One of the challenging problems in the aerospace industry is to design an automated 3D vision system that can sense the installation components in an assembly environment and check certain safety constraints are duly respected. This thesis describes a concept application to aid a safety engineer to perform an audit of a production aircraft against safety driven installation requirements such as segregation, proximity, orientation and trajectory. The capability is achieved using the following steps. The initial step is to perform image capture of a product and measurement of distance between datum points within the product with/without reference to a planar surface. This provides the safety engineer a means to perform measurements on a set of captured images of the equipment they are interested in. The next step is to reconstruct the digital model of fabricated product by using multiple captured images to reposition parts according to the actual model. Then, the projection onto the 3D digital reconstruction of the safety related installation constraints, respecting the original intent of the constraints that are defined in the digital mock up is done. The differences between the 3D reconstruction of the actual product and the design time digital mockup of the product are identified. Finally, the differences/non conformances that have a relevance to safety driven installation requirements with reference to the original safety requirement intent are identified. The above steps together give the safety engineer the ability to overlay a digital reconstruction that should be as true to the fabricated product as possible so that they can see how the product conforms or doesn't conform to the safety driven installation requirements. The work has produced a concept demonstrator that will be further developed in future work to address accuracy, work flow and process efficiency. A new depth based segmentation technique GrabcutD which is an improvement to existing Grabcut, a graph cut based segmentation method is proposed. Conventional Grabcut relies only on color information to achieve segmentation. However, in stereo or multiview analysis, there is additional information that could be also used to improve segmentation. Clearly, depth based approaches bear the potential discriminative power of ascertaining whether the object is nearer of farer. We show the usefulness of the approach when stereo information is available and evaluate it using standard datasets against state of the art result.

Three small-scale seismic experiments were conducted with the objective of identifying shallow scatterers that are principally fractures. The experiment targeted the upper 100m of the Earths subsurface. The analysis consisted of three steps. In the first step, we acquired data from three seismic arrays, at two different field sites. In the second step, the seismic records were processed using semblance analysis. The semblance coefficient for scattered waves was calculated as a function of their arrival time, apparent velocity, and azimuth. This information was the input for the third step the 3-D imaging algorithm. Scatterers in a homogeneous media were imaged along ellipses with dimensions defined by the true velocity of propagation and the time of arrival. The depth was defined from the ratio of true to apparent velocity. The three-dimensional images from an outcrop field site outline a zone of contact between granite and amphibolite-biotite gneiss. This contact zone is most likely controlled by a combination of fractures, joints and differential weathering. The semblance imaging technique failed to locate a subhorizontal fracture within the bedrock of a site with a soil cover. These results suggest that the technique can be successfully applied to a medium that can be approximated with homogeneous velocity structure. For more complex environments, the algorithm must be modified. First, ray tracing must be incorporated in the algorithm to find the exact locations of the near-surface heterogeneities. Second, geophones and source should be applied to the bedrock to avoid the attenuation from the soil overburden.

