Overview of topics on image acquisition and processing (sampling quantization, color perception, smoothing and differentiation filters)
Overview of topics on image analysis (edge detection, image segmentation)
Texture representation, analysis and synthesis
Interest point detection (Harris corner detector)
Blob detection
The scale Invariant Feature Transform – SIFT
The Hough transform
Model fitting (least squares)
Robust model fitting (LMedS, RANSAC)
Image alignment
Camera models, lenses, projective geometry
Camera calibration
Epipolar geometry
Stereo vision: point correspondence and 3D reconstruction
Volumetric 3D reconstruction from multiple views
2D motion (normal flow, optical flow)
3D motion modeling (motion field, egomotion)
Tracking linear dynamical systems
Tracking with particle filtering
Object detection
Object recognition
Object category recognition
Activity recognition
Learning Outcomes
Knowledge: Having attended and succeeded in the course, the student is able to describe how specific, selected computer vision problems are addressed in the relevant literature. Comprehension: Having attended and succeeded in the course, the student has achieved an in-depth understanding of the mechanisms of solving specific computer vision problems and is able to explain the reasons that make these mechanisms sufficient to solve other problems. Application: Having attended and succeeded in the course, the student is able to reuse existing methodologies and tools in order to produce other solutions for solving specific versions of specific computer vision problems or developing applications. Analysis: Having attended and succeeded in the course, the student is able to make a critical view of specific problems and perceive them as a synthesis of a series of individual subproblems. Composition: Having attended and succeeded in the course, the student is able to combine individual tools and methodologies in order to succeed in solving complex computer vision problems. Assessment: Having attended and succeeded in the course, the student is able to measure/quantify the quality of computer vision problem solutions and compare these solutions against other existing ones.
Student Performance Evaluation
Specific details on grading can be found on the course’ s website
The courses of the Computer Science Department are designated with the letters "CS" followed by three decimal digits. The first digit denotes the year of study during which students are expected to enroll in the course; the second digit denotes the area of computer science to which the course belongs.
First Digit
Advised Year of Enrollment
1,2,3,4
First, Second, Third and Fourth year
5,6
Graduate courses
7,8,9
Specialized topics
Second Digit
Computer Science Area
0
Introductory - General
1
Background (Mathematics, Physics)
2
Hardware Systems
3
Networks and Telecommunication
4,5
Software Systems
6
Information Systems
7
Computer Vision and Robotics
8
Algorithms and Theory of Computation
9
Special Projects
The following pages contain tables (one for each course category) summarizing courses offered by the undergraduate studies program of the Computer Science Department at the University of Crete. Courses with code-names beginning with "MATH" or "PHYS" are taught by the Mathematics Department and Physics Department respectively at the University of Crete.
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