@phdthesis{amirkhanov-2021-diss, title = "Visual Analysis of Defects", author = "Aleksandr Amirkhanov", year = "2021", abstract = "In everyday life, we use many objects on which we rely and expect them to work correctly. We use phones to communicate with friends, bicycles to commute, payment cards to buy groceries. However, due to defects, these objects may fail at some time, leading to adverse outcomes. Modern industry continually improves the quality of outputs (e.g., products and services) and ensures that they meet their specifications. A common quality management strategy is the defect analysis used to identify and control outputs that do not conform to their specifications. Traditional defect analysis methods are often manual and, therefore, time-consuming procedures. To build more efficient solutions, defect analysis increasingly employs visual analytics techniques. These techniques automatize and enhance the up-to-now manual analysis steps and support new visual approaches for defect representations that resolve existing defects without introducing new ones. In this dissertation, visual analytics techniques applied to defect analysis are referred to as visual analysis of defects. Being a rapidly developing area, the domain of visual analysis of defects is still missing a formalized basis. This dissertation presents and discusses a workflow for the visual analysis of defects based on the plan-do-check-act cycle of continual improvement. The workflow consists of four steps: defect prevention, control of defective outputs, performance evaluation, and improvement. During the defect prevention step, domain experts plan the design and development processes to ensure that intended results can be achieved while forecasting risks and opportunities. During the control of defective outputs step, domain experts implement the processes and control defects arising throughout these processes. During the performance evaluation step, domain experts ensure that defective outputs are identified by measuring the object's characteristics. During the improvement step, domain experts explore possible actions that improve the object quality. This dissertation presents four solutions that advance the visual analysis of defects at the four distinct steps of the workflow. The first solution corresponds to the defect prevention step and provides a preview of dental treatment. It helps dental technicians to identify the most suitable treatment option and avoid cases when patients are unsatisfied with the results due to poor denture aesthetics. The second solution corresponds to the control of defective outputs step and supports dental technicians in designing aesthetic and functional dentures. The approach provides immediate visual feedback on a change in the denture design, which helps to evaluate how the change affects aesthetics. The third solution corresponds to the performance evaluation step and supports material engineers in investigating the damage mechanism in composite materials. First, the system captures and measures various defects such as matrix fracture, fiber/matrix debonding, fiber pull-out, and fiber fracture. Later, users analyze these defects using several interactive visualization techniques. The fourth solution corresponds to the improvement step and visualizes 4D dynamical systems describing various phenomena. The solution enables the 4D representation of dynamical systems and allows the 4D representation to seamlessly transition into, familiar to the user, lower-dimensional plots.", pages = "178", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Research Unit of Computer Graphics, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien ", keywords = "visualization, visual analytics, defect analysis, dentistry, material science, mathematical visualization", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/amirkhanov-2021-diss/", } @incollection{heinzl-2018-ct-book, title = "Processing, Analysis and Visualization of CT Data in Industrial X-Ray Computed Tomography", author = "Christoph Heinzl and Aleksandr Amirkhanov and Johannes Kastner", year = "2018", abstract = "In an almost inexhaustible multitude of possibilities, CT allows to inspect highly complex systems and materials. Compared to other testing techniques CT provides results in a quick way: It is nondestructive and does not interfere with the specimen, it allows non-touching characterizations and what is most important CT allows to characterize hidden or internal features. However, CT would not have reached its current status in engineering without the achievements and possibilities in data processing. Only through processing, analysis and visualization of CT data, detailed insights into previously unachievable analyses are facilitated. Novel means of data analysis and visualization illustrate highly complex problems by means of clear and easy to understand renderings. In this chapter, we explore various aspects starting from the generalized data analysis pipeline, aspects of processing, analysis and visualization for metrology, nondestructive testing as well as specialized analyses.", booktitle = "Processing, Analysis and