face and Gesture Recognition [CourseWeb]

Overview: Face and gesture recognition is an advanced technology that utilizes the intrinsic physiological or behavioral traits of individual for machine-based automatic and reliable identification. It attracts much attention due the increasing demand for the security, privacy, and health care related human-centered applications. This course covers the state-of-the-art face and gesture recognition technologies, including face/human detection, face/body tracking, face recognition, head/body pose estimation, expression recognition, body language recognition, gait analysis, hand/body/eye gesture, action/activity analysis, and so forth. Multimodal, multimodality, and soft-biometric frameworks will also be discussed. Fundamental knowledge covered by the course include pattern recognition, feature extraction, classifier, probabilistic models, image processing, and machine learning. Tools and techniques for practical face and gesture recognition system design as well as hands-on exercises and projects will be provided.

Prerequisites: CSE 555 or CSE 574, and CSE 573; or permission by instructor.

-           Fall 2010, CSE 456/556 Introduction to Visualization [CourseWeb]

Overview: Introduction to relevant topics and concepts in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concept, animation techniques, pattern analysis, and computational methods. Tools and techniques for practical visualization. Elements of related fields including computer graphics, human perception, computer vision, imaging science, multimedia, human-computer interaction, computational science, and information theory. Covers examples from a variety of scientific, medical, interactive multimedia, and artistic applications. Hands-on exercises and projects.

Prerequisites: CSE250, basic programming skills, knowledge of fundamental data structures and algorithms.

-           Spring 2010, CSE 704 Seminar in Manifold and Subspace Learning [CourseWeb]

Overview: Designing subspace learning algorithms using manifold criterion and models is a rapid emerging area in computer vision and pattern recognition. This seminar will cover extensive discussions on the state of the art literature in manifold and subspace learning. Topics, which will be well balanced between the basic theoretical background and practical applications, include manifold modeling, dimensionality reduction, discriminant analysis, component analysis, kernelization, feature extraction/representation, transfer learning, semisupervised learning, etc. The involved applications are mainly derived from the imaging field, such as biometrics, image/video processing, machine vision, and human computer interaction. We will read and discuss papers on the listed topic together.

Prerequisites: Fundamental knowledge and some experiences of pattern classification, image processing, and computer vision.

Tufts University, Computer Science department, Medford, MA

-           Spring 2009, COMP150-08 Foundations of Scientific Visualization [CourseWeb]

Overview: Introduction to relevant topics and concepts in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concept, animation techniques, pattern analysis, and computational methods. Tools and techniques for practical visualization. Elements of related fields including computer graphics, human perception, computer vision, imaging science, multimedia, human-computer interaction, computational science, and information theory. Covers examples from a variety of scientific, medical, interactive multimedia, and artistic applications. Hands-on exercises and projects.

Prerequisites: Comp15 or permission of instructor.

Last Update: 9-18-2012, Copyright 2004~2012, Raymond Fu, All Rights Reserved