The prevalence of obesity has necessitated developing safe and convenient tools for timely assessing and monitoring this condition for a broad range of population. Three-dimensional (3D) body imaging has become a new mean for obesity assessment. Moreover, it generates body shape information that is meaningful for fitness, ergonomics, and personalized clothing. In the previous work of our lab, we developed a prototype active stereo vision system that demonstrated a potential to fulfill this goal. But the prototype required four computer projectors to cast artificial textures on the body which facilitate the stereo-matching on texture-deficient images (e.g., skin). This decreases the mobility of the system when used to collect a large population data. In addition, the resolution of the generated 3D~images is limited by both cameras and projectors available during the project. The study reported in this dissertation highlights our continued effort in improving the capability of 3Dbody imaging through simplified hardware for passive stereo and advanced computation techniques. The system utilizes high-resolution single-lens reflex (SLR) cameras, which became widely available lately, and is configured in a two-stance design to image the front and back surfaces of a person. A total of eight cameras are used to form four pairs of stereo units. Each unit covers a quarter of the body surface. The stereo units are individually calibrated with a specific pattern to determine cameras' intrinsic and extrinsic parameters for stereo matching. The global orientation and position of each stereo unit within a common world coordinate system is calculated through a 3Dregistration step. The stereo calibration and 3Dregistration procedures do not need to be repeated for a deployed system if the cameras' relative positions have not changed. This property contributes to the portability of the system, and tremendously alleviates the maintenance task. The image acquisition time is around two seconds for a whole-body capture. The system works in an indoor environment with a moderate ambient light. Advanced stereo computation algorithms are developed by taking advantage of high-resolution images and by tackling the ambiguity problem in stereo matching. A multi-scale, coarse-to-fine matching framework is proposed to match large-scale textures at a low resolution and refine the matched results over higher resolutions. This matching strategy reduces the complexity of the computation and avoids ambiguous matching at the native resolution. The pixel-to-pixel stereo matching algorithm follows a classic, four-step strategy which consists of matching cost computation, cost aggregation, disparity computation and disparity refinement. The system performance has been evaluated on mannequins and human subjects in comparison with other measurement methods. It was found that the geometrical measurements from reconstructed 3Dbody models, including body circumferences and whole volume, are highly repeatable and consistent with manual and other instrumental measurements (CV < 0.1$%, R2>0.99). The agreement of percent body fat (%BF) estimation on human subjects between stereo and dual-energy X-ray absorptiometry (DEXA) was found to be improved over the previous active stereo system, and the limits of agreement with 95% confidence were reduced by half. Our achieved %BF estimation agreement is among the lowest ones of other comparative studies with commercialized air displacement plethysmography (ADP) and DEXA. In practice, %BF estimation through a two-component model is sensitive to body volume measurement, and the estimation of lung volume could be a source of variation. Protocols for this type of measurement should still be created with an awareness of this factor. / text

Synthetic Aperture Focusing Technique (SAFT) is a powerful method to createfocused images of the inside of opaque samples by using delay-and-sum of acquireddata. It gives a high resolution and when using a generation laser and a detectionlaser it is also non-contact. This thesis was made at Swerim, and the aim wasto create an 3D-SAFT algorithm and to visualise the reconstructed image. Twosamples were used, both were 3D-printed with known defects that varied in sizefrom 0.05 mm to 1 mm. The defects were lined up in rows, with 10 in each row.After the algorithm was used on the acquired data from the two samples, six toeight defects were found in each row. Both samples had three rows of defects. Themeasured sizes of the defects were not exactly as the actual size but ranged a fewmillimetre too small or big compared to the real size. Overall the algorithm workswell. The resolution of the 3D images are the same as for the 2D-SAFT algorithmalready made by Swerim. As of now the 3D images may not be worth the time ittakes to process, but if a better way to visualise the data is made in the future, itwill be good to be able to see the defects in 3D.

Yes / In this study a method for 3D representation of active regions and sunspots that are detected from Solar and Heliospheric Observatory/Michelson Doppler Imager magnetogram and continuum images is provided. This is our first attempt to create a visual solar catalogue. Because of the difficulty of providing a full description of data in text based catalogues, it can be more accurate and effective for scientist to search 3D solar feature models and descriptions at the same time in such a visual solar catalogue. This catalogue would improve interpretation of solar images, since it would allow us to extract data embedded in various solar images and visualize it at the same time. In this work, active regions that are detected from magnetogram images and sunspots that are detected from continuum images are represented in 3D coordinates. Also their properties extracted from text based catalogues are represented at the same time in 3D environment. This is the first step for creating a 3D solar feature catalogue where automatically detected solar features will be presented visually together with their properties.

This dissertation merges the optical design and computational aspects of imaging systems to create novel devices that solve engineering problems in optical science and attempts to expand the solution space available to the optical designer. This dissertation is divided into two parts: the first discusses a new active illumination depth sensing modality, while the second part discusses a passive illumination system called plenoptic, or lightfield, imaging. The new depth sensing modality introduced in part one is called depth through controlled aberration. This technique illuminates a target with a known, aberrated projected pattern and takes an image using a traditional, unmodified imaging system. Knowing how the added aberration in the projected pattern changes as a function of depth, we are able to quantitatively determine depth of a series of points from the camera. A major advantage this method permits is the ability for illumination and imaging axes to be coincident. Plenoptic cameras capture both spatial and angular data simultaneously. This dissertation present a new set of parameters that permit the design and comparison of plenoptic devices outside the traditionally published plenoptic 1.0 and plenoptic 2.0 configurations. Additionally, a series of engineering advancements are presented, including full system ray traces of raw plenoptic images, Zernike compression techniques of raw image files, and non-uniform lenslet arrays to compensate for plenoptic system aberrations. Finally, a new snapshot imaging spectrometer is proposed based off the plenoptic configuration.