Visualization of CT Data", publisher = "Springer", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/heinzl-2018-ct-book/", } @article{Red_Andreas_2015_FFT, title = "Fuzzy feature tracking", author = "Andreas Reh and Aleksandr Amirkhanov and Johann Kastner and Eduard Gr\"{o}ller and Christoph Heinzl", year = "2015", abstract = "In situ analysis is becoming increasingly important in the evaluation of existing as well as novel materials and components. In this domain, specialists require answers on questions such as: How does a process change internal and external structures of a component? or How do the internal features evolve?In this work, we present a novel integrated visual analysis tool to evaluate series of X-ray Computed Tomography (XCT) data. We therefore process volume datasets of a series of XCT scans, which non-destructively cover the evolution of a process by in situ scans. After the extraction of individual features, a feature tracking algorithm is applied to detect changes of features throughout the series as events. We distinguish between creation, continuation, split, merge and dissipation events. As an explicit tracking is not always possible, we introduce the computation of a Tracking Uncertainty. We visualize the data together with the determined events in multiple linked-views, each emphasizing individual aspects of the 4D-XCT dataset series: A Volume Player and a 3D Data View show the spatial feature information, whereas the global overview of the feature evolution is visualized in the Event Explorer. The Event Explorer allows for interactive exploration and selection of the events of interest. The selection is further used as basis to calculate a Fuzzy Tracking Graph visualizing the global evolution of the features over the whole series.We finally demonstrate the results and advantages of the proposed tool using various real world applications, such as a wood shrinkage analysis and an AlSiC alloy under thermal load. Graphical abstractDisplay Omitted HighlightsWe calculate a Tracking Uncertainty in order to find correlated features.The Event Explorer shows a global overview of events and feature properties.The Fuzzy Tracking Graph is used to track features through all time-steps.The Volume Player shows control elements to traverse the steps of a dataset series.", month = dec, journal = "Computers and Graphics", number = "PB", volume = "53", pages = "177--184", URL = "https://www.cg.tuwien.ac.at/research/publications/2015/Red_Andreas_2015_FFT/", } @misc{Ganuza_2015, title = "Interactive Semi-Automatic Categorization for Spinel Group Minerals", author = " Mar\'{i}a Luj\'{a}n Ganuza and Maria Florencia Gargiulo and Gabriela Ferracutti and Silvia Castro and Ernesto Bjerg and Eduard Gr\"{o}ller and Kresimir Matkovic", year = "2015", month = oct, event = "IEEE VIS 2015 ", editor = "IEEE", Conference date = "Poster presented at IEEE VIS 2015 (2015-10)", URL = "https://www.cg.tuwien.ac.at/research/publications/2015/Ganuza_2015/", } @article{Froehler_Berhnard_2015_ESM, title = "Multimodal Visualization and Analysis of Spectral and XCT Data", author = "Bernhard Fr\"{o}hler and Artem Amirkhanov and Johann Kastner and Eduard Gr\"{o}ller and Christoph Heinzl", year = "2015", abstract = "An increasing number of industrial applications demand a comprehensive analysis of both structural and chemical composition. Typically, non-destructive testing techniques focus on either structural or chemical characterization but do not deliver both. 3D X-Ray Computed Tomography (XCT) scans are well-suited for determining the internal and external structure of an object at high resolution. The attenuation value it delivers can however be the same or very similar for different materials. For a detailed chemical analysis XCT is therefore combined with spectral characterization techniques such as K-Edge Absorptiometry or X-ray Fluorescence Spectroscopy. In this paper, we are extending a previously introduced framework for visualization and analysis of specimens scanned with these two modalities in multiple ways: For better understanding the dependencies between the spectral energy levels, we propose Spectral Similarity Maps. Spectral Functional Boxplots visualize the statistical distribution of the spectral data. The Spectrum Explor-er improves the analysis of specimens of unknown composition. We demonstrate the usefulness of our techniques on several use cases.", month = apr, journal = "Computer Graphic Forum", volume = "33", number = "3", note = "appeared in June 2014", issn = "2411-5428", pages = "91--100", URL = "https://www.cg.tuwien.ac.at/research/publications/2015/Froehler_Berhnard_2015_ESM/", } @article{amirkhanov-2014-ama, title = "InSpectr: Multi-Modal Exploration, Visualization, and Analysis of Spectral Data", author = "Artem Amirkhanov and Bernhard Fr\"{o}hler and Johann Kastner and Eduard Gr\"{o}ller and Christoph Heinzl", year = "2014", abstract = "This paper addresses the increasing demand in industry for methods to analyze and visualize multimodal data involving a spectral modality. Two data modalities are used: high-resolution X-ray computed tomography (XCT) for structural characterization