Realtà Aumentata applicata alla manutenzione tecnica di macchinari industriali

La tesi, svolta per il completamento della Laurea Magistrale in Ingegneria Informatica, tratta la realizzazione di un progetto prototipo di Computer Vision (CV) e Realtà Aumentata (RA) per la manutenzione tecnica di macchinari industriali attraverso l'utilizzo di dispositivi mobili See-Through. Lo scopo è stato, oltre lo studio dello stato dell'arte in materia, provare con mano e rendere maggiormente visibili al pubblico questi nuovi rami dell'informatica. Il prototipo creato è stato inserito in un contesto aziendale, con misurazioni e prove sul campo. Partendo da una breve introduzione sulla realtà aumentata, nel primo capitolo viene descritto il progetto sviluppato, diviso in due sottoprogetti. Il primo, svolto solamente in una fase iniziale e presentato nel secondo capitolo, espone la realizzazione di un'applicazione mobile per lo streaming video con l'aggiunta di contenuti grafici aumentati. Il secondo, progettato e sviluppato in totale autonomia, rappresenta un prototipo demo di utilizzo della RA. La realizzazione viene illustrata nei capitoli successivi. Nel terzo capitolo si introducono gli strumenti che sono stati utilizzati per lo sviluppo dell'applicazione, in particolare Unity (per il development multi-piattaforma), Vuforia (per gli algoritmi di CV) e Blender (per la realizzazione di procedure di manutenzione). Il quarto capitolo, la parte più rilevante della trattazione, descrive, passo dopo passo, la creazione dei vari componenti, riassumendo in modo conciso e attraverso l'uso di figure i punti cardine. Infine, il quinto capitolo conclude il percorso realizzato presentando i risultati raggiunti e lasciando spunto per possibili miglioramenti ed aggiunte.

Artists' advance: decreased upper alpha power while drawing in artists compared with non-artists

Brain mechanisms associated with artistic talents or skills are still not well understood. This exploratory study investigated differences in brain activity of artists and non-artists while drawing previously presented perspective line-drawings from memory and completing other drawing-related tasks. Electroencephalography (EEG) data were analyzed for power in the frequency domain by means of a Fast Fourier Transform (FFT). Low Resolution Brain Electromagnetic Tomography (LORETA) was applied to localize emerging significances. During drawing and related tasks, decreased power was seen in artists compared to non-artists mainly in upper alpha frequency ranges. Decreased alpha power is often associated with an increase in cognitive functioning and may reflect enhanced semantic memory performance and object recognition processes in artists. These assumptions are supported by the behavioral data assessed in this study and complement previous findings showing increased parietal activations in non-artists compared to artists while drawing. However, due to the exploratory nature of the analysis, additional confirmatory studies will be needed.

Automatic extraction of femur contours from calibrated fluoroscopic images

Automatic identification and extraction of bone contours from X-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated X-ray images. The automatic initialization is solved by an estimation of Bayesian network algorithm to fit a multiple component geometrical model to the X-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the X-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Preliminary experiments on clinical data sets verified its validity

Correspondence establishment in statistical modeling of shapes with arbitrary topology

Correspondence establishment is a key step in statistical shape model building. There are several automated methods for solving this problem in 3D, but they usually can only handle objects with simple topology, like that of a sphere or a disc. We propose an extension to correspondence establishment over a population based on the optimization of the minimal description length function, allowing considering objects with arbitrary topology. Instead of using a fixed structure of kernel placement on a sphere for the systematic manipulation of point landmark positions, we rely on an adaptive, hierarchical organization of surface patches. This hierarchy can be built on surfaces of arbitrary topology and the resulting patches are used as a basis for a consistent, multi-scale modification of the surfaces' parameterization, based on point distribution models. The feasibility of the approach is demonstrated on synthetic models with different topologies.

Nimodipine prior to alcohol withdrawal prevents memory deficits during the abstinence phase

Effects of the dihydropyridine, nimodipine, an antagonist at L-type calcium channels, on the memory loss in rats caused by long term alcohol consumption were examined. Either a single dose of nimodipine or 2 weeks of repeated administration was given prior to withdrawal from 8 months of alcohol consumption. Memory was measured by the object recognition test and the T maze. Both nimodipine treatments prevented the memory deficits when these were measured between 1 and 2 months after alcohol withdrawal. At the end of the memory testing, 2 months after cessation of chronic alcohol consumption, glucocorticoid concentrations were increased in specific regions of rat brain without changes in plasma concentrations. Both nimodipine treatment schedules substantially reduced these rises in brain glucocorticoid. The data indicate that blockade of L-type calcium channels prior to alcohol withdrawal protects against the memory deficits caused by prolonged alcohol intake. This shows that specific drug treatments, such as nimodipine, given over the acute withdrawal phase, can prevented the neuronal changes responsible for subsequent adverse effects of long term consumption of alcohol. The results also suggest the possibility that regional brain glucocorticoid increases may be involved in the adverse effects of long term alcohol intake on memory. Such local changes in brain glucocorticoid levels would have major effects on neuronal function. The studies indicate that L-type calcium channels and brain glucocorticoid levels could form new targets for the treatment of cognitive deficits in alcoholics.

