Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

An in Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching.Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades.In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing.This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups

Locally Adaptive Frames in the Roto-Translation Group and their Applications in Medical Imaging

Locally adaptive differential frames (gauge frames) are a well-known effective tool in image analysis, used in differential invariants and PDE-flows. However, at complex structures such as crossings or junctions, these frames are not well-defined. Therefore, we generalize the notion of gauge frames on images to gauge frames on data representations $U:\mathbb{R}^{d} \rtimes S^{d-1} \to \mathbb{R}$ defined on the extended space of positions and orientations, which we relate to data on the roto-translation group $SE(d)$, $d=2,3$. This allows to define multiple frames per position, one per orientation. We compute these frames via exponential curve fits in the extended data representations in $SE(d)$. These curve fits minimize first or second order variational problems which are solved by spectral decomposition of, respectively, a structure tensor or Hessian of data on $SE(d)$. We include these gauge frames in differential invariants and crossing preserving PDE-flows acting on extended data representation $U$ and we show their advantage compared to the standard left-invariant frame on $SE(d)$. Applications include crossing-preserving filtering and improved segmentations of the vascular tree in retinal images, and new 3D extensions of coherence-enhancing diffusion via invertible orientation scores

Correction of Errors in Time of Flight Cameras

En esta tesis se aborda la corrección de errores en cámaras de profundidad basadas en tiempo de vuelo (Time of Flight - ToF). De entre las más recientes tecnologías, las cámaras ToF de modulación continua (Continuous Wave Modulation - CWM) son una alternativa prometedora para la creación de sensores compactos y rápidos. Sin embargo, existen gran variedad de errores que afectan notablemente la medida de profundidad, poniendo en compromiso posibles aplicaciones. La corrección de dichos errores propone un reto desafiante. Actualmente, se consideran dos fuentes principales de error: i) sistemático y ii) no sistemático. Mientras que el primero admite calibración, el último depende de la geometría y el movimiento relativo de la escena. Esta tesis propone métodos que abordan i) la distorsión sistemática de profundidad y dos de las fuentes de error no sistemático más relevantes: ii.a) la interferencia por multicamino (Multipath Interference - MpI) y ii.b) los artefactos de movimiento. La distorsión sistemática de profundidad en cámaras ToF surge principalmente debido al uso de señales sinusoidales no perfectas para modular. Como resultado, las medidas de profundidad aparecen distorsionadas, pudiendo ser reducidas con una etapa de calibración. Esta tesis propone un método de calibración basado en mostrar a la cámara un plano en diferentes posiciones y orientaciones. Este método no requiere de patrones de calibración y, por tanto, puede emplear los planos, que de manera natural, aparecen en la escena. El método propuesto encuentra una función que obtiene la corrección de profundidad correspondiente a cada píxel. Esta tesis mejora los métodos existentes en cuanto a precisión, eficiencia e idoneidad. La interferencia por multicamino surge debido a la superposición de la señal reflejada por diferentes caminos con la reflexión directa, produciendo distorsiones que se hacen más notables en superficies convexas. La MpI es la causa de importantes errores en la estimación de profundidad en cámaras CWM ToF. Esta tesis propone un método que elimina la MpI a partir de un solo mapa de profundidad. El enfoque propuesto no requiere más información acerca de la escena que las medidas ToF. El método se fundamenta en un modelo radio-métrico de las medidas que se emplea para estimar de manera muy precisa el mapa de profundidad sin distorsión. Una de las tecnologías líderes para la obtención de profundidad en imagen ToF está basada en Photonic Mixer Device (PMD), la cual obtiene la profundidad mediante el muestreado secuencial de la correlación entre la señal de modulación y la señal proveniente de la escena en diferentes desplazamientos de fase. Con movimiento, los píxeles PMD capturan profundidades diferentes en cada etapa de muestreo, produciendo artefactos de movimiento. El método propuesto en esta tesis para la corrección de dichos artefactos destaca por su velocidad y sencillez, pudiendo ser incluido fácilmente en el hardware de la cámara. La profundidad de cada píxel se recupera gracias a la consistencia entre las muestras de correlación en el píxel PMD y de la vecindad local. Este método obtiene correcciones precisas, reduciendo los artefactos de movimiento enormemente. Además, como resultado de este método, puede obtenerse el flujo óptico en los contornos en movimiento a partir de una sola captura. A pesar de ser una alternativa muy prometedora para la obtención de profundidad, las cámaras ToF todavía tienen que resolver problemas desafiantes en relación a la corrección de errores sistemáticos y no sistemáticos. Esta tesis propone métodos eficaces para enfrentarse con estos errores

Solving Correspondences for Non-Rigid Deformations

Projecte final de carrera realitzat en col.laboració amb l'IR

Out-of-plane fiber waviness in composite materials: origins, detection and mechanical evaluation