Image classification is an active topic of computer vision research. This topic deals with the learning of patterns in order to allow efficient classification of visual information. However, most research efforts have focused on 2D image classification. In recent years, advances of 3D imaging enabled the development of applications and provided new research directions. In this thesis, we present methodologies and techniques for image classification using 3D image data. We conducted our research focusing on the attributes and limitations of depth information regarding possible uses. This research led us to the development of depth feature extraction methodologies that contribute to the representation of images thus enhancing the recognition efficiency. We proposed a new classification algorithm that adapts to the need of image representations by implementing a scale-based decision that exploits discriminant parts of representations. Learning from the design of image representation methods, we introduced our own which describes each image by its depicting content providing more discriminative image representation. We also propose a dictionary learning method that exploits the relation of training features by assessing the similarity of features originating from similar context regions. Finally, we present our research on deep learning algorithms combined with data and techniques used in 3D imaging. Our novel methods provide state-of-the-art results, thus contributing to the research of 3D image classification.

Three-Dimensional (3D) optical range-imaging is a field experiencing rapid growth, expanding into a wide variety of machine vision applications, most recently including consumer gaming. Time of Flight (ToF) cameras, akin to RADAR with light, sense distance by measuring the round trip time of modulated Infra-Red (IR) illumination light projected into the scene and reflected back to the camera. Such systems generate 'depth maps' without requiring the complex processing utilised by other 3D imaging techniques such as stereo vision and structured light. Existing range-imaging solutions within the ToF category either perform demodulation in the analogue domain, and are therefore susceptible to noise and non-uniformities, or by digitally detecting individual photons using a Single Photon Avalanche Diode (SPAD), generating large volumes of raw data. In both cases, external processing is required in order to calculate a distance estimate from this raw information. To address these limitations, this thesis explores alternative system architectures for ToF range imaging. Specifically, a new pixel concept is presented, coupling a SPAD for accurate detection of the arrival time of photons to an all-digital Phase- Domain Delta-Sigma (PDΔΣ) loop for the first time. This processes the SPAD pulses locally, converging to estimate the mean phase of the incoming photons with respect to the outgoing illumination light. A 128×96 pixel sensor was created to demonstrate this principle. By incorporating all of the steps in the range-imaging process – from time resolved photon detection with SPADs, through phase extraction with the in-pixel phase-domain ΔΣ loop, to depth map creation with on-chip decimation filters – this sensor is the first fully integrated 3D camera-on-achip to be published. It is implemented in a 130nm CMOS imaging process, the most modern technology used in 3D imaging work presented to date, enabled by the recent availability of a very low noise SPAD structure in this process. Excellent linearity of ±5mm is obtained, although the 1σ repeatability error was limited to 160mm by a number of factors. While the dimensions of the current pixel prevent the implementation of very high resolution arrays, the all-digital nature of this technique will scale well if manufactured in a more advanced CMOS imaging process such as the 90nm or 65nm nodes. Repartitioning of the logic could enhance fill factor further. The presented characterisation results nevertheless serve as first validation of a new concept in 3D range-imaging, while proposals for its future refinement are presented.

