Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Optical Imaging Technology In Colonoscopy - Is There A Role For Photometric Stereo

AbstractColonoscopy screening for the detection and removal of colonic adenomas is central to efforts to reduce the morbidity and mortality of colorectal cancer. However, up to a third of adenomas may be missed at colonoscopy, and the majority of post-colonoscopy colorectal cancers are thought to arise from these. Adenomas have three-dimensional surface topographic features that differentiate them from adjacent normal mucosa. However, these topographic features are not enhanced by white light colonoscopy, and the endoscopist must infer these from two-dimensional cues. This may contribute to the number of missed lesions. A variety of optical imaging technologies have been developed commercially to enhance surface topography. However, existing techniques enhance surface topography indirectly, and in two dimensions, and the evidence does not wholly support their use in routine clinical practice. In this narrative review, co-authored by gastroenterologists and engineers, we summarise the evidence for the impact of established optical imaging technologies on adenoma detection rate, and review the development of photometric stereo (PS) for colonoscopy. PS is a machine vision technique able to capture a dense array of surface normals to render three-dimensional reconstructions of surface topography. This imaging technique has several potential clinical applications in colonoscopy, including adenoma detection, polyp classification, and facilitating polypectomy, an inherently three-dimensional task. However, the development of PS for colonoscopy is at an early stage. We consider the progress that has been made with PS to date and identify the obstacles that need to be overcome prior to clinical application

{3D} Morphable Face Models -- Past, Present and Future

In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications

Cable-driven parallel robot for transoral laser phonosurgery

Transoral laser phonosurgery (TLP) is a common surgical procedure in otolaryngology. Currently, two techniques are commonly used: free beam and fibre delivery. For free beam delivery, in combination with laser scanning techniques, accurate laser pattern scanning can be achieved. However, a line-of-sight to the target is required. A suspension laryngoscope is adopted to create a straight working channel for the scanning laser beam, which could introduce lesions to the patient, and the manipulability and ergonomics are poor. For the fibre delivery approach, a flexible fibre is used to transmit the laser beam, and the distal tip of the laser fibre can be manipulated by a flexible robotic tool. The issues related to the limitation of the line-of-sight can be avoided. However, the laser scanning function is currently lost in this approach, and the performance is inferior to that of the laser scanning technique in the free beam approach. A novel cable-driven parallel robot (CDPR), LaryngoTORS, has been developed for TLP. By using a curved laryngeal blade, a straight suspension laryngoscope will not be necessary to use, which is expected to be less traumatic to the patient. Semi-autonomous free path scanning can be executed, and high precision and high repeatability of the free path can be achieved. The performance has been verified in various bench and ex vivo tests. The technical feasibility of the LaryngoTORS robot for TLP was considered and evaluated in this thesis. The LaryngoTORS robot has demonstrated the potential to offer an acceptable and feasible solution to be used in real-world clinical applications of TLP. Furthermore, the LaryngoTORS robot can combine with fibre-based optical biopsy techniques. Experiments of probe-based confocal laser endomicroscopy (pCLE) and hyperspectral fibre-optic sensing were performed. The LaryngoTORS robot demonstrates the potential to be utilised to apply the fibre-based optical biopsy of the larynx.Open Acces

Three-dimensional modeling of the human jaw/teeth using optics and statistics.

