Advanced Computer Vision
0
2023-2024
02042649
Computers
Portuguese
English
Face-to-face
6.0
Elective
2nd Cycle Studies - Mestrado
Recommended Prerequisites
Linear algebra and analytic geometry, Matrix Calculus, Differential Calculus, Stochastic Estimation, Computer Vision, Signal Processing
Teaching Methods
The teaching methodologies in this course are based on the presentation of fundamental and conceptual aspects of computer vision in magister classes (theoretical classes), including presentations by students (based on book chapters and papers), complemented with a strong laboratory component where students are able to implement and validate the concepts presented and learened learned on the master classes. The algorithms and applications are implementations based on Matlab.
Learning Outcomes
This course covers advanced research topics in computer vision. Building on the introductory materials in Computer Vision, this class will prepare students in both the theoretical foundations of computer vision and in the practical approaches to building real Computer Vision systems. The goal of this course is to give students the background and skills necessary to perform research in computer vision and to develop applications in domains such as robotics, biometrics, surveillance, biomodeical applications and graphics. Students should understand the strengths and weaknesses of current approaches to research problems and identify interesting open questions and future research directions. It also aims at improving students' critical reading and communication skills.
Work Placement(s)
NoSyllabus
1. Feature detection and matching: SIFT, SURF, HOG, MSER, FAST, BRIEF
2. Feature detection and matching: Bayesian-based and deep-learning based approaches
3. Object recognition: classic approaches
4. Object recognition: deep learning-based approaches
5. Visual detection and tracking
6. Activity recognition
7. Visual localization and mapping
Head Lecturer(s)
Jorge Manuel Moreira de Campos Pereira Batista
Assessment Methods
Assessment
Laboratory work or Field work: 50.0%
Exam: 50.0%
Bibliography
”Computer Vision: Algorithms and Applications”, Richard Szeliski
“An Invitation to 3-D Vision: From Images to Geometric Models”, Yi Ma, S. Soatto, J. Kosecka, S. Sastry
“Deep Learning”, I. Goodfellow, Y. Bengio, A. Courville
“Machine Learning: A Probabilistic Perspective”, Kevin P. Murphy
“Deep Learning for Computer Vision”, R. Shanmugamani