Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.

Data-driven 3D reasoning for augmented reality

La réalité augmentée (RA) est un paradigme informatique non conventionnel dans lequel l'utilisateur interagit naturellement avec des ordinateurs en visualisant des informations en 3D et en interagissant physiquement avec du contenu virtuel. L'insertion de contenu 3D dans l'environnement nécessite que l'appareil informatique mesure le monde qui nous entoure. Les capteurs sont cependant physiquement limités et renvoient des informations brutes incomplètes ou complexes. Distiller ces données en concepts plus abstraits est donc nécessaire pour permettre de raisonner sur des concepts tels que la géométrie ou l'interaction de la lumière avec la scène. Dans cette thèse, nous explorons une question critique dans le contexte de la RA : comment les caméras de qualité grand public et les approches basées sur les données peuvent-elles être combinées pour parvenir à un raisonnement 3D du monde pour les problèmes fondamentaux de la RA ? Nous répondons à cette affirmation en nous concentrant sur trois objectifs importants couramment rencontrés dans la plupart des applications de réalité augmentée. Tout d'abord, nous estimons une pose 3D robuste de diverses instances d'objets dans des séquences temporelles à l'aide d'une seule caméra RGB-D. Notre nouvelle méthode d'apprentissage par réseaux profond permet une estimation robuste et précise de la pose malgré la présence d'occlusion. De plus, nous améliorons la stratégie d'évaluation de suiveurs d'objets en six degrées de libertés avec une méthodologie méticuleuse et un nouvel ensemble de données. Nous démontrons que l'utilisation du système de coordonnées de l'objet estimé nous permet d'éffectuer le rendu de contenu virtuel sur des objets inanimés. Deuxièmement, nous détectons les articulations du haut du corps en 3D à l'aide d'un casque de réalité virtuelle muni de plusieurs caméras pour améliorer les interactions entre le contenu humain et virtuel. Notre méthode tire partie des multiples caméras à large champ de vision sur l'appareil pour estimer une position 3D précise des articulations du corps de l'utilisateur. L'architecture du réseau neuronal utilise explicitement la géométrie projective de chaque caméra pour estimer les caractéristiques 3D pouvant être utilisées lors de la régression des positions des différentes articulations ainsi que d'autres tâches telles que la segmentation du corps. Nos expériences démontrent que l'utilisation de sources de supervision faibles améliore la précision du suiveur tout en permettant de collecter des données qui ne contiennent pas de position d'articulation 3D en vérité terrain. Enfin, nous proposons une méthode pour raisonner sur des conditions de lumière variant dans l'espace à partir d'une seule image couleur. Estimer uniquement l'éclairage global n'est pas précis lorsque les sources lumineuses sont proches du sujet et lorsque les objets de la scène occultent les sources lumineuses, un scénario courant dans les scènes d'intérieur. Notre méthode prend une image couleur et une coordonnée d'image 2D comme entrée pour estimer une représentation harmonique sphérique de la lumière à ce point de la scène. Nous montrons que les prédictions sont cohérentes avec les sources de lumière 3D et l'occlusion. La méthode est également une solution en temps réel en utilisant une architecture légère et des harmoniques sphériques pour effectuer des rendus rapidement. Chacun de ces objectifs est soutenu par des expériences approfondies et des analyses de résultats et, espérons-le, aide à combler le fossé vers de meilleures expériences utilisateur en RA.Augmented Reality (AR) is an unconventional computing paradigm where the user interacts naturally with machines by visualizing information in 3D and physically interacting with virtual content. Inserting 3D content in the environment requires the computing device to measure the world surrounding us. Sensors are however physically limited and return incomplete or complex raw information. Distilling this data in more abstract concepts is thus mandatory to allow reasoning about numerous concepts such as geometry or light interaction with the scene. In this thesis, we explore a critical question in the context of AR: how consumer grade cameras and data-driven approaches can be combined to achieve 3D reasoning of the world for fundamental AR problems? We address this statement by focusing on three important objectives commonly encountered in most augmented reality applications. First, we estimate a robust 3D pose of various object instances in temporal sequences using a single RGB-D camera. Our novel deep learning framework allows robust and accurate pose estimation despite the presence of occlusion. We further improve the evaluation strategy of 6 DOF object trackers with a meticulous methodology and challenging new dataset. We demonstrate that using the estimated object reference allows us to render virtual content over inanimate objects. Second, we detect the upper body joints in 3D using an off-the-shelf head mounted display (HMD) to improve human and virtual content interactions. Our method takes advantage of the multiple wide field of view cameras on the HMD to estimate an accurate 3D position of the user body joints. The neural network architecture explicitly uses the projective geometry of each cameras to estimate 3D features that can be used to regress the joint position and other tasks such as body segmentation. Our experiments demonstrate that using weak sources of supervision enhance the accuracy of the tracker while allowing to gather data that does not contain ground truth 3D joint position. Finally, we propose a method to reason about spatially-varying light conditions from a single RGB image. Estimating only global lighting does not provide accurate illumination when light sources are near the subject and when objects in the scene occlude the light sources, a common scenario in indoor scenes. Our method takes an RGB image and 2D image coordinate as input to estimate a spherical harmonic representation of light at that point in the scene. We show that the predictions are consistent with 3D light sources and occlusion. The method is also a real-time solution for the full render pipeline by using a lightweight architecture and spherical harmonics. Each of these objectives is supported by extensive experiments and result analyzes and hopefully help closing the gap to better AR experiences

Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

The first step in monitoring an observer’s eye gaze is identifying and locating the image of their pupils in video recordings of their eyes. Current systems work under a range of conditions, but fail in bright sunlight and rapidly varying illumination. A computer vision system was developed to assist with the recognition of the pupil in every frame of a video, in spite of the presence of strong first-surface reflections off of the cornea. A modified Hough Circle detector was developed that incorporates knowledge that the pupil is darker than the surrounding iris of the eye, and is able to detect imperfect circles, partial circles, and ellipses. As part of processing the image is modified to compensate for the distortion of the pupil caused by the out-of-plane rotation of the eye. A sophisticated noise cleaning technique was developed to mitigate first surface reflections, enhance edge contrast, and reduce image flare. Semi-supervised human input and validation is used to train the algorithm. The final results are comparable to those achieved using a human analyst, but require only a tenth of the human interaction

Process Modeling Optimization in Additive Manufacturing Using Artificial Neural Networks

The need for production has roots in human life and its history. This date back to primitive days of human life, where he or she had to apply surrounding materials in order to manufacture the tools necessary for survival and durability against any insecurity. This was legitimizing the use of any means in order to obtain the tools and reach the goals at any cost. However, with human development primarily within the knowledge and understanding domain and also with the desire of humanity for best, expectations have risen. This was the time not only the cost mattered but also the simplicity of design, massive production, and diversity, less waste, autonomy, and implementation within a shorter time gained a higher momentum. On the other hand, the conventional manufacturing method was based on subtractive manufacturing with cutting and eliminating the unwanted sections or parts of an object. The disadvantage of such a method is that it requires a complicated production process design and is accompanied by waste. However, with the rise of additive manufacturing and three-dimensional printing equipment back in the 1980s, it became possible to build parts which could have almost any shape or geometry. Moreover, this also empowered the possibility of using digital and 3D models built by computer-aided design software. Simultaneously, on the other side, the foundation and application of artificial intelligence were maturing. This was due to the demand for machines to assist human beings in the domain of knowledge reasoning, learning, and planning. These were the pillars for making machines autonomous and to benefit from such features. Accordingly, this research work studies and overviews the applications and techniques of machine learning and artificial intelligence in the domain of additive manufacturing. It aims to determine the interaction of influential parameters on the process and to find the best solutions for improving the quality and mechanical features of manufactured parts. Moreover, this research tends to enable the experts to grasp a better understanding of AM process during manufacturing and additionally intends to infuse the experts' knowledge in additive manufacturing field utilizing the artificial neural network and finally generate a model with the ability of prediction and selection for promising results

Automated Assembly Using Feature Localization

Automated assembly of mechanical devices is studies by researching methods of operating assembly equipment in a variable manner; that is, systems which may be configured to perform many different assembly operations are studied. The general parts assembly operation involves the removal of alignment errors within some tolerance and without damaging the parts. Two methods for eliminating alignment errors are discussed: a priori suppression and measurement and removal. Both methods are studied with the more novel measurement and removal technique being studied in greater detail. During the study of this technique, a fast and accurate six degree-of-freedom position sensor based on a light-stripe vision technique was developed. Specifications for the sensor were derived from an assembly-system error analysis. Studies on extracting accurate information from the sensor by optimally reducing redundant information, filtering quantization noise, and careful calibration procedures were performed. Prototype assembly systems for both error elimination techniques were implemented and used to assemble several products. The assembly system based on the a priori suppression technique uses a number of mechanical assembly tools and software systems which extend the capabilities of industrial robots. The need for the tools was determined through an assembly task analysis of several consumer and automotive products. The assembly system based on the measurement and removal technique used the six degree-of-freedom position sensor to measure part misalignments. Robot commands for aligning the parts were automatically calculated based on the sensor data and executed