Effects of the glucocorticoid antagonist, mifepristone, on the consequences of withdrawal from long term alcohol consumption

BACKGROUND: Studies were carried out to test the hypothesis that administration of a glucocorticoid Type II receptor antagonist, mifepristone (RU38486), just prior to withdrawal from chronic alcohol treatment, would prevent the consequences of the alcohol consumption and withdrawal in mice. MATERIALS AND METHODS: The effects of administration of a single intraperitoneal dose of mifepristone were examined on alcohol withdrawal hyperexcitability. Memory deficits during the abstinence phase were measured using repeat exposure to the elevated plus maze, the object recognition test, and the odor habituation/discrimination test. Neurotoxicity in the hippocampus and prefrontal cortex was examined using NeuN staining. RESULTS: Mifepristone reduced, though did not prevent, the behavioral hyperexcitability seen in TO strain mice during the acute phase of alcohol withdrawal (4 hours to 8 hours after cessation of alcohol consumption) following chronic alcohol treatment via liquid diet. There were no alterations in anxiety-related behavior in these mice at 1 week into withdrawal, as measured using the elevated plus maze. However, changes in behavior during a second exposure to the elevated plus maze 1 week later were significantly reduced by the administration of mifepristone prior to withdrawal, indicating a reduction in the memory deficits caused by the chronic alcohol treatment and withdrawal. The object recognition test and the odor habituation and discrimination test were then used to measure memory deficits in more detail, at between 1 and 2 weeks after alcohol withdrawal in C57/BL10 strain mice given alcohol chronically via the drinking fluid. A single dose of mifepristone given at the time of alcohol withdrawal significantly reduced the memory deficits in both tests. NeuN staining showed no evidence of neuronal loss in either prefrontal cortex or hippocampus after withdrawal from chronic alcohol treatment. CONCLUSIONS: The results suggest mifepristone may be of value in the treatment of alcoholics to reduce their cognitive deficits.

Exploring Urban Environments Using Virtual and Augmented Reality

In this paper, we propose the use of specific system architecture, based on mobile device, for navigation in urban environments. The aim of this work is to assess how virtual and augmented reality interface paradigms can provide enhanced location based services using real-time techniques in the context of these two different technologies. The virtual reality interface is based on faithful graphical representation of the localities of interest, coupled with sensory information on the location and orientation of the user, while the augmented reality interface uses computer vision techniques to capture patterns from the real environment and overlay additional way-finding information, aligned with real imagery, in real-time. The knowledge obtained from the evaluation of the virtual reality navigational experience has been used to inform the design of the augmented reality interface. Initial results of the user testing of the experimental augmented reality system for navigation are presented.

Real-time Human Motion Capture with Simple Marker Sets and Monocular Video

In this paper we present a hybrid method to track human motions in real-time. With simplified marker sets and monocular video input, the strength of both marker-based and marker-free motion capturing are utilized: A cumbersome marker calibration is avoided while the robustness of the marker-free tracking is enhanced by referencing the tracked marker positions. An improved inverse kinematics solver is employed for real-time pose estimation. A computer-visionbased approach is applied to refine the pose estimation and reduce the ambiguity of the inverse kinematics solutions. We use this hybrid method to capture typical table tennis upper body movements in a real-time virtual reality application.

Spatial modulation of primate inferotemporal responses by eye position.

BACKGROUND: A key aspect of representations for object recognition and scene analysis in the ventral visual stream is the spatial frame of reference, be it a viewer-centered, object-centered, or scene-based coordinate system. Coordinate transforms from retinocentric space to other reference frames involve combining neural visual responses with extraretinal postural information. METHODOLOGY/PRINCIPAL FINDINGS: We examined whether such spatial information is available to anterior inferotemporal (AIT) neurons in the macaque monkey by measuring the effect of eye position on responses to a set of simple 2D shapes. We report, for the first time, a significant eye position effect in over 40% of recorded neurons with small gaze angle shifts from central fixation. Although eye position modulates responses, it does not change shape selectivity. CONCLUSIONS/SIGNIFICANCE: These data demonstrate that spatial information is available in AIT for the representation of objects and scenes within a non-retinocentric frame of reference. More generally, the availability of spatial information in AIT calls into questions the classic dichotomy in visual processing that associates object shape processing with ventral structures such as AIT but places spatial processing in a separate anatomical stream projecting to dorsal structures.

Food Volume Computation for Self Dietary Assessment Applications

There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.

Twenty questions with noise: Bayes optimal policies for entropy loss

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We formulate this problem as a dynamic program and show that any policy optimizing the one-step expected reduction in entropy is also optimal over the full horizon. Two such Bayes optimal policies are presented: one generalizes the probabilistic bisection policy due to Horstein and the other asks a deterministic set of questions. We study the structural properties of the latter, and illustrate its use in a computer vision application.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

A Logarithmic Image Prior for Blind Deconvolution

Blind Deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Many successful image priors enforce the sparsity of the sharp image gradients. Ideally the L0 “norm” is the best choice for promoting sparsity, but because it is computationally intractable, some methods have used a logarithmic approximation. In this work we also study a logarithmic image prior. We show empirically how well the prior suits the blind deconvolution problem. Our analysis confirms experimentally the hypothesis that a prior should not necessarily model natural image statistics to correctly estimate the blur kernel. Furthermore, we show that a simple Maximum a Posteriori formulation is enough to achieve state of the art results. To minimize such formulation we devise two iterative minimization algorithms that cope with the non-convexity of the logarithmic prior: one obtained via the primal-dual approach and one via majorization-minimization.