and low-resolution X-ray fluorescence (XRF) spectral data for elemental decomposition. We present InSpectr, an integrated tool for the interactive exploration and visual analysis of multimodal, multiscalar data. The tool has been designed around a set of tasks identified by domain experts in the fields of XCT and XRF. It supports registered single scalar and spectral datasets optionally coupled with element maps and reference spectra. InSpectr is instantiating various linked views for the integration of spatial and non-spatial information to provide insight into an industrial component’s structural and material composition: views with volume renderings of composite and individual 3D element maps visualize global material composition; transfer functions defined directly on the spectral data and overlaid pie-chart glyphs show elemental composition in 2D slice-views; a representative aggregated spectrum and spectra density histograms are introduced to provide a global overview in the spectral view. Spectral magic lenses, spectrum probing and elemental composition probing of points using a pie-chart view and a periodic table view aid the local material composition analysis. Two datasets are investigated to outline the usefulness of the presented techniques: a 3D virtually created phantom with a brass metal alloy and a real-world 2D water phantom with insertions of gold, barium, and gadolinium. Additionally a detailed user evaluation of the results is provided.", month = jun, journal = "Computer Graphics Forum", volume = "33", number = "3", note = "Article first published online: 12 JUL 2014", pages = "91--100", keywords = "multi-modal data, XRF, industrial computed tomography, linked views, spectral data", URL = "https://www.cg.tuwien.ac.at/research/publications/2014/amirkhanov-2014-ama/", } @article{Groeller_2014_UPS, title = "Guest editorial—Uncertainty and parameter space analysis in visualization", author = "Christoph Heinzl and Stefan Bruckner and Eduard Gr\"{o}ller", year = "2014", abstract = "Within the past decades visualization advanced to a powerful means of exploring and analyzing data. Recent developments in both hard- and software contributed to previously unthinkable evaluations and visualizations of data with strongly increasing sizes and levels of complexity. Providing just insight into available data of a problem seems not to be sufficient anymore: Uncertainty and parameter space analyses in visualization are becoming more prevalent and may be found in astronomic, (bio)-medical, industrial, and engineering applications. The major goal is to find out, at which stage of the pipeline - from data acquisition to the final rendering of the output image - how much uncertainty is introduced and consequently how the desired result (e.g., a dimensional measurement feature) is affected. Therefore effective methods and techniques are required by domain specialists, which help to understand how data is generated, how reliable is the generated data, and where and why data is uncertain. Furthermore, as the problems to investigate are becoming increasingly complex, also finding suitable algorithms providing the desired solution tends to be more difficult. Additional questions may arise, e.g., how does a slight parameter change modify the result, how stable is a parameter, in which range is a parameter stable or which parameter set is optimal for a specific problem. Metaphorically speaking, an algorithm for solving a problem may be seen as finding a path through some rugged terrain (the core problem) ranging from the high grounds of theory to the haunted swamps of heuristics. There are many different paths through this terrain with different levels of comfort, length, and stability. Finding all possible paths corresponds in our case to doing an analysis of all possible parameters of a problem solving algorithm, which yields a typically multi-dimensional parameter space. This parameter space allows for an analysis of the quality and stability of a specific parameter set. In many cases of conventional visualization approaches the issues of uncertainty and parameter space analyses are neglected. For a long time, uncertainty - if visualized at all - used to be depicted as blurred data. But in most cases the uncertainty in the base data is not considered at all and just the quantities of interest are calculated. And even to calculate these quantities of interest, too often an empirically found parameter set is used to parameterize the underlying algorithms without exploring its sensitivity to changes and without exploring the whole parameter space to find the global or a local optimum. This tutorial aims to open minds and to look at our data and the parameter sets of our algorithms with a healthy skepticism. In the tutorial we combine uncertainty visualization and parameter space analyses which we believe is essential for the acceptance and applicability of future algorithms and techniques. The tutorial provides six sessions starting with an overview of uncertainty visualization including a historical perspective, uncertainty modeling and statistical visualization. The second part of the tutorial will be dedicated to structural uncertainty, parameter space analysis, industrial applications of uncertainty visualization and an outlook in this domain. ", month = jun, journal = "Computer & Graphics", volume = "41", pages = "A1--A2", URL = "https://www.cg.tuwien.ac.at/research/publications/2014/Groeller_2014_UPS/", } @inproceedings{Groeller_Eduard_2014_UCT, title = "Uncertainty in CT Metrology: Visualizations for Exploration and Analysis of Geometric Tolerances", author = "Artem Amirkhanov and Bernhard Fr\"{o}hler and Michael Reiter and Johann Kastner and Eduard Gr\"{o}ller and Christoph Heinzl", year = "2014", abstract = "Industrial 3D X-ray computed tomography (3DXCT) is increasingly applied as a technique for metrology applications. In contrast to comventional metrology tools such as coordinate measurement machines (CMMs). 3DXCT only estimates the exact position of the specimen’s surface and is subjected to a specific set of artifact types. These factors result in uncertainty that is present in the data. Previous work by Amirkhanov et. al [2] presented a tool prototype that is taking such uncertainty into account when measuring geometric tolerances such as straightness, circularity, or flatness. In this paper we extend the previous work with two more geometric tolerance types: cylindricity and angularity. We provide methods and tools for visualization, inspection, and analysis of these tolerances. For the cylindricity tolerance we employ neighboring profiles visualization, box-plot overview, and interactive 3D view. We evaluate applicability and usefulness our methods on a new TP03 data set, and present results and new potential use cases.", month = feb, location = "Wels, Austria", issn = "978-3-8440-2557-6", event = "iCT Conference 2014", booktitle = "Proceedings of 5th Conference on Industrial Computed Tomography (iCT Conference 2014)", journal = "Proceedings of iCT 2014", pages = "189--195", keywords = "metrology, level-of-details, uncertainty visualization, Industrial 3D computed tomography", URL = "https://www.cg.tuwien.ac.at/research/publications/2014/Groeller_Eduard_2014_UCT/", } @article{reh-2013, title = "MObjects - A Novel Method for the Visualization and Interactive Exploration of Defects in Industrial XCT Data", author = "Andreas Reh and Christian Gusenbauer and Johann Kastner and Eduard Gr\"{o}ller and Christoph Heinzl", year = "2013", abstract = "This paper describes an advanced visualization method for the analysis of defects in industrial 3D X-Ray Computed Tomography (XCT) data. We present a novel way to explore a high number of individual objects in a dataset, e.g., pores, inclusions, particles, fibers, and cracks demonstrated on the special application area of pore extraction in carbon fiber reinforced polymers (CFRP). After calculating the individual object properties volume, dimensions and shape factors, all objects are clustered into a mean object (MObject). The resulting MObject parameter space can be explored interactively. To do so, we introduce the visualization of mean object sets (MObject Sets) in a radial and a parallel arrangement. Each MObject may be split up into sub-classes by selecting a specific property, e.g., volume or shape factor, and the desired number of classes. Applying this interactive selection iteratively leads to the intended classifications and visualizations of MObjects along the selected analysis path. Hereby the given different scaling factors of the MObjects down the analysis path are visualized through a visual linking approach. Furthermore the representative MObjects are exported as volumetric datasets to serve as input for successive calculations and simulations. In the field of porosity determination in CFRP non-destructive testing practitioners use representative MObjects to improve ultrasonic calibration curves. Representative pores also serve as input for heat conduction simulations in active thermography. For a fast overview of the pore properties in a dataset we propose a local MObjects visualization in combination with a color-coded homogeneity visualization of cells. The advantages of our novel approach are demonstrated using real world CFRP specimens. The results were evaluated through a questionnaire in order to determine the practicality of the MObjects visualization as a supportive tool for domain specialists.", month = dec, journal = "IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE Scientific Visualization 2013)", volume = "19", number = "12", pages = "2906--2915", keywords = "porosity, carbon fiber reinforced polymers, parameter space analysis, MObjects, 3D X-ray computed tomography", URL = "https://www.cg.tuwien.ac.at/research/publications/2013/reh-2013/", } @inproceedings{amirkhanov_2013_AMA, title = "Fuzzy CT Metrology: Dimensional Measurements on Uncertain Data", author = "Artem Amirkhanov and Christoph Heinzl and Christoph Kuhn and Johann Kastner and Eduard Gr\"{o}ller", year = "2013", abstract = "Metrology through geometric dimensioning and tolerancing is an important instrument applied for industrial manufacturing and quality control. Typically tactile or optical coordinate measurement machines (CMMs) are