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

MUSME 2011 4 th International Symposium on Multibody Systems and Mechatronics

El libro de actas recoge las aportaciones de los autores a través de los correspondientes artículos a la Dinámica de Sistemas Multicuerpo y la Mecatrónica (Musme). Estas disciplinas se han convertido en una importante herramienta para diseñar máquinas, analizar prototipos virtuales y realizar análisis CAD sobre complejos sistemas mecánicos articulados multicuerpo. La dinámica de sistemas multicuerpo comprende un gran número de aspectos que incluyen la mecánica, dinámica estructural, matemáticas aplicadas, métodos de control, ciencia de los ordenadores y mecatrónica. Los artículos recogidos en el libro de actas están relacionados con alguno de los siguientes tópicos del congreso: Análisis y síntesis de mecanismos ; Diseño de algoritmos para sistemas mecatrónicos ; Procedimientos de simulación y resultados ; Prototipos y rendimiento ; Robots y micromáquinas ; Validaciones experimentales ; Teoría de simulación mecatrónica ; Sistemas mecatrónicos ; Control de sistemas mecatrónicosUniversitat Politècnica de València (2011). MUSME 2011 4 th International Symposium on Multibody Systems and Mechatronics. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/13224Archivo delegad

The benefits of an additional practice in descriptive geomerty course: non obligatory workshop at the Faculty of Civil Engineering in Belgrade

At the Faculty of Civil Engineering in Belgrade, in the Descriptive geometry (DG) course, non-obligatory workshops named “facultative task” are held for the three generations of freshman students with the aim to give students the opportunity to get higher final grade on the exam. The content of this workshop was a creative task, performed by a group of three students, offering free choice of a topic, i.e. the geometric structure associated with some real or imagery architectural/art-work object. After the workshops a questionnaire (composed by the professors at the course) is given to the students, in order to get their response on teaching/learning materials for the DG course and the workshop. During the workshop students performed one of the common tests for testing spatial abilities, named “paper folding". Based on the results of the questionnairethe investigation of the linkages between:students’ final achievements and spatial abilities, as well as students’ expectations of their performance on the exam, and how the students’ capacity to correctly estimate their grades were associated with expected and final grades, is provided. The goal was to give an evidence that a creative work, performed by a small group of students and self-assessment of their performances are a good way of helping students to maintain motivation and to accomplish their achievement. The final conclusion is addressed to the benefits of additional workshops employment in the course, which confirmhigherfinal scores-grades, achievement of creative results (facultative tasks) and confirmation of DG knowledge adaption

The contemporary visualization and modelling technologies and the techniques for the design of the green roofs

The contemporary design solutions are merging the boundaries between real and virtual world. The Landscape architecture like the other interdisciplinary field stepped in a contemporary technologies area focused on that, beside the good execution of works, designer solutions has to be more realistic and “touchable”. The opportunities provided by Virtual Reality are certainly not negligible, it is common knowledge that the designs in the world are already presented in this way so the Virtual Reality increasingly used. Following the example of the application of virtual reality in landscape architecture, this paper deals with proposals for the use of virtual reality in landscape architecture so that designers, clients and users would have a virtual sense of scope e.g. rooftop garden, urban areas, parks, roads, etc. It is a programming language that creates a series of images creating a whole, so certain parts can be controlled or even modified in VR. Virtual reality today requires a specific gadget, such as Occulus, HTC Vive, Samsung Gear VR and similar. The aim of this paper is to acquire new theoretical and practical knowledge in the interdisciplinary field of virtual reality, the ability to display using virtual reality methods, and to present through a brief overview the plant species used in the design and construction of an intensive roof garden in a Mediterranean climate, the basic characteristics of roofing gardens as well as the benefits they carry. Virtual and augmented reality as technology is a very powerful tool for landscape architects, when modeling roof gardens, parks, and urban areas. One of the most popular technologies used by landscape architects is Google Tilt Brush, which enables fast modeling. The Google Tilt Brush VR app allows modeling in three-dimensional virtual space using a palette to work with the use of a three dimensional brush. The terms of two "programmed" realities - virtual reality and augmented reality - are often confused. One thing they have in common, though, is VRML - Virtual Reality Modeling Language. In this paper are shown the ways on which this issue can be solved and by the way, get closer the term of Virtual Reality (VR), also all the opportunities which the Virtual reality offered us. As well, in this paper are shown the conditions of Mediterranean climate, the conceptual solution and the plant species which will be used by execution of intensive green roof on the motel “Marković”