The problem of finding targeted medicine is a central problem in chemotherapy. From this point of view the ubiquitin-proteasome system is a highly promising object in the pharmaceutical approach. Proteasome plays a critical role in cellular protein degradation, cell cycle and apoptosis regulation. Proteasome inhibitors are substances blocking the actions of proteasome. Cancer cells are more sensitive to inhibition of the ubiquitin-proteasome system than normal cells. Therefore proteasome inhibitors have the potential to be successfully used in the cancer treatment. The study aimed to test various substances to identify possible proteasome inhibitors with the IncuCyteTM FLR image system and fluorometric microculture cytotoxicity assay. Using the IncuCyte FLR method allows for detecting changes in the molecular processes of living cells. To make proteasome inhibition visible the model cell line MelJuSoUbG76V-YFP is used which helps to detect alterations in proteasome activity by means of the yellow fluorescent protein enrichment in cells as a response to proteasome inhibition. Fluorometric microculture cytotoxicity assay is a method for the determination of cytotoxicity in human tumor cells. The study showed that substance #25 possessed a proteasome inhibitory capacity in a dose-dependent manner as demonstrated with the IncuCyte FLR image system. According to the fluorometric microculture cytotoxicity assay, substance #1 was the most stable and toxic. Substances #2 and #185 had selective toxicity against cancer cells and lower effects against normal cells. Combining IncuCyte FLR and fluorometric microculture cytotoxicity assay allows finding substances which act as proteasome inhibitors with high toxic effect.

Research and development of Non-Photorealistic Rendering algorithms has recently moved towards the use of computer vision algorithms to extract image features. The feature representation capabilities of image moments could be used effectively for the selection of brush-stroke characteristics for painterly-rendering applications. This technique is based on the estimation of local geometric features from the intensity distribution in small windowed images to obtain the brush size, color and direction. This thesis proposes an improvement of this method, by additionally extracting the connected components so that the adjacent regions of similar color are grouped for generating large and noticeable brush-stroke images. An iterative coarse-to-fine rendering algorithm is developed for painting regions of varying color frequencies. Improvements over the existing technique are discussed with several examples.

Digital Image-based Elasto-Tomography (DIET) is an emerging technology for non-invasive breast cancer screening. This technology relies on obtaining high resolution images of a breasts surface under high frequency actuation; typically 50-100Hz. Off-the-shelf digital cameras are unable to capture images directly at these speeds and current digital camera set-ups that are potentially capable of high speed image capture are either low in resolution, expensive, or occupy a volume too large to have them placed about the breast in a dense array. A method is presented for obtaining the required high speed image capture at a resolution of 1280x1024 (1.3 mega-pixels) and actuation frequency of 100Hz. The apparatus uses two Kodak CMOS KAC-9648 imaging sensors in combination with frame grabbers and the dSpace control system, to produce an automated image capture system. The final working system produced images that enabled effective 3D motion tracking of the surface of a silicon phantom actuated at 100Hz. The surface of the phantom was strobed at pre-selected phases from 0 to 360 degrees, and an image was captured for each phase. The times at which image capture occurred were calculated for a phase lag increment of 10 degrees resulting in an image effectively every 0.00028s for the actuator cycle of 0.01s. The comparison of the actual trigger times and pre-selected ideal trigger times gave a mean absolute error of 1.4%, thus demonstrating the accuracy of the final system.

This thesis is devoted to 2D and 3D modeling of urban environments using structured representations and grammars. Our approach introduces a semantic representation for buildings that encodes expected architectural constraints and is able to derive complex instances using fairly simple grammars. Furthermore, we propose two novel inference algorithms to parse images using such grammars. To this end, a steepest ascent hill climbing concept is considered to derive the grammar and the corresponding parameters from a single facade view. It combines the grammar constraints with the expected visual properties of the different architectural elements. Towards addressing more complex scenarios and incorporating 3D information, a second inference strategy based on evolutionary computational algorithms is adopted to optimize a two-component objective function introducing depth cues. The proposed framework was evaluated qualitatively and quantitatively on a benchmark of annotated facades, demonstrating robustness to challenging situations. Substantial improvement due to the strong grammatical context was shown in comparison to the performance of the same appearance models coupled with local priors. Therefore, our approach provides powerful techniques in response to increasing demand on large scale 3D modeling of real environments through compact, structured and semantic representations, while opening new perspectives for image understanding