used to perform dimensional measurements. In recent years industrial 3D X-ray computed tomography (3DXCT) has been increasingly applied for metrology due to the development of XCT systems with higher accuracy and their ability to capture both internal and external structures of a specimen within one scan. Using 3DXCT the location of the specimen surface is estimated based on the scanned attenuation coefficients. As opposed to tactile or optical measurement techniques, the surface is not explicit and implies a certain positional uncertainty depending on artifacts and noise in the scan data and the used surface extraction algorithm. Moreover, conventional XCT measurement software does not consider uncertainty in the data. In this work we present techniques which account for uncertainty arising in the XCT metrology data flow. Our technique provides the domain experts with uncertainty visualizations, which extend the XCT metrology workflow on different levels. The developed techniques are integrated into a tool utilizing linked views, smart 3D tolerance tagging and plotting functionalities. The presented system is capable of visualizing the uncertainty of measurements on various levels-of-detail. Commonly known geometric tolerance indications are provided as smart tolerance tags. Finally, we incorporate the uncertainty of the data as a context in commonly used measurement plots. The proposed techniques provide an augmented insight into the reliability of geometric tolerances while maintaining the daily workflow of domain specialists, giving the user additional information on the nature of areas with high uncertainty. The presented techniques are evaluated based on domain experts feedback in collaboration with our company partners.", month = may, isbn = "978-80-223-3377-1", publisher = "Comenius university, Bratislava, Slovakia", location = "Smolenice, Slovak Republic", booktitle = "SCCG 2013 - 29th Proceedings Spring conference on Computer Graphics", pages = "93--101", keywords = "metrology, uncertainty visualization, level-of-details, industrial 3D computed tomography", URL = "https://www.cg.tuwien.ac.at/research/publications/2013/amirkhanov_2013_AMA/", } @article{PMI_AR_2012, title = "Porosity Maps – Interactive Exploration and Visual Analysis of Porosity in Carbon Fiber Reinforced Polymers", author = "Andreas Reh and B Plank and J Kastner and Eduard Gr\"{o}ller and Christoph Heinzl", year = "2012", abstract = "In this work a novel method for the characterization of porosity in carbon fiber reinforced polymers (CFRP) is presented. A visualization pipeline for the interactive exploration and visual analysis of CFRP specimens is developed to enhance the evaluation workflow for non-destructive testing (NDT) practitioners based on specified tasks. Besides quantitative porosity determination and the calculation of local pore properties, i.e., volume, surface, dimensions and shape factors, we employ a drill-down approach to explore pores in a CFRP specimen. We introduce Porosity Maps (PM), to allow for a fast porosity evaluation of the specimen. Pores are filtered in two stages. First a region of interest is selected in the porosity maps. Second, pores are filtered with parallel coordinates according to their local properties. Furthermore a histogram-based best-viewpoint widget was implemented to visualize the quality of viewpoints on a sphere. The advantages of our approach are demonstrated using real world CFRP specimens. We are able to show that our visualization-driven approach leads to a better evaluation of CFRP components than existing reference methods.", month = jun, journal = "Computer Graphics Forum,", volume = "31", number = "3", pages = "1185--1194", keywords = "Interaction Techniques, Methodology and techniques", URL = "https://www.cg.tuwien.ac.at/research/publications/2012/PMI_AR_2012/", } @article{amirkhanov-2011, title = "Projection-Based Metal-Artifact Reduction for Industrial 3D X-ray Computed Tomography", author = "Artem Amirkhanov and Christoph Heinzl and Michael Reiter and Johann Kastner and Eduard Gr\"{o}ller", year = "2011", abstract = "Multi-material components, which contain metal parts surrounded by plastic materials, are highly interesting for inspection using industrial 3D X-ray computed tomography (3DXCT). Examples of this application scenario are connectors or housings with metal inlays in the electronic or automotive industry. A major problem of this type of components is the presence of metal, which causes streaking artifacts and distorts the surrounding media in the reconstructed volume. Streaking artifacts and dark-band artifacts around metal components significantly influence the material characterization (especially for the plastic components). In specific cases these artifacts even prevent a further analysis. Due to the nature and the different characteristics of artifacts, the development of an efficient artifact-reduction technique in reconstruction-space is rather complicated. In this paper we present a projection-space pipeline for metal-artifacts reduction. The proposed technique first segments the metal in the spatial domain of the reconstructed volume