Header logo is


2019


Thumb xl lic overview
Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

ics

PDF [BibTex]

2019


PDF [BibTex]


no image
Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


no image
Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]


no image
Load-inducing factors in instructional design: Process-related advances in theory and assessment

Wirzberger, M.

TU Chemnitz, 2019 (phdthesis)

Abstract
This thesis addresses ongoing controversies in cognitive load research related to the scope and interplay of resource-demanding factors in instructional situations on a temporal perspective. In a novel approach, it applies experimental task frameworks from basic cognitive research and combines different methods for assessing cognitive load and underlying cognitive processes. Taken together, the obtained evidence emphasizes a process-related reconceptualization of the existing theoretical cognitive load framework and underlines the importance of a multimethod-approach to continuous cognitive load assessment. On a practical side, it informs the development of adaptive algorithms and the learner-aligned design of instructional support and thus leverages a pathway towards intelligent educational assistants.

re

link (url) [BibTex]


no image
Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

ei

[BibTex]

[BibTex]


no image
Novel X-ray lenses for direct and coherent imaging

Sanli, U. T.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2013


no image
Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

ei pn

PDF [BibTex]

2013


PDF [BibTex]


Thumb xl cover3
Statistics on Manifolds with Applications to Modeling Shape Deformations

Freifeld, O.

Brown University, August 2013 (phdthesis)

Abstract
Statistical models of non-rigid deformable shape have wide application in many fi elds, including computer vision, computer graphics, and biometry. We show that shape deformations are well represented through nonlinear manifolds that are also matrix Lie groups. These pattern-theoretic representations lead to several advantages over other alternatives, including a principled measure of shape dissimilarity and a natural way to compose deformations. Moreover, they enable building models using statistics on manifolds. Consequently, such models are superior to those based on Euclidean representations. We demonstrate this by modeling 2D and 3D human body shape. Shape deformations are only one example of manifold-valued data. More generally, in many computer-vision and machine-learning problems, nonlinear manifold representations arise naturally and provide a powerful alternative to Euclidean representations. Statistics is traditionally concerned with data in a Euclidean space, relying on the linear structure and the distances associated with such a space; this renders it inappropriate for nonlinear spaces. Statistics can, however, be generalized to nonlinear manifolds. Moreover, by respecting the underlying geometry, the statistical models result in not only more e ffective analysis but also consistent synthesis. We go beyond previous work on statistics on manifolds by showing how, even on these curved spaces, problems related to modeling a class from scarce data can be dealt with by leveraging information from related classes residing in di fferent regions of the space. We show the usefulness of our approach with 3D shape deformations. To summarize our main contributions: 1) We de fine a new 2D articulated model -- more expressive than traditional ones -- of deformable human shape that factors body-shape, pose, and camera variations. Its high realism is obtained from training data generated from a detailed 3D model. 2) We defi ne a new manifold-based representation of 3D shape deformations that yields statistical deformable-template models that are better than the current state-of-the- art. 3) We generalize a transfer learning idea from Euclidean spaces to Riemannian manifolds. This work demonstrates the value of modeling manifold-valued data and their statistics explicitly on the manifold. Specifi cally, the methods here provide new tools for shape analysis.

ps

pdf Project Page [BibTex]


Thumb xl phd
Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms

Geiger, A.

Karlsruhe Institute of Technology, Karlsruhe Institute of Technology, April 2013 (phdthesis)

Abstract
Visual 3D scene understanding is an important component in autonomous driving and robot navigation. Intelligent vehicles for example often base their decisions on observations obtained from video cameras as they are cheap and easy to employ. Inner-city intersections represent an interesting but also very challenging scenario in this context: The road layout may be very complex and observations are often noisy or even missing due to heavy occlusions. While Highway navigation and autonomous driving on simple and annotated intersections have already been demonstrated successfully, understanding and navigating general inner-city crossings with little prior knowledge remains an unsolved problem. This thesis is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences. The model takes advantage of monocular information in the form of vehicle tracklets, vanishing lines and semantic labels. Additionally, the benefit of stereo features such as 3D scene flow and occupancy grids is investigated. Motivated by the impressive driving capabilities of humans, no further information such as GPS, lidar, radar or map knowledge is required. Experiments conducted on 113 representative intersection sequences show that the developed approach successfully infers the correct layout in a variety of difficult scenarios. To evaluate the importance of each feature cue, experiments with different feature combinations are conducted. Additionally, the proposed method is shown to improve object detection and object orientation estimation performance.

avg ps

pdf [BibTex]

pdf [BibTex]


Thumb xl jampani 13 thesis
A Study of X-Ray Image Perception for Pneumoconiosis Detection

Jampani, V.

