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Computer Vision and Image Processing


Professor Ioannis Stamos


Computer vision and image processing are important and fast evolving areas of computer science, and have been applied in many disciplines. This course will introduce students to the fascinating fields. Student will gain familiarity with both established and emergent methods, algorithms and architectures. This course will enable students to apply computer vision and image processing techniques to solve various real-world problems, and develop skills for research in the fields.

Course description

This course introduces fundamental concepts and techniques for image processing and computer vision. Topics to be covered include image formation, image filtering, edge detection and segmentation, morphological processing, registration, object recognition, object detection and tracking, 3D vision, and etc.

List of topics

The topics may include but are not limited to:

  • Image formation and perception, image representation

  • Image filtering: space- and frequency- domain filtering, linear and non-linear filters

  • Morphological image processing

  • Image geometric transformations, image registration

  • Edge detection, image segmentation, active contours, level set methods

  • Object recognition, template matching, classification

  • Object detection and tracking: background modeling, kernel-based tracking, particle filters

  • Camera models, stereo vision

  • 3D point cloud processing

Learning objectives

The learning goals include:

  • Understand the major concepts and techniques in computer vision and image processing

  • Demonstrate computer vision and image processing knowledge by designing and implementing algorithms to solve practical problems

  • Understand current research in the fields

  • Prepare for research in computer vision and image processing


The course assessments include homework and programming assignments (40%), one middle term exam (20%), and a final project (40%). The homeworks and programming assignments will cover topics related to image formation and filtering, image segmentation, edge detection, object detection and classification. The middle term exam will concentrate on image formation and perception, image representation, image filtering, image geometric transformation, edge detection and image segmentation. The final project should be related to topics in stereo vision, object classification, object tracking, and object classification. The programming projects require students to implement algorithms to solve real computer vision problems to demonstrate their understanding on concepts of computer vision and image processing. A final project on a research topic requires a proposal, a final project report, and a presentation to prepare students for research experience.