Refine
Has Fulltext
- yes (2)
Is part of the Bibliography
- yes (2)
Document Type
- Doctoral Thesis (2) (remove)
Language
- English (2)
Keywords
- 3d point clouds (1)
- Alpha-Aktivität (1)
- Dreidimensionale Bildverarbeitung (1)
- EEG (1)
- Electroencephalographie (1)
- Frontal asymmetry (1)
- Motivation (1)
- Punktwolke (1)
- bilateral BAS model (1)
- change detection (1)
Institute
Frontal asymmetry, a construct invented by Richard Davidson, linking positive and negative valence as well as approach and withdrawal motivation to lateralized frontal brain activation has been investigated for over thirty years. The frontal activation patterns described as relevant were measured via alpha-band frequency activity (8-13 Hz) as a measurement of deactivation in electroencephalography (EEG) for homologous electrode pairs, especially for the electrode position F4/ F3 to account for the frontal relative lateralized brain activation.
Three different theories about frontal activation patterns linked to motivational states were investigated in two studies. The valence theory of Davidson (1984; 1998a; 1998b) and its extension to the motivational direction theory by Harmon-Jones and Allen (1998) refers to the approach motivation with relative left frontal brain activity (indicated by relative right frontal alpha activity) and to withdrawal motivation with relative right frontal brain activation (indicated by relative left frontal alpha activity). The second theory proposed by Hewig and colleagues (2004; 2005; 2006) integrates the findings of Davidson and Harmon – Jones and Allen with the reinforcement sensitivity theory of Jeffrey A. Gray (1982, 1991). Hewig sees the lateralized frontal approach system and withdrawal system proposed by Davidson as subsystems of the behavioral activation system proposed by Gray and bilateral frontal activation as a biological marker for the behavioral activation system. The third theory investigated in the present studies is the theory from Wacker and colleagues (2003; 2008; 2010) where the frontal asymmetrical brain activation patterns are linked to the revised reinforcement sensitivity theory of Gray and McNaughton (2000). Here, right frontal brain activity (indicated by lower relative right frontal alpha activity) accounts for conflict, behavioral inhibition and activity of the revised behavioral inhibition system, while left frontal brain activation (indicated by lower relative left frontal alpha activity) stands for active behavior and the activity of the revised behavioral activation system as well as the activation of the revised flight fight freezing system. In order to investigate these three theories, a virtual reality T-maze paradigm was introduced to evoke motivational states in the participants, offering the opportunity to measure frontal brain activation patterns via EEG and behavior simultaneously in the first study. In the second study the virtual reality paradigm was additionally compared to mental imagery and a movie paradigm, two well-known state inducing paradigms in the research field of frontal asymmetry.
In the two studies, there was confirming evidence for the theory of Hewig and colleages (2004; 2005; 2006), showing higher bilateral frontal activation for active behavior and lateralized frontal activation patterns for approach (left frontal brain activation) and avoidance (right frontal brain activation) behavior. Additionally a limitation for the capability model of anterior brain asymmetry proposed by Coan and colleagues (2006), where the frontal asymmetry should be dependent on the relevant traits driving the frontal asymmetry pattern if a relevant situation occurs, could be found. As the very intense virtual reality paradigm did not lead to a difference of frontal brain activation patterns compared to the mental imagery paradigm or the movie paradigm for the traits of the participants, the trait dependency of the frontal asymmetry in a relevant situation might not be given, if the intensity of the situation exceeds a certain level. Nevertheless there was an influence of the traits in the virtual reality T-maze paradigm, because the shown behavior in the maze was trait-dependent.
The implications of the findings are multifarious, leading from possible objective personality testing via diversification of the virtual reality paradigm to even clinical implications for depression treatments based on changes in the lateralized frontal brain activation patterns for changes in the motivational aspects, but also for changes in bilateral frontal brain activation when it comes to the drive and preparedness for action in patients. Finally, with the limitation of the capability model, additional variance in the different findings about frontal asymmetry can be explained by taking the intensity of a state manipulation into account.
Affordable prices for 3D laser range finders and mature software solutions for registering multiple point clouds in a common coordinate system paved the way for new areas of application for 3D point clouds. Nowadays we see 3D laser scanners being used not only by digital surveying experts but also by law enforcement officials, construction workers or archaeologists. Whether the purpose is digitizing factory production lines, preserving historic sites as digital heritage or recording environments for gaming or virtual reality applications -- it is hard to imagine a scenario in which the final point cloud must also contain the points of "moving" objects like factory workers, pedestrians, cars or flocks of birds. For most post-processing tasks, moving objects are undesirable not least because moving objects will appear in scans multiple times or are distorted due to their motion relative to the scanner rotation.
The main contributions of this work are two postprocessing steps for already registered 3D point clouds. The first method is a new change detection approach based on a voxel grid which allows partitioning the input points into static and dynamic points using explicit change detection and subsequently remove the latter for a "cleaned" point cloud. The second method uses this cleaned point cloud as input for detecting collisions between points of the environment point cloud and a point cloud of a model that is moved through the scene.
Our approach on explicit change detection is compared to the state of the art using multiple datasets including the popular KITTI dataset. We show how our solution achieves similar or better F1-scores than an existing solution while at the same time being faster.
To detect collisions we do not produce a mesh but approximate the raw point cloud data by spheres or cylindrical volumes. We show how our data structures allow efficient nearest neighbor queries that make our CPU-only approach comparable to a massively-parallel algorithm running on a GPU. The utilized algorithms and data structures are discussed in detail. All our software is freely available for download under the terms of the GNU General Public license. Most of the datasets used in this thesis are freely available as well. We provide shell scripts that allow one to directly reproduce the quantitative results shown in this thesis for easy verification of our findings.