Machine Perception

Lectures:

1. Overview of the field of Machine perception and scientific challenges
2. Image processing
   1. Image formation
   2. Binarization, morfology, segmentation
   3. Colour spaces and colour perception
   4. Linear and nonlinear filters
3. Image derivatives and edge perception
   1. Derivative-based edge perception
   2. Edge-based object perception
   3. Parametric shape perception
4. Model fitting
   1. Normal equations
   2. Homogenous systems
   3. Robust approaches
5. Local features
   1. Corner perception
   2. Local descriptors in scale space and affine adaptation
6. Stereoscopy and depth perception
   1. Calibrated and uncalibrated systems and reconstruction
7. Object recognition
   1. Subspace methods (PCA, LDA)
   2. Local-features-based recognition
8. Object detection
   1. Visual features and detection approaches
9. Motion perception
   1. Local motion perception and object tracking

Exercises:

Exercises will take a form of project-oriented exercises in properly equipped student laboratories. Students will implement various algorithms and test them on different datasets using a variety of sensor systems. Exercises will support an in-depth understanding of the theory. They will also encourage independent thinking and creativity.

In the framework of this course, the students will acquire concrete knowledge and skills in the area of machine perception. The students will develop competences in low-level image processing, 3D geometry of stereo systems, object detection, object recognition, and motion extraction in video sequences. The students will also practice mathematical basics crucial for solving demanding engineering problems, which are essential for analysis of complex signals such as images and video.

In addition, the students will obtain the following competences:

• The ability to understand and solve professional challenges in computer and information science.

• The ability of professional communication in the native language as well as a foreign language.

• The ability to independently perform both less demanding and complex engineering and organisational tasks in certain narrow areas and independently solve specific well-defined tasks in computer and information science.

Lectures, laboratory exercises in computer classroom with active participation. Individual work on exercises. Theory from the lectures made concrete with hands-on laboratory exercises. Special emphasis will be put on continuous assessment at exercises.