Lecturer: Virginio Cantoni  

Course name: Computer Vision
Course code: 504703
Degree course: Computer Engeneering
Disciplinary field of science: ING-INF/05
L'insegnamento è caratterizzante per: Computer Engeneering
University credits: ECTS 6
Course website: http://vision.unipv.it/VA/

Specific course objectives

Computer Vision consists of inferring properties of the world based on one or more digital images. Provides background in image processing and image formation. Focus on algorithms for image and video analysis based on color, texture, shading, stereo, and motion.

Course programme

Introduction to Computer Vision
Basic definitions. Low-level image analysis methods, including image formation, edge detection, feature detection, and image segmentation.

3D Vision and motion analysis
Methods for reconstructing three-dimensional scene information using techniques such as depth from stereo, structure from motion, and shape from shading. Motion and video analysis.

Object recognition
Recognition Processes. Direct Comparison. Alignment methods. Invariant properties methods. Parts decompositions method. Hough transform.

Image synthesis
Computer graphics topics involving computational photography and image-based rendering. Local rendering, Phong model. Advanced rendering techniques, topics include ray casting, ray tracing, and radiosity.

Course entry requirements

This course is intended for advanced undergraduate students. We assume students have a rudimentary understanding of linear algebra, calculus, and are able to program in some type of structured language.

Course structure and teaching

Lectures (hours/year in lecture theatre): 45
Practical class (hours/year in lecture theatre): 0
Practicals / Workshops (hours/year in lecture theatre): 0

Suggested reading materials

V. Cantoni, S. Levialdi, B. Zavidovique. 3C Vision - Cues, Context and Channels. Elsevier, 2011.

V. Cantoni. Course slides.

Testing and exams

Students will be asked to read three papers. They will be required to write a report of one of these papers, due before we discuss the paper. Each student is required to complete a laboratory project consisting of a sequence of image analysis steps resulting in image interpretation thus emphasizing hands-on image analysis experience. The exam consists on the discussion of the projects and on the paper report.