Computer Vision

Introduction

• The aims of computer vision, the origins of computer vision, and related fields.
• Computer vision trends and application domains.

Image formation

• Basic image properties.
• Perspective projection camera model.
• Camera calibration, direct linear transform, lens distortion correction.
• Propagation of light, photometry, photometric lens equation.
• Cameras and lenses, lighting techniques.
• Human eye, color perception, reproducing color, color spaces.

Image analysis

• Image filtering basics, histogramming.
• Edge detection, corner detection.
• Hough transform.
• Connected components analysis.
• Morphological filtering.
• Active contour models (snakes).
• Shape description.
• Scale space and image pyramids.
• Geometric image transformations, similarity measures.
• Image registration, model fitting, RANSAC.

Stereo vision

• Basic concepts of stereo vision.
• Stereo matching.
• Modeling and calibration, epipolar geometry.
• Active stereo, structured lighting.

Visual motion analysis

• Motion detection.
• Time to collision.
• Optic flow, motion field, velocity field.
• Visual tracking, Kalman filtering basics.

The aims of this course are to understand basic concepts, underlying theory, algorithms, and applications of computer vision, especially in intelligent systems for automation and robotics.

The lectures provide a theoretical background on particular subjects together with practical examples in Matlab or C.

Practical work is being performed as the part of laboratory exercises, and is accomplished in the form of multiple assignments, acquainting students with computer vision algorithms. Students work in groups, consisting of two or three students, and the results are in the form of Matlab code. After completing each part, students present their results to the assistant.