Out-of-plane fiber waviness, also referred to as wrinkling, is considered one of the most significant effects that occur in composite materials. It significantly affects mechanical properties, such as stiffness, strength and fatigue and, therefore, dramatically reduces the load carrying capacity of the material. Fiber waviness is inherent to various manufacturing processes of fiber-reinforced composite parts. They cannot be completely avoided and thus have to be tolerated and considered as an integral part of the structure. Because of this influenceable but in many cases unavoidable nature of fiber waviness, it might be more appropriate to consider fiber waviness as effects or features rather than defects. Hence, it is important to understand the impact of different process parameters on the formation of fiber waviness in order to reduce or, in the best case, completely avoid them as early as possible in the product and process development phases. Mostly depending on the chosen geometry of the part and the specific manufacturing process used, different types of fiber waviness result. Fiber-reinforced composite materials allow for a significant mass reduction due to the comparably low density (c.f. 4-5 times less than steel) and, in addition, fibers can be aligned in accordance with the load paths. This possibility of alignment allows the fibers to be placed at the exact position where they are needed to provide the component with the required stiffness and strength. However, this can lead to a load path-optimized composite structure, which is not necessarily easy to produce and free of defects. The placement of the fibers or semi-finished textile products is still often carried out by hand-lay-up, especially in the aviation industry. This allows a diverse draping of the unidirectional (UD) layers, woven textiles or non-crimped fabrics (NCF) onto the production tool. However, manufacturing effects such as fiber waviness, porosity, delamination and distortion cannot be completely avoided. The increased demand for composite components and their production process stability for the aviation and automotive industries requires a transition to at least partially automated manufacturing processes. Those systems come with a higher deposition rate and ensure reproducible quality, but also imply production effects, e.g. fiber waviness. This necessitates a sophisticated understanding of those implicit effects on the mechanical properties of the manufactured structure. The decision as to whether these unwanted irregularities are considered as manufacturing features (effects), or as defects, depends on the size, number and location in the component. Those allowance limits depend on the strength and stiffness reserve at the location of the feature, as well as on functional requirements, e.g. water tightness. The assessment of manufacturing effects further depends on the industry. In the aviation industry, the allowance limits for defects are very restricted, while in the automotive industry the need for short cycle times leads to a trade-off between robust processes and tolerated manufacturing imperfections. To this point, there is still no generally accepted approach to quantitatively support accept/reject/repair-decisions and make a consistent assessment of wavy layers in composites. If the effect is termed to be a defect, typically a deviation from design must be requested in the aviation industry and an individual decision must be made on "use as is", repair or reject entirely. In some cases, experiments on representative test samples are performed at the subcomponent-level on a statistical basis. However, this is both time consuming and cost intensive. It is necessary to strive for a fiber-oriented and in particular a manufacturing-oriented design and construction of composite components. Towards this goal, design and production engineers aim to expand the permissible margin of safety by assessing the effect on stiffness and strength of those production effects, i.e. fiber waviness, porosity, delamination etc. Additionally, they aim to reduce or, in the best case, avoid them on the process side, increasingly with the help of finite element based process simulations. In this thesis, numerous mechanisms of wrinkling were analyzed, leading to several recommendations to prevent wrinkle formation not only during composite processing, but also at an earlier design stage, where generally several influencing factors are defined. Based on that, an overview of typically occurring wave shapes is presented and a classification scheme based on ten characteristic features is suggested for categorization purposes. The assessment of out-of-plane fiber waviness in composite materials is strongly dependent on the accuracy of detection and quantification of the wave parameters such as amplitude, wavelength and position in the laminate. In the aviation industry, ultrasonic testing (UT) is the preferred method for the evaluation of composite materials. The evaluation of the ultrasound signal from different manufacturing effects is difficult and it often cannot be clearly determined whether there are actually wavy regions in the laminate or not. In this thesis, different non-destructive testing (NDT) methods, such as infrared thermography (IRT), digital shearography, eddy current testing (ET) and X-ray computed tomography (CT) have been used to assess their potential for the detection and characterization of embedded out-of-plane fiber waviness in composite materials. These methods were applied on test plates with artificially embedded waviness with varying amplitudes, wavelengths and positions in the laminate and evaluated with respect to their ability of detecting the wrinkle morphology. The experimental non-destructive procedures of infrared thermography and digital shearography were simulated using the Finite Element Method (FEM) to gain a deeper understanding on the influence of fiber waviness on the measured results. To understand the complex failure behaviour of composite materials containing out-of-plane fiber waviness under compressive and tensile loading, numerous experimental tests have been carried out. Digital image correlation (DIC), passive thermography (IRT) and acoustic emission (AE) test methods have been used to investigate damage initiation and propagation on specimen level. In addition to that, an extensive material characterization on planar specimens was also performed. Composite materials exposed to harsh environmental conditions, i.e. hot-wet, show considerably reduced mechanical properties, governed by a degrading matrix. To investigate the effect of fiber waviness on the mechanical properties at both room temperature and after 12 months hot-wet conditioning at 70°C and 85% relative humidity, mechanical tests (compressive and tensile loading) were conducted. The basic strategies for the assessment of fiber waviness are briefly described. In engineering practice several approaches are used, i.e. empirical, generic, and semi-empirical. These include experimentally obtained knockdown factors, simplified simulations or extensive testing on subcomponent level, both experimentally and numerically. A developed micromechanical model is implemented in a MATLAB GUI to determine the effective elastic properties as well as the resulting complex stress state of uniform and graded fiber waviness. The well-established Puck failure criterion was implemented and applied on the calculated stresses to predict local ply failure and determine the strength of wavy plies. The mechanical behavior of out-of-plane fiber waviness is investigated for both unidirectional and quasi-isotropic laminates by numerically simulating damage initiation and propagation. A nonlinear material model was implemented in ABAQUS/Explicit as a material user-subroutine, which is able to capture the material behavior including shear nonlinearities, failure initiation and propagation in unidirectional laminates reasonably accurate