Suchergebnis: Katalogdaten im Frühjahrssemester 2016

Informatik Master Information
Vertiefungsfächer
Vertiefung in Visual Computing
Wahlfächer der Vertiefung in Visual Computing
NummerTitelTypECTSUmfangDozierende
252-0526-00LStatistical Learning Theory Information W4 KP2V + 1UJ. M. Buhmann
KurzbeschreibungThe course covers advanced methods of statistical learning :
PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
LernzielThe course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.
Inhalt# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification.
# Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come.
# Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error?
# Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include:

* Maximum Entropy
* Information Bottleneck
* Deterministic Annealing

# Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures.
# Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike.
# Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
Skriptno script; transparencies of the lectures will be made available.
LiteraturDuda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000.

Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.

L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Voraussetzungen / BesonderesRequirements:

basic knowledge of statistics, interest in statistical methods.

It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course.
252-0570-00LGame Programming Laboratory Information
Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.
W10 KP9PB. Sumner
KurzbeschreibungDas Ziel dieses Kurses ist ein vertieftes Verständnis der Technologie und der Programmierung von Computer-Spielen. Die Studierenden entwerfen und entwickeln in kleinen Gruppen ein Computer-Spiel und machen sich so vertraut mit der Kunst des Spiel-Programmierens.
LernzielDas Ziel dieses neuen Kurses ist es, die Studenten mit der Technologie und der Kunst des Programmierens von modernen dreidimensionalen Computerspielen vertraut zu machen.
InhaltDies ist ein neuer Kurs, der auf die Technologie von modernen dreidimensionalen Computerspielen eingeht. Während des Kurses werden die Studenten in kleinen Gruppen ein Computerspiel entwerfen und entwickeln. Der Schwerpunkt des Kurses wird auf technischen Aspekten der Spielentwicklung wie Rendering, Kinematographie, Interaktion, Physik, Animation und KI liegen. Zusätzlich werden wir aber auch Wert auf kreative Ideen für fortgeschrittenes Gameplay und visuelle Effekte legen.

Der Kurs wird als „Labor“ durchgeführt. Anstelle von traditionellen Vorträgen und Übungen wird der Kurs in einen praktischen, hands-on Ansatz durchgeführt. Wir treffen uns einmal wöchentlich um technische Aspekte zu besprechen und den Fortschritt der Entwicklung zu verfolgen. Wir planen das XNA Game Studio Express von Microsoft zu verwenden, eine Ansammlung von Bibliotheken und Werkzeugen um die Spieleentwicklung zu erleichtern. Die Entwicklung wird zunächst auf dem PC stattfinden, das Spiel wird dann im weiteren Verlauf auf der Xbox 360 Konsole eingesetzt.

Am Ende des Kurses werden die Resultate öffentlich präsentiert.
SkriptOnline XNA Dokumentation.
Voraussetzungen / BesonderesDie Anzahl der Teilnehmer wird begrenzt sein.

Voraussetzung für die Teilnahme sind:

- Gute Programmierkenntnisse (Java, C++, C#, o.ä.)

- Erfahrung in Computergrafik: Teilnehmer sollten mindestens die Vorlesung Visual Computing besucht haben. Wir empfehlen auch noch die weiterführenden Kurse Introduction to Computer Graphics, Surface Representations and Geometric Modeling, und Physically-based Simulation in Computer Graphics.
252-0579-00L3D Vision Information W4 KP3GM. Pollefeys, T. Sattler
KurzbeschreibungThe course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual inertial odometry (VIO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
LernzielAfter attending this course, students will:
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
InhaltThe goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
263-3700-00LUser Interface Engineering Information W4 KP2V + 1UO. Hilliges, F. Pece
KurzbeschreibungAn in-depth introduction to the core concepts of post-desktop user interface engineering. Current topics in UI research, in particular non-desktop based interaction, mobile device interaction, augmented and mixed reality, and advanced sensor and output technologies.
LernzielStudents will learn about fundamental aspects pertaining to the design and implementation of modern (non-desktop) user interfaces. Students will understand the basics of human cognition and capabilities as well as gain an overview of technologies for input and output of data. The core competency acquired through this course is a solid foundation in data-driven algorithms to process and interpret human input into computing systems. 