IIIT-Hyderabad, Hyderabad, India, January 2013 (mastersthesis)

Abstract
Pneumoconiosis is an occupational lung disease caused by the inhalation of industrial dust. Despite the increasing safety measures and better work place environments, pneumoconiosis is deemed to be the most common occupational disease in the developing countries like India and China. Screening and assessment of this disease is done through radiological observation of chest x-rays. Several studies have shown the significant inter and intra reader observer variation in the diagnosis of this disease, showing the complexity of the task and importance of the expertise in diagnosis. The present study is aimed at understanding the perceptual and cognitive factors affecting the reading of chest x-rays of pneumoconiosis patients. Understanding these factors helps in developing better image acquisition systems, better training regimen for radiologists and development of better computer aided diagnostic (CAD) systems. We used an eye tracking experiment to study the various factors affecting the assessment of this diffused lung disease. Specifically, we aimed at understanding the role of expertize, contralateral symmetric (CS) information present in chest x-rays on the diagnosis and the eye movements of the observers. We also studied the inter and intra observer fixation consistency along with the role of anatomical and bottom up saliency features in attracting the gaze of observers of different expertize levels, to get better insights into the effect of bottom up and top down visual saliency on the eye movements of observers. The experiment is conducted in a room dedicated to eye tracking experiments. Participants consisting of novices (3), medical students (12), residents (4) and staff radiologists (4) were presented with good quality PA chest X-rays, and were asked to give profusion ratings for each of the 6 lung zones. Image set consisting of 17 normal full chest x-rays and 16 single lung images are shown to the participants in random order. Time of the diagnosis and the eye movements are also recorded using a remote head free eye tracker. Results indicated that Expertise and CS play important roles in the diagnosis of pneumoconiosis. Novices and medical students are slow and inefficient whereas, residents and staff are quick and efficient. A key finding of our study is that the presence of CS information alone does not help improve diagnosis as much as learning how to use the information. This learning appears to be gained from focused training and years of experience. Hence, good training for radiologists and careful observation of each lung zone may improve the quality of diagnostic results. For residents, the eye scanning strategies play an important role in using the CS information present in chest radiographs; however, in staff radiologists, peripheral vision or higher-level cognitive processes seems to play role in using the CS information. There is a reasonably good inter and intra observer fixation consistency suggesting the use of similar viewing strategies. Experience is helping the observers to develop new visual strategies based on the image content so that they can quickly and efficiently assess the disease level. First few fixations seem to be playing an important role in choosing the visual strategy, appropriate for the given image. Both inter-rib and rib regions are given equal importance by the observers. Despite reading of chest x-rays being highly task dependent, bottom up saliency is shown to have played an important role in attracting the fixations of the observers. This role of bottom up saliency seems to be more in lower expertize groups compared to that of higher expertize groups. Both bottom up and top down influence of visual fixations seems to change with time. The relative role of top down and bottom up influences of visual attention is still not completely understood and it remains the part of future work. Based on our experimental results, we have developed an extended saliency model by combining the bottom up saliency and the saliency of lung regions in a chest x-ray. This new saliency model performed significantly better than bottom-up saliency in predicting the gaze of the observers in our experiment. Even though, the model is a simple combination of bottom-up saliency maps and segmented lung masks, this demonstrates that even basic models using simple image features can predict the fixations of the observers to a good accuracy. Experimental analysis suggested that the factors affecting the reading of chest x-rays of pneumoconiosis are complex and varied. A good understanding of these factors definitely helps in the development of better radiological screening of pneumoconiosis through improved training and also through the use of improved CAD tools. The presented work is an attempt to get insights into what these factors are and how they modify the behavior of the observers.

ps

pdf [BibTex]

pdf [BibTex]


no image
Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

ei

[BibTex]

[BibTex]


no image
Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

ei

[BibTex]

[BibTex]


no image
Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

ei

[BibTex]

[BibTex]


no image
Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Wang, Z.

Technical University Darmstadt, Germany, 2013 (phdthesis)

ei

[BibTex]


no image
Quantum kinetic theory for demagnetization after femtosecond laser pulses

Teeny, N.

Universität Stuttgart, Stuttgart, 2013 (mastersthesis)

mms

[BibTex]

[BibTex]

2010


no image
Approximate Inference in Graphical Models

Hennig, P.