Object modeling is a fundamental problem in engineering, involving talents from computer-aided design, computational geometry, computer vision and advanced manufacturing. The process of object modeling takes three stages: sensing, representation, and analysis. Various sensors may be used to capture information about objects; optical cameras and laser scanners are common with rigid objects, while X-ray, CT and MRI are common with biological organs. These sensors may provide a direct or an indirect inference about the object, requiring a geometric representation in the computer that is suitable for subsequent usage. Geometric representations that are compact, i.e., capture the main features of the objects with a minimal number of data points or vertices, fall into the domain of computational geometry. Once a compact object representation is in the computer, various analysis steps can be conducted, including recognition, coding, transmission, etc. The subject matter of this dissertation is object reconstruction from a sequence of optical images using shape from shading (SFS) and SFS with shape priors. The application domain is dentistry. Most of the SFS approaches focus on the computational part of the SFS problem, i.e. the numerical solution. As a result, the imaging model in most conventional SFS algorithms has been simplified under three simple, but restrictive assumptions: (1) the camera performs an orthographic projection of the scene, (2) the surface has a Lambertian reflectance and (3) the light source is a single point source at infinity. Unfortunately, such assumptions are no longer held in the case of reconstruction of real objects as intra-oral imaging environment for human teeth. In this work, we introduce a more realistic formulation of the SFS problem by considering the image formation components: the camera, the light source, and the surface reflectance. This dissertation proposes a non-Lambertian SFS algorithm under perspective projection which benefits from camera calibration parameters. The attenuation of illumination is taken account due to near-field imaging. The surface reflectance is modeled using the Oren-Nayar-Wolff model which accounts for the retro-reflection case. In this context, a new variational formulation is proposed that relates an evolving surface model with image information, taking into consideration that the image is taken by a perspective camera with known parameters. A new energy functional is formulated to incorporate brightness, smoothness and integrability constraints. In addition, to further improve the accuracy and practicality of the results, 3D shape priors are incorporated in the proposed SFS formulation. This strategy is motivated by the fact that humans rely on strong prior information about the 3D world around us in order to perceive 3D shape information. Such information is statistically extracted from training 3D models of the human teeth. The proposed SFS algorithms have been used in two different frameworks in this dissertation: a) holistic, which stitches a sequence of images in order to cover the entire jaw, and then apply the SFS, and b) piece-wise, which focuses on a specific tooth or a segment of the human jaw, and applies SFS using physical teeth illumination characteristics. To augment the visible portion, and in order to have the entire jaw reconstructed without the use of CT or MRI or even X-rays, prior information were added which gathered from a database of human jaws. This database has been constructed from an adult population with variations in teeth size, degradation and alignments. The database contains both shape and albedo information for the population. Using this database, a novel statistical shape from shading (SSFS) approach has been created. Extending the work on human teeth analysis, Finite Element Analysis (FEA) is adapted for analyzing and calculating stresses and strains of dental structures. Previous Finite Element (FE) studies used approximate 2D models. In this dissertation, an accurate three-dimensional CAD model is proposed. 3D stress and displacements of different teeth type are successfully carried out. A newly developed open-source finite element solver, Finite Elements for Biomechanics (FEBio), has been used. The limitations of the experimental and analytical approaches used for stress and displacement analysis are overcome by using FEA tool benefits such as dealing with complex geometry and complex loading conditions

High-quality face capture, animation and editing from monocular video

Digitization of virtual faces in movies requires complex capture setups and extensive manual work to produce superb animations and video-realistic editing. This thesis pushes the boundaries of the digitization pipeline by proposing automatic algorithms for high-quality 3D face capture and animation, as well as photo-realistic face editing. These algorithms reconstruct and modify faces in 2D videos recorded in uncontrolled scenarios and illumination. In particular, advances in three main areas offer solutions for the lack of depth and overall uncertainty in video recordings. First, contributions in capture include model-based reconstruction of detailed, dynamic 3D geometry that exploits optical and shading cues, multilayer parametric reconstruction of accurate 3D models in unconstrained setups based on inverse rendering, and regression-based 3D lip shape enhancement from high-quality data. Second, advances in animation are video-based face reenactment based on robust appearance metrics and temporal clustering, performance-driven retargeting of detailed facial models in sync with audio, and the automatic creation of personalized controllable 3D rigs. Finally, advances in plausible photo-realistic editing are dense face albedo capture and mouth interior synthesis using image warping and 3D teeth proxies. High-quality results attained on challenging application scenarios confirm the contributions and show great potential for the automatic creation of photo-realistic 3D faces.Die Digitalisierung von Gesichtern zum Einsatz in der Filmindustrie erfordert komplizierte Aufnahmevorrichtungen und die manuelle Nachbearbeitung von Rekonstruktionen, um perfekte Animationen und realistische Videobearbeitung zu erzielen. Diese Dissertation erweitert vorhandene Digitalisierungsverfahren durch die Erforschung von automatischen Verfahren zur qualitativ hochwertigen 3D Rekonstruktion, Animation und Modifikation von Gesichtern. Diese Algorithmen erlauben es, Gesichter in 2D Videos, die unter allgemeinen Bedingungen und unbekannten Beleuchtungsverhältnissen aufgenommen wurden, zu rekonstruieren und zu modifizieren. Vor allem Fortschritte in den folgenden drei Hauptbereichen tragen zur Kompensation von fehlender Tiefeninformation und der allgemeinen Mehrdeutigkeit von 2D Videoaufnahmen bei. Erstens, Beiträge zur modellbasierten Rekonstruktion von detaillierter und dynamischer 3D Geometrie durch optische Merkmale und die Shading-Eigenschaften des Gesichts, mehrschichtige parametrische Rekonstruktion von exakten 3D Modellen mittels inversen Renderings in allgemeinen Szenen und regressionsbasierter 3D Lippenformverfeinerung mittels qualitativ hochwertigen Daten. Zweitens, Fortschritte im Bereich der Computeranimation durch videobasierte Gesichtsausdrucksübertragung und temporaler Clusterbildung, Übertragung von detaillierten Gesichtsmodellen, deren Mundbewegung mit Ton synchronisiert ist, und die automatische Erstellung von personalisierten "3D Face Rigs". Schließlich werden Fortschritte im Bereich der realistischen Videobearbeitung vorgestellt, welche auf der dichten Rekonstruktion von Hautreflektionseigenschaften und der Mundinnenraumsynthese mittels bildbasierten und geometriebasierten Verfahren aufbauen. Qualitativ hochwertige Ergebnisse in anspruchsvollen Anwendungen untermauern die Wichtigkeit der geleisteten Beiträgen und zeigen das große Potential der automatischen Erstellung von realistischen digitalen 3D Gesichtern auf

