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Computed tomography (CT) and magnetic resonance imaging (MRI) in the medical area deliver huge amounts of data, which doctors have to handle in a short time. These data can be visualised efficiently with direct volume rendering. Consequently most direct volume rendering applications on the market are specialised on medical tasks or integrated in medical visualisa- tion environments. Highly evolved applications for tasks like diagnosis or surgery simulation are available in this area. In the last years, however, another area is making increasing use of com- puted tomography. Companies like phoenix |x-ray, founded in 1999 pro- duce CT-scanners especially dedicated to industrial applications like non destructive material testing (NDT). Of course an application like NDT has different demands on the visualisation than a typical medical application. For example a typical task for non destructive testing would be to high- light air inclusions (pores) in a casting. These inclusions usually cover a very small area and are very hard to classify only based on their density value as this would also highlight the air around the casting. This thesis presents multiple approaches to improve the rendering of in- dustrial CT data, most of them based on higher dimensional transfer func- tions. Therefore the existing volume renderer application of VRVis was extended with a user interface to create such transfer functions and exist- ing render modes were adapted to profit from the new transfer functions. These approaches are especially suited to improve the visualisation of sur- faces and material boundaries as well as pores. The resulting renderings make it very easy to identify these features while preserving interactive framerates.
We introduce linear expressions for unrestricted dags (directed acyclic graphs) and finite deterministic and nondeterministic automata operating on them. Those dag automata are a conservative extension of the Tu,u-automata of Courcelle on unranked, unordered trees and forests. Several examples of dag languages acceptable and not acceptable by dag automata and some closure properties are given.
Research has shown that people recognize personality, gender, inner states and many other items of information by simply observing human motion. Therefore the expressive human motion seems to be a valuable non-verbal communication channel. On the quest for more believable characters in virtual three dimensional simulations a great amount of visual realism has been achieved during the last decades. However, while interacting with synthetic characters in real-time simulations, often human users still sense an unnatural stiffness. This disturbance in believability is generally caused by a lack of human behavior simulation. Expressive motions, which convey personality and emotional states can be of great help to create more plausible and life-like characters. This thesis explores the feasibility of an automatic generation of emotionally expressive animations from given neutral character motions. Such research is required since common animation methods, such as manual modeling or motion capturing techniques, are too costly to create all possible variations of motions needed for interactive character behavior. To investigate how emotions influence human motion relevant literature from various research fields has been viewed and certain motion rules and features have been extracted. These movement domains were validated in a motion analysis and implemented in a system in an exemplary manner capable of automating the expression of angry, sad and happy states in a virtual character through its body language. Finally, the results were evaluated in user test.
The automatic detection of position and orientation of subsea cables and pipelines in camera images enables underwater vehicles to make autonomous inspections. Plants like algae growing on top and nearby cables and pipelines however complicate their visual detection: the determination of the position via border detection followed by line extraction often fails. Probabilistic approaches are here superior to deterministic approaches. Through modeling probabilities it is possible to make assumptions on the state of the system even if the number of extracted features is small. This work introduces a new tracking system for cable/pipeline following in image sequences which is based on particle filters. Extensive experiments on realistic underwater videos show robustness and performance of this approach and demonstrate advantages over previous works.
Colonoscopy is the gold standard for the detection of colorectal polyps that can progress into cancer. In such an examination, physicians search for polyps in endoscopic images. Thereby polyps can be removed. To support experts with a computer-aided diagnosis system, the University of Koblenz-Landau currently makes some efforts in research different methods for automatic detection. Comparable to traditional pattern recognition systems, features are initially extracted and a classifier is trained on such data. Afterwards, unknown endoscopic images can be classified with the previously trained classifier. This thesis concentrates on the extension of the feature extraction module in the existing system. New detection methods are compared to existing techniques. Several features are implemented, incorporating Graylevel Co-occurrence Matrices, Local Binary Patterns and Discrte Wavelet Transform. Different modifications on those features are applied and evaaluated.
This thesis evaluates automated techniques to remove objects from an image and proposed several modifications for the specific application of removing a colour checker from structure dominated images. The selection of approaches covers the main research field of image inpainting as well as an approach used in medical image processing. Their results are investigated to disclose their applicability to removing objects from structure-intense images. The advantages and disadvantages discovered in the process are then used to propose several modifications for an adapted inpainting approach suitable for removing the colour checker.
