Professor:

Dr. John Oliensis

Objective:

The goal of computer vision is to compute properties of the three-dimensional world from images and video. Problems in this field include identifying the 3D shape of a scene, determining how things are moving, and recognizing familiar people and objects. This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, object recognition, and 3D shape reconstruction through stereo and structure from motion.

Professor:

H. Quynh Dinh

Objective:

The goal of computer graphics is to understand basic concept of graphics pipeline, ray tracing, lines, scan conversion, 2D clipping, 2D - 3D transforms, projections, curves and surfaces, hidden surface removal, illumination, shading, recursive ray tracing and volume rendering.

Professor:

Dr. Hong Man

Objective:

The goal of image processing and computer vision is to learn basic concepts about Multidimensional digital signals and systems, frequency analysis, sampling and filtering, 2-D data transforms with DTFT, DFT, DCT, KLT, human visual system and image perception, image enhancement with histogram analysis, linear and morphological operators, image restoration and image reconstruction from projections, image analysis, feature detection and recognition, image coding with DCT and wavelet technologies, JPEG and JPEG2000, Video coding with motion estimation, H.263 and MPEG etc.

Professor:

Mr. Steven A Gabarro

Objective:

The goal of web programming is to give the basic knowledge on how the Internet works and how to create advanced web sites by the use of script languages, after learning the basics of HTML. The course will teach the students how to create a complex global site through the creation of individual working modules, giving them the skills required in any business such as proper team work and coordination between groups.

Professor:

Mr. Samuel Kim

Objective:

The goal of data management system is to learn a basic concept of relational schemas, keys and foreign key references, relational algebra (as an introduction to SQL), SQL in depth, Entity-Relationship (ER) database design, translating from ER models to relational schemas and from relational schemas to ER models, functional dependencies, and normalization.

Professor:

Mr. M. Daneshmand

Objective:

This course introduces fundamental and practical tools, techniques, and algorithms for Knowledge Discovery and Data Mining (KD&DM). It provides a balanced approach between methods and practice. On the methodological side, it covers several techniques for transforming corporate data into business intelligence. These include: On-line Analytical Processing (OLAP) Systems, Artificial Neural Networks (ANN), Rule-Based Systems (RBS), Fuzzy Logic (FL), Machine Learning (ML), Classification Trees (C4.5 Algorithm), and Classification and Regression Trees (CART Algorithm). To illustrate the practical significance of the various techniques, half of the course is devoted to case studies. The case studies, drawn from real-world applications, demonstrate application of techniques to real world problems.

Professor:

Stephen Bloom

Objective:

This is a course on more complex data structures, and algorithm design and analysis, using the C language. Topics include: advanced and/or balanced search trees, hashing, further asymptotic complexity analysis, standard algorithm design techniques, graph algorithms, complex sort algorithms, and other "classic" algorithms that serve as examples of design techniques.

Professor:

George D. Ogden

Objective:

http://guinness.cs.stevens-tech.edu/~lbernste/

Supervisor