Visual recognition 1000-318bVR
1. Introduction to Visual Recognition (classic methods: SIFT, Hough transform).
2. Convolutional Neural Networks - recap.
3. Visualising and Understanding.
4. Object Detection.
5. Semantic and Instance Segmentation.
6. Video understanding.
7. 3D vision.
8. Generative models.
Type of course
Requirements
Course coordinators
Learning outcomes
Knowledge: the student
* has based in theory and well organized knowledge of problems of image classification and object detection [K_W12].
Abilities: the student is able to
* create a developed solution in the domain of image classification and object detection [K_U15].
Social competences: the student is ready to
* critically evaluate acquired knowledge and information [K_K01];
* recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution [K_K02];
* think and act in an entrepreneurial way [K_K03].
Assessment criteria
Laboratories: programming projects. Lectures: written examination
Bibliography
http://www.deeplearningbook.org/
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: