Image segmentation with adaptive region growing based on a polynomial surface model

A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces

Experimental Flow Investigation of a Truncated Ideal Contour Nozzle

Various tests showed a significant side load peak for low nozzle pressure ratios during engine start-up and shut down phase. DLR Lampoldshausen carried out tests to examine the flow field in a truncated ideal contour nozzle for low NPR. For NPR20 a slight concave shaped Mach disk was found. Its curvature is limited to the centre and its height trend correlates with measured side loads. A concave shaped Mach disk being responsible for re-attached flows at low NPR could be excluded. The experiments were accompanied by numerical simulations of the flow field on various pressure ratios with regards on the shock pattern. The predicted Mach disk shape compares well with the experiments

Improving precision of material extrusion 3D printing by in-situ monitoring and predicting 3D geometric deviation using Conditional Adversarial Networks

The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the geometric deviation of the predicted profiles by making compensation to the CAD model

Using Ground Penetrating Radar and attribute analysis for identifying depositional units in a fluvial-aeolian interaction environment: The Guandacol Valley, northwest Argentina

This paper deals with the application of the Ground Penetrating Radar (GPR) method and the analysis of attributes of the GPR data to characterize and interpret a fluvial-aeolian interaction field located in the Guandacol Valley, northwest Argentina. Several profiles over dunes, interdunes, aeolian mesoforms, and fluvial channels have been acquired. Each data section is analyzed by using standard images of the amplitude of the electric field, as well as representations of different attributes of the reflections such as contrast, dip, curvature, parallelism, and RMS frequency. The analysis of attributes improves the interpretation of the subsurface, by quantifying and making evident properties of the reflection patterns that characterize the sedimentary units. The information obtained using the GPR profiles allows defining seven radar packages, which are useful for reconstructing the internal structure of the fluvial-aeolian succession. Packages 1, 2 and 3 illustrate the stratification of different types of low-sinuosity and high-sinuosity aeolian dunes, as well as aeolian mesoforms. Package 4 corresponds to horizontal or low-angle inclined reflectors obtained in both sandy interdunes and upper parts of several aeolian dunes. A muddy bed that covers most of the area (package 5) probably indicates a period of climate amelioration linked to a high level of the water table. The fluvial component of the fluvial-aeolian succession exhibits two different packages; package 6 represents the infill of partially incised fluvial channels with frequent incisions (concave-up bounding surfaces) and bars (convex-up surfaces). Package 7 is composed of the stacking of parallel to subparallel horizontal reflectors, without concave-up surfaces that indicate deep channels. Finally, we propose a conceptual model that relates the principal radar packages with the temporal evolution of the fluvial-aeolian interaction field of Guandacol Valley.Fil: Zabala Medina, Peter. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Limarino, Carlos Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Bonomo, Nestor Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Salvó Bernárdez, Salomé Candela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Osella, Ana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

Neural Mechanisms Underlying the Perception of Three-Dimensional Shape from Texture: Adaptation and Aftereffects

Input into the visual system is two-dimensional (2D) and yet we effortlessly perceive the world around us as three-dimensional (3D). How we are able to accurately extract 3D shape information from the 2D representations that fall on the retina remains largely unknown. Although much research has been conducted that investigates higher levels of form processing (i.e. face recognition), less is known about the mechanisms that underlie the perception of simple 3D shape. Previous studies in our lab have shown that our ability to perceive 3D shape from texture cues relies on the visibility of orientation flows -- patterns that run parallel to the surface curvature of a 3D shape. Using the psychophysical technique of selective adaptation, we have further characterized the neural mechanisms that underlie the accurate perception of 3D shape. In Experiment One, we examined whether orientation flows that are defined by second order contours convey 3D shape, whether they induce 3D shape aftereffects, and whether these aftereffects are invariant to the patterns that define the orientation flows. Aftereffects were obtained and 3D shape was conveyed using stimuli in which orientation flows were defined by two classes of second order contours, and adapting to second order stimuli caused 3D shape aftereffects in first order stimuli. These results can be explained by the adaptation of 3D shape-selective neurons in extrastriate regions that invariantly extract first- and second order orientation flows from striate and extrastriate signals. In Experiment Two, we were interested in determining to what extent these neural mechanisms are invariant to differences in spatial frequency. We chose adapting/test stimuli that differed in spatial frequency by a factor of three, consistent with documented frequency bandwidths of V1 and V2 neurons. Shape aftereffects were obtained, indicating that these neural mechanisms are invariant to differences in spatial frequency by a factor of 3. Furthermore, these neural mechanisms are invariant to the patterns in which spatial frequency was varied (i.e., stimuli in which the orientation flows were created by first- or second order properties). Both of these properties are indicative of neurons that are located in extrastriate cortex. In Experiment Three, we were interested in testing to what extent these neural mechanisms were selective for retinal position by misaligning adapting and test stimuli by 2°, which corresponded to a single convexity or concavity in our corrugated surfaces. Our results suggest that 3D shape-selective mechanisms that respond to luminance modulated orientation flows appear to be sensitive to shifts in position of 2°. Overall, our results indicate that there are 3D shape mechanisms that are pattern invariant, invariant to differences in spatial frequencies by a factor of 3, and that exhibit position selectivity to shifts in retinal position of 2°. Taken together, these results implicate 3D shape mechanisms that are located in extrastriate cortex

Traffic flow modelling

Tato diplomová práce prezentuje problematiku dopravního toku a jeho modelování. Zabývá se především několika LWR modely, které následně rozebírá a hledá řešení pro počáteční úlohy. Ukazuje se, že ne pro všechny počáteční úlohy lze řešení definovat na celém prostoru, ale jen v určitém okolí počáteční křivky. Proto je dále odvozena metoda výpočtu velikosti tohoto okolí a to nejen zcela obecně, ale i pro dané modely. Teoretický rozbor LWR modelů a řešení počátečních úloh jsou demonstrovány několika příklady, které zřetelně ukazují, jak se dopravní tok simulovaný danými modely chová.This thesis presents an issue of the traffic flow and its modelling. It speaks especially about a couple of LWR models which are analysed and for which the solution is searched. It is known in general that solutions are not defined everywhere for all the initial problems, but it is defined only for some neighbourhood of the initial curve. Therefore the general method for finding the extent of the neighbourhood is derived and extended on particular models. The theoretical analysis of the LWR models and the solution to the initial problems are demonstrated on some examples with illustrating models' behaviour.