|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
The course is about image processing and computer vision techniques for 3D static and dynamic scene interpretation and to discuss applications to object tracking, depth perception, object recognition and automatic guidance
The course aims at providing knowledge on theory and tools on the basics of Computer Vision, for the extraction of semantic and geometric information about a scene from an image or a sequence of images. Topics of interest include: camera models and image formation; camera calibration; connection between 2D images and 3D scene structures; image processing basics as image filtering, local features extraction (edge, corner, blob), including the use of multi-scale image representations; image matching, with reference to classification and retrieval problems; stereo vision and scene depth estimation; motion detection in image sequences, including change detection and optical flow estimation.
The aim of the course is to provide a broad introduction to different core aspects of computer vision, including camera modelling, camera calibration, image processing, pose estimation, multi view geometry, visual tracking, and vision based calibration.
At the end of the course the student will be able to understand the main theoretical concepts and to design and implement classical computer vision algorithms. The course will also provide an overview of the main application domains, with a special reference to the robotics scenario.
background knowledge on linear algebra and calculus; basic programming skills
Theoretical classes followed by hands-on activities
Introduction to computer vision for robotics applications
Part 1 - image processing fundamentals
Digital image fundamentals: sensing and acquisition, sampling and quantization, basic operations (warping)
Intensity transformations and spatial filtering (filtering in the frequency domain)
Edge and corner detection
Color image processing
Hough transforms and image segmentation
Scale space and blob detection
Part 2 - motion analysis
Motion: 3D and 2D motion fields, dense and sparse optical flow. Dominant motion estimation
Tracking with linear dynamic models (Kalman Filter)
Part 3 - geometry
3D computer vision fundamentals
The geometry of image formation: review of projective geometry (basic), projective transformations, camera models and single view geometry, camera calibration, Homographies
Stereopsis: epipolar geometry, stereo rectification, depth estimation, 3D reconstruction
Conclusions: Visual Recognition and image retrieval; introduction to object and action recognition methods in HRI
Further readings: Material distributed by lecturers through the Aulaweb portal
FABIO SOLARI (President)
NICOLETTA NOCETI (President Substitute)
All class schedules are posted on the EasyAcademy portal.
The course is organized in theory classes and practical (hands-on) classes.
Practical activities cover about 1/3 of the course. The goal of such activities is presented in class by the instructors, and should be completed by the students as a homework. They can be carried out individually or in groups; some of them are associated with an assignment and constitute a continuous assessment of the student's work.
The final assessment of the theory part is carried out through a final exam.