This thesis focuses on the utilization of modern graphics hardware (GPU) for visualization and computation purposes, especially of volumetric data from medical imaging. The considerable increase in raw computing power in recent years has turned commodity systems into high-performance workstations. In combination with the direct rendering capabilities of graphics hardware, "visual computing" and "computational steering" approaches on large data sets have become feasible. In this regard several example applications and concepts such as the "ray textures" have been developed and are discussed in detail. As the amount of data to be processed and visualized is steadily increasing, memory and bandwidth limitations require compact representations of the data. While the compression of image data has been investigated extensively in the past, the thesis addresses possibilities of performing computations directly on the compressed data. Therefore, different categories of algorithms are identified and represented in the wavelet domain. By using special variants of the compressed format, efficient implementations of essential image processing algorithms are possible and demonstrate the potential of the approach. From the technical perspective, the GPU-based framework "Cascada" has been developed in the course of this thesis. The introduction of object-oriented concepts to shader programming, as well as a hierarchical representation of computation and/or visualization procedures led to a simplified utilization of graphics hardware while maintaining competitive performance. This is shown with different implementations throughout the contributions, as well as two clinical projects in the field of diagnosis assistance. On the one hand the semi-automatic segmentation of low-resolution MRI data sets of the human liver is evaluated. On the other hand different possibilities in assessing abdominal aortic aneurysms are discussed; both projects make use of graphics hardware. In addition, "Cascada" provides extensions towards recent general-purpose programming architectures and a modular design for future developments.
The goal of this minor thesis is to integrate a robotic arm into an existing robotics software. A robot built on top of this stack should be able to participate successfully RoboCup @Home league. The robot Lisa (Lisa is a service android) needs to manipulate objects, lifting them from shelves or handing them to people. Up to now, the only possibility to do this was a small gripper attached to the robot platform. A "Katana Linux Robot" of Swiss manufacturer Neuronics has been added to the robot for this thesis. This arm needs a driver software and path planner, so that the arm can reach its goal object "intelligently", avoiding obstacles and creating smooth, natural motions.
Texture-based text detection in digital images using wavelet features and support vector machines
(2010)
In this bachelor thesis a new texture-based approach for the detection of text in digital images is presented. The procedure can be essentially divided into two main tasks, in detection of text blocks and detection of individual words, whereby the individual words are extracted from the detected text blocks. Roughly, the developed method acts with multiple support vector machines, which classify possible text regions of an image into real text regions, using wavelet-based features. In the process the possible text regions are defifined by edge projections with diσerent orientations. The results of the approach are X/Y coordinates, width and height of rectangular regions of an image, which contains individual words. This knowledge can be further processed, for example by an optical character recognition software to get the important and useful text information.
We present a non-linear camera pose estimator, which is able to handle a combined input of point and line feature correspondences. For three or more correspondences, the estimator works on any arbitrary number and choice of the feature type, which provides an estimation of the pose on a preferably small and flexible amount of 2D-3D correspondences. We also give an analysis of different minimization techniques, parametrizations of the pose data, and of error measurements between 2D and 3D data. These will be tested for the usage of point features, lines and the combination case. The result shows the most stable and fast working non-linear parameter set for pose estimation in model-based tracking.
This paper introduces Vocville, a causal online game for learning vocabularies. I am creating this application for my master thesis of my career as a "Computervisualist" (computer visions) for the University of Koblenz - Landau. The application is an online browser game based on the idea of the really successful Facebook game FarmVille. The application is seperated in two parts; a Grails application manages a database which holds the game objects like vocabulary, a Flex/Flash application generates the actual game by using these data. The user can create his own home with everything in it. For creating things, the user has to give the correct translation of the object he wants to create several times. After every query he has to wait a certain amount of time to be queried again. When the correct answer is given sufficient times, the object is builded. After building one object the user is allowed to build others. After building enough objects in one area (i.e. a room, a street etc.) the user can activate other areas by translating all the vocabularies of the previous area. Users can also interact with other users by adding them as neighbors and then visiting their homes or sending them gifts, for which they have to fill in the correct word in a given sentence.
