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2018


Model-based Optical Flow: Layers, Learning, and Geometry
Model-based Optical Flow: Layers, Learning, and Geometry

Wulff, J.

Tuebingen University, April 2018 (phdthesis)

Abstract
The estimation of motion in video sequences establishes temporal correspondences between pixels and surfaces and allows reasoning about a scene using multiple frames. Despite being a focus of research for over three decades, computing motion, or optical flow, remains challenging due to a number of difficulties, including the treatment of motion discontinuities and occluded regions, and the integration of information from more than two frames. One reason for these issues is that most optical flow algorithms only reason about the motion of pixels on the image plane, while not taking the image formation pipeline or the 3D structure of the world into account. One approach to address this uses layered models, which represent the occlusion structure of a scene and provide an approximation to the geometry. The goal of this dissertation is to show ways to inject additional knowledge about the scene into layered methods, making them more robust, faster, and more accurate. First, this thesis demonstrates the modeling power of layers using the example of motion blur in videos, which is caused by fast motion relative to the exposure time of the camera. Layers segment the scene into regions that move coherently while preserving their occlusion relationships. The motion of each layer therefore directly determines its motion blur. At the same time, the layered model captures complex blur overlap effects at motion discontinuities. Using layers, we can thus formulate a generative model for blurred video sequences, and use this model to simultaneously deblur a video and compute accurate optical flow for highly dynamic scenes containing motion blur. Next, we consider the representation of the motion within layers. Since, in a layered model, important motion discontinuities are captured by the segmentation into layers, the flow within each layer varies smoothly and can be approximated using a low dimensional subspace. We show how this subspace can be learned from training data using principal component analysis (PCA), and that flow estimation using this subspace is computationally efficient. The combination of the layered model and the low-dimensional subspace gives the best of both worlds, sharp motion discontinuities from the layers and computational efficiency from the subspace. Lastly, we show how layered methods can be dramatically improved using simple semantics. Instead of treating all layers equally, a semantic segmentation divides the scene into its static parts and moving objects. Static parts of the scene constitute a large majority of what is shown in typical video sequences; yet, in such regions optical flow is fully constrained by the depth structure of the scene and the camera motion. After segmenting out moving objects, we consider only static regions, and explicitly reason about the structure of the scene and the camera motion, yielding much better optical flow estimates. Furthermore, computing the structure of the scene allows to better combine information from multiple frames, resulting in high accuracies even in occluded regions. For moving regions, we compute the flow using a generic optical flow method, and combine it with the flow computed for the static regions to obtain a full optical flow field. By combining layered models of the scene with reasoning about the dynamic behavior of the real, three-dimensional world, the methods presented herein push the envelope of optical flow computation in terms of robustness, speed, and accuracy, giving state-of-the-art results on benchmarks and pointing to important future research directions for the estimation of motion in natural scenes.

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Official link DOI Project Page [BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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[BibTex]

[BibTex]

2011


Multi-Modal Scene Understanding for Robotic Grasping
Multi-Modal Scene Understanding for Robotic Grasping

Bohg, J.

(2011:17):vi, 194, Trita-CSC-A, KTH Royal Institute of Technology, KTH, Computer Vision and Active Perception, CVAP, Centre for Autonomous Systems, CAS, KTH, Centre for Autonomous Systems, CAS, December 2011 (phdthesis)

Abstract
Current robotics research is largely driven by the vision of creating an intelligent being that can perform dangerous, difficult or unpopular tasks. These can for example be exploring the surface of planet mars or the bottom of the ocean, maintaining a furnace or assembling a car. They can also be more mundane such as cleaning an apartment or fetching groceries. This vision has been pursued since the 1960s when the first robots were built. Some of the tasks mentioned above, especially those in industrial manufacturing, are already frequently performed by robots. Others are still completely out of reach. Especially, household robots are far away from being deployable as general purpose devices. Although advancements have been made in this research area, robots are not yet able to perform household chores robustly in unstructured and open-ended environments given unexpected events and uncertainty in perception and execution.In this thesis, we are analyzing which perceptual and motor capabilities are necessary for the robot to perform common tasks in a household scenario. In that context, an essential capability is to understand the scene that the robot has to interact with. This involves separating objects from the background but also from each other.Once this is achieved, many other tasks become much easier. Configuration of object scan be determined; they can be identified or categorized; their pose can be estimated; free and occupied space in the environment can be outlined.This kind of scene model can then inform grasp planning algorithms to finally pick up objects.However, scene understanding is not a trivial problem and even state-of-the-art methods may fail. Given an incomplete, noisy and potentially erroneously segmented scene model, the questions remain how suitable grasps can be planned and how they can be executed robustly.In this thesis, we propose to equip the robot with a set of prediction mechanisms that allow it to hypothesize about parts of the scene it has not yet observed. Additionally, the robot can also quantify how uncertain it is about this prediction allowing it to plan actions for exploring the scene at specifically uncertain places. We consider multiple modalities including monocular and stereo vision, haptic sensing and information obtained through a human-robot dialog system. We also study several scene representations of different complexity and their applicability to a grasping scenario. Given an improved scene model from this multi-modal exploration, grasps can be inferred for each object hypothesis. Dependent on whether the objects are known, familiar or unknown, different methodologies for grasp inference apply. In this thesis, we propose novel methods for each of these cases. Furthermore,we demonstrate the execution of these grasp both in a closed and open-loop manner showing the effectiveness of the proposed methods in real-world scenarios.

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pdf [BibTex]

2011


pdf [BibTex]


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Iterative path integral stochastic optimal control: Theory and applications to motor control

Theodorou, E. A.

University of Southern California, University of Southern California, Los Angeles, CA, 2011 (phdthesis)

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PDF [BibTex]

PDF [BibTex]


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Learning of grasp selection based on shape-templates

Herzog, A.

Karlsruhe Institute of Technology, 2011 (mastersthesis)

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[BibTex]

[BibTex]


Spatial Models of Human Motion
Spatial Models of Human Motion

Soren Hauberg

University of Copenhagen, 2011 (phdthesis)

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PDF [BibTex]

PDF [BibTex]

2005


Adaptation of Central Pattern Generators to Preexisting Mechanical Structure
Adaptation of Central Pattern Generators to Preexisting Mechanical Structure

Spröwitz, A.

Technische Universität Ilmenau, Ilmenau, 2005 (mastersthesis)

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[BibTex]

2005


[BibTex]