in order to separate it from the other materials. Then metal parts are forward-projected on the set of projections in a way that metal-projection regions are treated as voids. Subsequently the voids, which are left by the removed metal, are interpolated in the 2D projections. Finally, the metal is inserted back into the reconstructed 3D volume during the fusion stage. We present a visual analysis tool, allowing for interactive parameter estimation of the metal segmentation. The results of the proposed artifact-reduction technique are demonstrated on a test part as well as on real world components. For these specimens we achieve a significant reduction of metal artifacts, allowing an enhanced material characterization.", month = dec, journal = "IEEE Transactions on Visualization and Computer Graphics", volume = "17", number = "12", issn = "1077-2626", pages = "2193--2202", keywords = "Metal-artifact reduction, multi-material components, 3D X-ray computed tomography, visual analysis", URL = "https://www.cg.tuwien.ac.at/research/publications/2011/amirkhanov-2011/", } @article{amirkhanov2010AMA, title = "Visual Optimality and Stability Analysis of 3DCT Scan Positions", author = "Artem Amirkhanov and Christoph Heinzl and Michael Reiter and Eduard Gr\"{o}ller", year = "2010", abstract = "Industrial cone-beam X-Ray computed tomography (CT) systems often face problems due to artifacts caused by a bad placement of the specimen on the rotary plate. This paper presents a visual-analysis tool for CT systems, which provides a simulation-based preview and estimates artifacts and deviations of a specimen’s placement using the corresponding 3D geometrical surface model as input. The presented tool identifies potentially good or bad placements of a specimen and regions of a specimen, which cause the major portion of artefacts. The tool can be used for a preliminary analysis of the specimen before CT scanning, in order to determine the optimal way of placing the object. The analysis includes: penetration lengths, placement stability and an investigation in Radon space. Novel visualization techniques are applied to the simulation data. A stability widget is presented for determining the placement parameters’ robustness. The performance and the comparison of results provided by the tool compared with real world data is demonstrated using two specimens.", month = oct, journal = "IEEE Transactions on Visualization and Computer Graphics", pages = "Page 1477 --1487", URL = "https://www.cg.tuwien.ac.at/research/publications/2010/amirkhanov2010AMA/", } @inproceedings{Reiter_2009_IXIA, title = "Improvement of X-Ray image acquisition using a GPU based 3DCT simulation tool", author = "Michael Reiter and Muhammad Muddassir Malik and Christoph Heinzl and Dietmar Salaberger and Eduard Gr\"{o}ller and Hubert Lettenbauer and Johann Kastner", year = "2009", abstract = "This paper presents a simulation tool for industrial X-Ray computed tomography (CT) systems which is able to predict the results of real measurements. Such a prediction helps the technician in measurement technology to minimize artefacts by using optimal measurement parameters and therefore it helps to get more accurate results. The presented simulation software offers an implementation for CPU’s and GPU’s. The performance difference between these implementa-tions is shown, for a specific test part. Furthermore a parameter variation has been carried out, to illustrate the influence of the acquisition settings. We use a multi-image view tool to compare and evaluate the acquired dataset series which contains CT data gained with different X-Ray source voltages and a different number of projections.", month = may, note = "not peer reviewed, will appear", location = "Wels, Austria", booktitle = "International Conference on Quality Control by Artificial Vision", keywords = "Computed tomography, CT simulation, Industrial X-Ray Imaging", URL = "https://www.cg.tuwien.ac.at/research/publications/2009/Reiter_2009_IXIA/", } @article{malik-2009-CVFA, title = "Computation and Visualization of Fabrication Artifacts", author = "Muhammad Muddassir Malik and Christoph Heinzl and Eduard Gr\"{o}ller", year = "2009", abstract = "This paper proposes a novel technique to measure fabrication artifacts through direct comparison of a reference surface model with the corresponding industrial CT volume. Our technique uses the information from the surface model to locate corresponding points in the CT dataset. We then compute various comparison metrics to measure differences (fabrication artifacts) between the two datasets. The differences are presented to the user both visually as well as quantitatively. Our comparison techniques are divided into two groups, namely geometry-driven comparison techniques and visual-driven comparison techniques. The geometry-driven techniques provide an overview, while the visual-driven techniques can be used for a localized examination.", month = feb, journal = "Journal of WSCG", volume = "17", number = "1", issn = "Online: 1213-6964 (printed: 1213 – 6972)", pages = "17--24", URL = "https://www.cg.tuwien.ac.at/research/publications/2009/malik-2009-CVFA/", }