Tractography on HARDI data
(2011)
Diffusion weighted imaging is an important modality in clinical imaging and the only possibility to gain insight into the human brain noninvasively and in-vivo. The applications of this imaging technique are diversified. It is used to study the brain, its structure, development and the functionality of the different areas. Further, important fields of application are neurosurgical planning, examinations of pathologies, investigation of Alzheimer-, strokes, and multiple sclerosis. This thesis gives a brief introduction to MRI and diffusion MRI. Based on this, the mostly used data representation in diffusion MRI in clinical imaging, the diffusion tensor, is introduced. As the diffusion tensor suffers from severe limitations new techniques subsumed under the term HARDI (high angular resolution diffusion imaging) are introduced and discussed in detail. Further, an extensive introduction to tractography, approaches that aim at reconstructing neuronal fibers, is given. Based on the knowledge fromthe theoretical part established tractography algorithms are redesigned to handle HARDI data and, thus, improve the reconstruction of neuronal fibers. Among these algorithms, a novel approach is presented that successfully reconstructs fibers on phantom data as well as on human brain data. Further, a novel global classification approach is presented to cluster voxels according to their diffusion properties.
The following thesis analyses the functionality and programming capabilitiesrnof compute shaders. For this purpose, chapter 2 gives an introductionrnto compute shaders by showing how they work and how they can be programmed. In addition, the interaction of compute shaders and OpenGL 4.3 is shown through two introductory examples. Chapter 3 describes an NBodyrnsimulation that has been implemented in order to show the computational power of compute shaders and the use of shared memory. Then it is shown in chapter 4 how compute shaders can be used for physical simulationsrnand where problems may arise. In chapter 5 a specially conceived and implemented algorithm for detecting lines in images is described and then compared with the Hough transform. Lastly, a final conclusion is drawn in chapter 6.
Object recognition is a well-investigated area in image-based computer vision and several methods have been developed. Approaches based on Implicit Shape Models have recently become popular for recognizing objects in 2D images, which separate objects into fundamental visual object parts and spatial relationships between the individual parts. This knowledge is then used to identify unknown object instances. However, since the emergence of aσordable depth cameras like Microsoft Kinect, recognizing unknown objects in 3D point clouds has become an increasingly important task. In the context of indoor robot vision, an algorithm is developed that extends existing methods based on Implicit Shape Model approaches to the task of 3D object recognition.
Real-time graphics applications are tending to get more realistic and approximate real world illumination gets more reasonable due to improvement of graphics hardware. Using a wide variation of algorithms and ideas, graphics processing units (GPU) can simulate complex lighting situations rendering computer generated imagery with complicated effects such as shadows, refraction and reflection of light. Particularly, reflections are an improvement of realism, because they make shiny materials, e.g. brushed metals, wet surfaces like puddles or polished floors, appear more realistic and reveal information of their properties such as roughness and reflectance. Moreover, reflections can get more complex, depending on the view: a wet surface like a street during rain for example will reflect lights depending on the distance of the viewer, resulting in more streaky reflection, which will look more stretched, if the viewer is locatedrnfarther away from the light source. This bachelor thesis aims to give an overview of the state-of-the-art in terms of rendering reflections. Understanding light is a basic need to understand reflections and therefore a physical model of light and its reflection will be covered in section 2, followed by the motivational section 2.2, that will give visual appealing examples for reflections from the real world and the media. Coming to rendering techniques, first, the main principle will be explained in section 3 followed by a short general view of a wide variety of approaches that try to generate correct reflections in section 4. This thesis will describe the implementation of three major algorithms, that produce plausible local reflections. Therefore, the developed framework is described in section 5, then three major algorithms will be covered, that are common methods in most current game and graphics engines: Screen space reflections (SSR), parallax-corrected cube mapping (PCCM) and billboard reflections (BBR). After describing their functional principle, they will be analysed of their visual quality and the possibilities of their real-time application. Finally they will be compared to each other to investigate the advantages and disadvantages over each other. In conclusion, the gained experiences will be described by summarizing advantages and disadvantages of each technique and giving suggestions for improvements. A short perspective will be given, trying to create a view of upcoming real-time rendering techniques for the creation of reflections as specular effects.
The mitral valve is one of the four valves in the human heart. It is located in the left heart chamber and its function is to control the blood flow from the left atrium to the left ventricle. Pathologies can lead to malfunctions of the valve so that blood can flow back to the atrium. Patients with a faulty mitral valve function may suffer from fatigue and chest pain. The functionality can be surgically restored, which is often a long and exhaustive intervention. Thorough planning is necessary to ensure a safe and effective surgery. This can be supported by creating pre-operative segmentations of the mitral valve. A post-operative analysis can determine the success of an intervention. This work will combine existing and new ideas to propose a new approach to (semi-)automatically create such valve models. The manual part can guarantee a high quality model and reliability, whereas the automatic part contributes to saving valuable labour time.