In recent years, technological advances in creating, storing, and accessing digital facsimiles of print and manuscript documents has resulted in an explosion of digitization initiatives. While such initiatives commonly endorse the viewpoint that digital facsimiles either replace or successfully stand in for their physical originals, textual scholars, whose principle interest is in the text as material artifact, do not share this perspective. Thresholds of Engagement explores the ways textual scholars engage with textual artifacts, tests the limits of representation of digital facsimiles and of the interfaces that house them, and proposes a model for the relationship between physical texts and their digital counterparts that privileges the requirements of textual scholars. The digital-facsimile interface proposed in this study is designed to facilitate methods described by textual scholars in interview—methods of comparison, material analysis, pattern recognition, and modelling—using an open-source web-based approach that is accessible for individuals to innovate and build upon.

This study develops a 3D meso-scale fracture characterisation and modelling framework for better understanding of the failure mechanisms in concrete, by combining in-situ micro-scale X-ray computed tomography (XCT) experiments and XCT image-based finite element (FE) simulations. Firstly, sophisticated in-situ XCT experiments are conducted on concrete cubes under Brazilian-like, uniaxial and cyclic compression. Proper procedures for XCT image reconstruction and multi-phasic segmentation are identified. The fracture evolution at different loading stages is characterised and visualised as well as the detailed distributions of aggregates and voids. The Young's moduli of aggregate and cement are obtained by micro-indentation tests and used in XCT-image based asymptotic homogenisation simulations to calculate effective elastic constants of concrete cubes. The XCT technique proves very powerful in characterising and visualising the complicated 3D fracture evolution in concrete. The material properties and the segmented 3D images from the experiments are then used for FE fracture simulations with realistic aggregates, cement and voids. Image-based mesh generation algorithms are developed for 2D in a MATLAB code and identified for 3D in Simpleware. Cohesive interface elements are embedded within cement and aggregate-cement interfaces to simulate the complex nonlinear fracture. Extensive simulations of 40mm and 20mm cubes under compression and tension are carried out. Good agreements are achieved between the load-displacement curves and final crack patterns from the simulations and those from the compressive in-situ XCT tests. The XCT image-based modelling proves very promising in elucidating fundamental mechanisms of complicated crack initiation and propagation in concrete.

Abstract Automatic three-dimensional scene reconstruction from multiple images is a central problem in geometric computer vision. This thesis considers topics that are related to this problem area. New models and methods are presented for various tasks in such specific domains as camera calibration, image-based modeling and image matching. In particular, the main themes of the thesis are geometric camera calibration and quasi-dense image matching. In addition, a topic related to the estimation of two-view geometric relations is studied, namely, the computation of a planar homography from corresponding conics. Further, as an example of a reconstruction system, a structure-from-motion approach is presented for modeling sewer pipes from video sequences. In geometric camera calibration, the thesis concentrates on central cameras. A generic camera model and a plane-based camera calibration method are presented. The experiments with various real cameras show that the proposed calibration approach is applicable for conventional perspective cameras as well as for many omnidirectional cameras, such as fish-eye lens cameras. In addition, a method is presented for the self-calibration of radially symmetric central cameras from two-view point correspondences. In image matching, the thesis proposes a method for obtaining quasi-dense pixel matches between two wide baseline images. The method extends the match propagation algorithm to the wide baseline setting by using an affine model for the local geometric transformations between the images. Further, two adaptive propagation strategies are presented, where local texture properties are used for adjusting the local transformation estimates during the propagation. These extensions make the quasi-dense approach applicable for both rigid and non-rigid wide baseline matching. In this thesis, quasi-dense matching is additionally applied for piecewise image registration problems which are encountered in specific object recognition and motion segmentation. The proposed object recognition approach is based on grouping the quasi-dense matches between the model and test images into geometrically consistent groups, which are supposed to represent individual objects, whereafter the number and quality of grouped matches are used as recognition criteria. Finally, the proposed approach for dense two-view motion segmentation is built on a layer-based segmentation framework which utilizes grouped quasi-dense matches for initializing the motion layers, and is applicable under wide baseline conditions.