University of Cambridge, November 2010 (phdthesis)

ei pn

Web [BibTex]

2010


Web [BibTex]


no image
Bayesian Inference and Experimental Design for Large Generalised Linear Models

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2010 (phdthesis)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


no image
Predictive Representations For Sequential Decision Making Under Uncertainty

Boularias, A.

Université Laval, Quebec, Canada, July 2010 (phdthesis)

Abstract
The problem of making decisions is ubiquitous in life. This problem becomes even more complex when the decisions should be made sequentially. In fact, the execution of an action at a given time leads to a change in the environment of the problem, and this change cannot be predicted with certainty. The aim of a decision-making process is to optimally select actions in an uncertain environment. To this end, the environment is often modeled as a dynamical system with multiple states, and the actions are executed so that the system evolves toward a desirable state. In this thesis, we proposed a family of stochastic models and algorithms in order to improve the quality of of the decision-making process. The proposed models are alternative to Markov Decision Processes, a largely used framework for this type of problems. In particular, we showed that the state of a dynamical system can be represented more compactly if it is described in terms of predictions of certain future events. We also showed that even the cognitive process of selecting actions, known as policy, can be seen as a dynamical system. Starting from this observation, we proposed a panoply of algorithms, all based on predictive policy representations, in order to solve different problems of decision-making, such as decentralized planning, reinforcement learning, or imitation learning. We also analytically and empirically demonstrated that the proposed approaches lead to a decrease in the computational complexity and an increase in the quality of the decisions, compared to standard approaches for planning and learning under uncertainty.

ei

PDF [BibTex]


no image
Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


no image
Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET)

Mantlik, F.

Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

ei

[BibTex]

[BibTex]


no image
Finding Gene-Gene Interactions using Support Vector Machines

Rakitsch, B.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

ei

[BibTex]

[BibTex]


no image
Accurate Prediction of Protein-Coding Genes with Discriminative Learning Techniques

Schweikert, G.

Technische Universität Berlin, Germany, 2010 (phdthesis)

ei

[BibTex]


no image
Structural and Relational Data Mining for Systems Biology Applications

Georgii, E.

Eberhard Karls Universität Tübingen, Germany , 2010 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


no image
Population Coding in the Visual System: Statistical Methods and Theory

Macke, J.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

ei

[BibTex]

[BibTex]


no image
Bayesian Methods for Neural Data Analysis

Gerwinn, S.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


no image
Clustering with Neighborhood Graphs

Maier, M.

Universität des Saarlandes, Saarbrücken, Germany, 2010 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


no image
Detecting and modeling time shifts in microarray time series data applying Gaussian processes

Zwießele, M.

Eberhard Karls Universität Tübingen, Germany, 2010 (thesis)

ei

[BibTex]

[BibTex]


no image
Detecting the mincut in sparse random graphs

Köhler, R.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

ei

[BibTex]

[BibTex]


no image
A wider view on encoding and decoding in the visual brain-computer interface speller system

Martens, S.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

ei

[BibTex]


Thumb xl screen shot 2018 02 03 at 4.30.26 pm
Roombots: Design and Implementation of a Modular Robot for Reconfiguration and Locomotion

Spröwitz, A.

EPFL, Lausanne, Lausanne, 2010 (phdthesis)

dlg

DOI [BibTex]


no image
Goal-Oriented Control of Self-Organizing Behavior in Autonomous Robots

Martius, G.

Georg-August-Universität Göttingen, 2010 (phdthesis)

al

link (url) [BibTex]


no image
Statics and dynamics of simple fluids on chemically patterned substrates

Dörfler, F.

Universität Stuttgart, Stuttgart, Germany, 2010 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


no image
Entnetzung verspannter Filme

Reindl, A.

Universität Stuttgart, Stuttgart, 2010 (mastersthesis)

mms

[BibTex]

[BibTex]


no image
Advanced ferromagnetic nanostructures

Goll, D.

Universität Stuttgart, Stuttgart, 2010 (phdthesis)

mms

[BibTex]

[BibTex]


no image
Wasserstoff in funktionellen Dünnschichtsystemen

Honert, J.

Universität Stuttgart, Stuttgart, 2010 (mastersthesis)

mms

[BibTex]

[BibTex]

2009


no image
Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract
This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

ei

PDF [BibTex]

2009


PDF [BibTex]


no image
A PAC-Bayesian Approach to Structure Learning

Seldin, Y.

Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, September 2009 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


no image
Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery

Blaschko, MB.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


no image
Motor Control and Learning in Table Tennis

Mülling, K.

Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

ei

[BibTex]

[BibTex]