The main contributions of the automatic algorithm are an estimated semantic separation of the two leaflets of the mitral valve and an optimization process that is capable of finding a coaptation-line and -area between the leaflets. The segmentation method can perform a fully automatic segmentation of the mitral leaflets if the annulus ring is already given. The intermediate steps of this process will be integrated into a manual segmentation method so a user can guide the whole procedure. The quality of the valve models generated by the method proposed in this work will be measured by comparing them to completely manually segmented models. This will show that commonly used methods to measure the quality of a segmentation are too general and do not suffice to reflect the real quality of a model. Consequently the work at hand will introduce a set of measurements that can qualify a mitral valve segmentation in more detail and with respect to anatomical landmarks. Besides the intra-operative support for a surgeon, a segmented mitral valve provides additional benefits. The ability to patient-specifically obtain and objectively describe the valve anatomy may be the base for future medical research in this field and automation allows to process large data sets with reduced expert dependency. Further, simulation methods that use the segmented models as input may predict the outcome of a surgery.
Proceedings of the 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding
(2015)
The Proceedings of the 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding include publications (extended abstracts), that cover but are not limited to the following topics: - Mathematical Theory of Pattern Recognition, Image and Speech Processing, Analysis, Recognition and Understanding. - Cognitive Technologies, Information Technologies, Automated Systems and Software for Pattern Recognition, Image, Speech and Signal Processing, Analysis and Understanding - Databases, Knowledge Bases, and Linguistic Tools - Special-Purpose Architectures, Software and Hardware Tools - Vision and Sensor Data Interpretation for Robotics - Industrial, Medical, Multimedia and Other Applications - Algorithms, Software, Automated Systems and Information Technologies in Bioinformatics and Medical Informatics. The workshop took place from December 1st-5th, 2014, at the University of Koblenz-Landau in Koblenz, Germany.
In this thesis we present an approach to track a RGB-D camera in 6DOF andconstruct 3D maps. We first acquire, register and synchronize RGB and depth images. After preprocessing we extract FAST features and match them between two consecutive frames. By depth projection we regain the z-value for the inlier correspondences. Afterwards we estimate the camera motion by 3D point set alignment between the correspondence set using least-squares. This local motion estimate is incrementally applied to a global transformation. Additionally wernpresent methods to build maps based on point cloud data acquired by a RGB-D camera. For map creation we use the OctoMap framework and optionally create a colored point cloud map. The system is evaluated with the widespread RGB-D benchmark.
Statistical Shape Models (SSMs) are one of the most successful tools in 3Dimage analysis and especially medical image segmentation. By modeling the variability of a population of training shapes, the statistical information inherent in such data are used for automatic interpretation of new images. However, building a high-quality SSM requires manually generated ground truth data from clinical experts. Unfortunately, the acquisition of such data is a time-consuming, error-prone and subjective process. Due to this effort, the majority of SSMs is often based on a limited set of this ground truth training data, which makes the models less statistically meaningful. On the other hand, image data itself is abundant in clinics from daily routine. In this work, methods for automatically constructing a reliable SSM without the need of manual image interpretation from experts are proposed. Thus, the training data is assumed to be the result of any segmentation algorithm or may originate from other sources, e.g. non-expert manual delineations. Depending on the algorithm, the output segmentations will contain errors to a higher or lower degree. In order to account for these errors, areas of low probability of being a boundary should be excluded from the training of the SSM. Therefore, the probabilities are estimated with the help of image-based approaches. By including many shape variations, the corrupted parts can be statistically reconstructed. Two approaches for reconstruction are proposed - an Imputation method and Weighted Robust Principal Component Analysis (WRPCA). This allows the inclusion of many data sets from clinical routine, covering a lot more variations of shape examples. To assess the quality of the models, which are robust against erroneous training shapes, an evaluation compares the generalization and specificity ability to a model build from ground truth data. The results show, that especially WRPCA is a powerful tool to handle corrupted parts and yields to reasonable models, which have a higher quality than the initial segmentations.