LEARNING OUTCOMES
After the course, the student is familiar with basic concepts and methods of computer vision. The student understands the basic principles of image-based 3D reconstruction and is familiar with techniques used for automatic object recognition from images. The student can design and implement common computer vision methods and apply them to practical problems with real-world image data.
Credits: 5
Schedule: 02.09.2024 - 12.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Juho Kannala
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Image formation and processing, feature detection and matching, motion estimation, structure-from-motion, object recognition, image-based 3D reconstruction. The course gives an overview of algorithms, models and methods, which are used in automatic analysis of visual data.
Assessment Methods and Criteria
valid for whole curriculum period:
Combination of exercises and exam (details are provided on the first lecture).
DETAILS
Study Material
valid for whole curriculum period:
Lecture material is partially based on the following books:
R. Szeliski. Computer Vision: Algorithms and Applications (http://szeliski.org/Book/)
Hartley & Zisserman: Multiple View Geometry in Computer Vision (http://www.robots.ox.ac.uk/~vgg/hzbook/)
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I - II
2025-2026 Autumn I - II