A estimação da iluminação é essencial para aplicações de realidade mista que se propõem a integrar elementos virtuais a cenas reais de maneira harmoniosa e sem a perda do realismo. Um dos métodos mais utilizados para fazer essa estimação é conhecido como iluminação baseada em imagens (Image Based Lighting - IBL), método que utiliza light probes para capturar a intensidade da iluminação incidente em uma cena. Porém, IBL estima a iluminação incidente apenas para um determinado instante e posição. Nesta dissertação, será avaliado um modelo de iluminação que utiliza séries temporais de imagens de light probes, obtidas de maneira esparsa em relação ao tempo, para renderizar cenas em instantes arbitrários. Novas cenas contendo objetos virtuais poderão ser renderizadas utilizando imagens de light probes artificiais, geradas a partir das amostras da iluminação originais. Diferentes funções de interpolação e aproximação são avaliadas para modelar o comportamento luminoso. As imagens finais produzidas pela metodologia também são verificadas por voluntários, de modo a determinar o impacto na qualidade de renderização em aplicações de realidade mista. Além da metodologia, foi desenvolvida uma ferramenta de software em forma de plugin para facilitar o uso de IBL temporalmente variável, permitindo assim a renderização realística de objetos virtuais para instantes arbitrários / Lighting estimation is essential for mixed reality applications that strive to integrate virtual elements into real scenes in a seamlessly fashion without sacrificing realism. A widely used method for lighting estimation is known as Image Based Lighting (IBL), which utilizes light probes to determine incident light intensity within a scene. However, IBL estimates light incidence only for a given time and position. In this dissertation, we assess a lighting model based on a time series of light probe images, obtained sparsely, to render scenes at arbitrary times. New scenes containing virtual objects can then be rendered by using artificial light probe images, which are generated from the original light samples. Different types of interpolation and approximation functions were evaluated for modeling lighting behavior. The resulting images were assessed for the impact in rendering quality for mixed reality applications by volunteers. In addition to the methodology, we also developed a software plugin to simplify the use of temporally variable IBL, allowing realistic rendering of virtual objects for arbitrary times

In this work, we have developed user interactive capabilities that allow us to perform segmentation and manipulation of patient-specific geometries required for Computational Fluid Dynamics (CFD) studies, entirely in image domain and within a single platform of ‘IAFEMesh'. Within this toolkit we have added commonly required manipulation capabilities for performing CFD on segmented objects by utilizing libraries like ITK, VTK and KWWidgets. With the advent of these capabilities we can now manipulate a single patient specific image into a set of possible cases we seek to study; which is difficult to do in commercially available software like VMTK, Slicer, MITK etc. due to their limited manipulation capabilities. Levelset representation of the manipulated geometries can be simulated in our flow solver (SCIMITAR-3D) without creating any surface or volumetric mesh. This image-levelset-flow framework offers few advantages. 1) We don't need to deal with the problems associated with mesh quality, edge connectivity related to mesh models, 2) and manipulations like boolean operation result in smooth, physically realizable entities which is challanging in mesh domain. We have validated our image-levelset-flow setup with the known results from previous studies. We have modified the algorithm by Krissian et al. and implemented it for the segmentation of Type-A aortic dissection. Finally, we implemented these capabilities to study the hemodynamics in Type-A aortic dissection. Our image based framework is a first of its kind and the hemodynamic study of Type-A dissection too is first study onto the best of our knowledge.

While real-time applications are nowadays routinely used in visualizing large nu- merical simulations and volumes, handling these large-scale datasets requires high-end graphics clusters or supercomputers to process and visualize them. However, not all users have access to powerful clusters. Therefore, it is challenging to come up with a visualization approach that provides insight to large-scale datasets on a single com- puter. Explorable images (EI) is one of the methods that allows users to handle large data on a single workstation. Although it is a view-dependent method, it combines both exploration and modification of visual aspects without re-accessing the original huge data. In this thesis, we propose a novel image-based method that applies the concept of EI in visualizing large flow-field pathlines data. The goal of our work is to provide an optimized image-based method, which scales well with the dataset size. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.