Chez les mammifères, les dents sont un outil essentiel à la fragmentation et à la fracture des aliments. En retour, les propriétés mécaniques des aliments exercent une pression de sélection sur la morphologie dentaire. L'objectif de ce mémoire est de déterminer si ce signal adaptatif peut être détecté à partir de la morphologie des molaires chez les primates actuels et fossiles. Dans ce cadre, la topographie dentaire 3D de 31 espèces de primates actuels est étudiée à l'aide d'une combinaison de variables classiques et originales. Un colobe fossile, Mesopithecus pentelicus du Miocène terminal de Pikermi (Grèce), est également analysé. Les résultats sont interprétés à partir du régime alimentaire mais aussi d'un système de catégorisation inédit évaluant le champ mécanique d'aptitude à cisailler, écraser et « craquer » les aliments.Les variables choisies permettent de distinguer de façon significative les catégories alimentaires et les champs d'aptitude au sein de l'échantillon actuel. Elles prédisent également que M. pentelicus avait une morphologie dentaire intermédiaire, et qu'il était apte à « craquer » des aliments durs tout en maintenant une adaptation à cisailler des aliments coriaces. En outre, les nouvelles variables permettent de montrer que les crêtes d'émail sont significativement plus tranchantes chez les espèces consommant des aliments coriaces, ce qui confirme leur utilisation en tant qu'outil de cisaillement. En revanche, la distribution de l'épaisseur de l'émail semble être plus homogène chez les espèces durophages, suggérant que la dent entière constitue l'outil élémentaire de « cracking » chez les primates. / Mammalian teeth are a major tool in food cominution and fracture. Conversely, food mechanical properties apply a strong selective pressure on dental morphology. The aim of this manuscript is to investigate whether this adaptive signal can be detected from dental shape in extant and extinct primates.In this context, the 3D dental topography of 31 species of extant primates is analyzed with a combination of traditional and original variables. Furthermore one extinct colobine monkey, Mesopithecus pentelicus from the Late Miocene of Pikermi (Greece) is investigated. The results are interpreted using dietary categories, but also a novel categorization system that evaluates the mechanical scope of food shearing, grinding and cracking.Selected variables show significant differences between dietary categories as well as mechanical scopes across the sample. Moreover, the dental morphology of M. pentelicus is predicted to be intermediate between hard food cracking and tough food shearing taxa.In addition, the new variables show that shearing crests are significantly sharper in tough food shearers, which confirms their suggested role as a dental shearing tool. In contrast, enamel thickness distribution seems more homogenous within durophageous species, which suggests that the whole tooth per se makes the basic cracking tool of primates.

The clinical utility of multi-dimensional MRI applications like multi-parameter mapping and 3D dynamic lung imaging is limited by long acquisition times. Quantification of multiple tissue MRI parameters has been shown to be useful for early detection and diagnosis of various neurological diseases and psychiatric disorders. They also provide useful information about disease progression and treatment efficacy. Dynamic lung imaging enables the diagnosis of abnormalities in respiratory mechanics in dyspnea and regional lung function in pulmonary diseases like chronic obstructive pulmonary disease (COPD), asthma etc. However, the need for acquisition of multiple contrast weighted images as in case of multi-parameter mapping or multiple time points as in case of pulmonary imaging, makes it less applicable in the clinical setting as it increases the scan time considerably. In order to achieve reasonable scan times, there is often tradeoffs between SNR and resolution. Since, most MRI images are sparse in known transform domain; they can be recovered from fewer samples. Several compressed sensing schemes have been proposed which exploit the sparsity of the signal in pre-determined transform domains (eg. Fourier transform) or exploit the low rank characteristic of the data. However, these methods perform sub-optimally in the presence of inter-frame motion since the pre-determined dictionary does not account for the motion and the rank of the data is considerably higher. These methods rely on two step approach where they first estimate the dictionary from the low resolution data and using these basis functions they estimate the coefficients by fitting the measured data to the signal model. The main focus of the thesis is accelerating the multi-parameter mapping and 3D dynamic lung imaging applications to achieve desired volume coverage and spatio-temporal resolution. We propose a novel dictionary learning framework called the Blind compressed sensing (BCS) scheme to recover the underlying data from undersampled measurements, in which the underlying signal is represented as a sparse linear combination of basic functions from a learned dictionary. We also provide an efficient implementation using variable splitting technique to reduce the computational complexity by up to 15 fold. In both multi- parameter mapping and 3D dynamic lung imaging, the comparisons of BCS scheme with other schemes indicates superior performance as it provides a richer presentation of the data. The reconstructions from BCS scheme result in high accuracy parameter maps for parameter imaging and diagnostically relevant image series to characterize respiratory mechanics in pulmonary imaging.