At the end of the course students should be able to understand and apply advanced hardware and software technologies to sense and interpret user input. Students will be able to develop systems that incorporate non-standard sensor and display technologies and will be able to apply data-driven algorithms in order to extract semantic meaning from raw sensor data.
InhaltUser Interface Engineering covers theoretical and practical aspects relating to the design and implementation of modern non-standard user interfaces. A particular area of interest are machine-learning based algorithms for input recognition in advanced non-desktop user interfaces, including UIs for mobile devices but also Augmented Reality UIs, gesture and multi-modal user interfaces. 

The course covers three main areas:
I) Basic principles of human cognition and perception (and their application for UIs)
II) (Hardware) technologies for user input sensing
III) Data-driven methods for input recognition (gestures, speech, etc.)

Specific topics include: 
* Model Human Processor (MHP) model - prediction of task completion times.
* Fitts' Law - measure of information load on human motor and cognitive system during user interaction.
* Touch sensor technologies (capacitive, resistive, force sensing etc).
* Data-driven algorithms for user input recognition:
- SVMs for classification and regression
- Randomized Decision Forests for gesture recognition and pose estimation
- Markov chains and HMMs for gesture and speech recognition
- Optical flow and other image processing and computer vision techniques
- Input filtering (Kalman)
* Applications of the above in HCI research
SkriptSlides and other materials will be available online. Lecture slides on a particular topic will typically not be made available prior the completion of that lecture.
LiteraturA detailed reading list will be made available on the course website.
Voraussetzungen / BesonderesPrerequisites: proficiency in a programming language such as C, programming methodology, problem analysis, program structure, etc. Normally met through an introductory course in programming in C, C++, Java.

The following courses are strongly recommended as prerequisite:
* "Human Computer Interaction"
* "Machine Learning"
* "Visual Computing" or "Computer Vision"

The course will be assessed by a written Midterm and Final examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises.
252-5706-00LMathematical Foundations of Computer Graphics and Vision Information W4 KP2V + 1UJ.‑C. Bazin, M. R. Oswald, C. Öztireli
KurzbeschreibungThis course presents the fundamental mathematical tools and concepts used in computer graphics and vision. Each theoretical topic is introduced in the context of practical vision or graphic problems, showcasing its importance in real-world applications.
LernzielThe main goal is to equip the students with the key mathematical tools necessary to understand state-of-the-art algorithms in vision and graphics. In addition to the theoretical part, the students will learn how to use these mathematical tools to solve a wide range of practical problems in visual computing. After successfully completing this course, the students will be able to apply these mathematical concepts and tools to practical industrial and academic projects in visual computing.
InhaltThe theory behind various mathematical concepts and tools will be introduced, and their practical utility will be showcased in diverse applications in computer graphics and vision. The course will cover topics in sampling, reconstruction, approximation, optimization, robust fitting, differentiation, quadrature and spectral methods. Applications will include 3D surface reconstruction, camera pose estimation, image editing, data projection, character animation, structure-aware geometry processing, and rendering.
227-1034-00LComputational Vision Information
For NSC Students:
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI402

Mind the enrolment deadlines at UZH:
Link
W6 KP2V + 1UD. Kiper, K. A. Martin
KurzbeschreibungThis course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
LernzielThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
InhaltThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
LiteraturBooks: (recommended references, not required)
1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997.
2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
252-0538-00LShape Modeling and Geometry Processing Information
Findet dieses Semester nicht statt.
W4 KP2V + 1UO. Sorkine Hornung
KurzbeschreibungThis course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry and interactive shape editing.
LernzielThe students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.
InhaltRecent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry and interactive shape editing.
SkriptSlides and course notes
Voraussetzungen / BesonderesPrerequisites:
Introduction to Computer Graphics, experience with C++ programming. Some background in geometry or computational geometry is helpful, but not necessary.
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