Geometric modeling of non-rigid 3D shapes : theory and application to object recognition.

One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This is true, especially for non-rigid 3D shapes where a great variety of shapes are produced as a result of deformations of a non-rigid object. Modeling these non-rigid shapes is a very challenging problem. Being able to analyze the properties of such shapes and describe their behavior is the key issue in research. Also, considering photometric features can play an important role in many shape analysis applications, such as shape matching and correspondence because it contains rich information about the visual appearance of real objects. This new information (contained in photometric features) and its important applications add another, new dimension to the problem\u27s difficulty. Two main approaches have been adopted in the literature for shape modeling for the matching and retrieval problem, local and global approaches. Local matching is performed between sparse points or regions of the shape, while the global shape approaches similarity is measured among entire models. These methods have an underlying assumption that shapes are rigidly transformed. And Most descriptors proposed so far are confined to shape, that is, they analyze only geometric and/or topological properties of 3D models. A shape descriptor or model should be isometry invariant, scale invariant, be able to capture the fine details of the shape, computationally efficient, and have many other good properties. A shape descriptor or model is needed. This shape descriptor should be: able to deal with the non-rigid shape deformation, able to handle the scale variation problem with less sensitivity to noise, able to match shapes related to the same class even if these shapes have missing parts, and able to encode both the photometric, and geometric information in one descriptor. This dissertation will address the problem of 3D non-rigid shape representation and textured 3D non-rigid shapes based on local features. Two approaches will be proposed for non-rigid shape matching and retrieval based on Heat Kernel (HK), and Scale-Invariant Heat Kernel (SI-HK) and one approach for modeling textured 3D non-rigid shapes based on scale-invariant Weighted Heat Kernel Signature (WHKS). For the first approach, the Laplace-Beltrami eigenfunctions is used to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the Collaborative Representation-based Classification with a Regularized Least Square (CRC-RLS) algorithm. The experimental results have shown that the proposed descriptor can achieve state-of-the-art results on two benchmark data sets. For the second approach, an improved method to introduce scale-invariance has been also proposed to avoid noise-sensitive operations in the original transformation method. Then a new 3D shape descriptor is formed based on the histograms of the scale-invariant HK for a number of critical points on the shape at different time scales. A Collaborative Classification (CC) scheme is then employed for object classification. The experimental results have shown that the proposed descriptor can achieve high performance on the two benchmark data sets. An important observation from the experiments is that the proposed approach is more able to handle data under several distortion scenarios (noise, shot-noise, scale, and under missing parts) than the well-known approaches. For modeling textured 3D non-rigid shapes, this dissertation introduces, for the first time, a mathematical framework for the diffusion geometry on textured shapes. This dissertation presents an approach for shape matching and retrieval based on a weighted heat kernel signature. It shows how to include photometric information as a weight over the shape manifold, and it also propose a novel formulation for heat diffusion over weighted manifolds. Then this dissertation presents a new discretization method for the weighted heat kernel induced by the linear FEM weights. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. Finally, this dissertation proposes an approach for 3D face recognition based on the front contours of heat propagation over the face surface. The front contours are extracted automatically as heat is propagating starting from a detected set of landmarks. The propagation contours are used to successfully discriminate the various faces. The proposed approach is evaluated on the largest publicly available database of 3D facial images and successfully compared to the state-of-the-art approaches in the literature. This work can be extended to the problem of dense correspondence between non-rigid shapes. The proposed approaches with the properties of the Laplace-Beltrami eigenfunction can be utilized for 3D mesh segmentation. Another possible application of the proposed approach is the view point selection for 3D objects by selecting the most informative views that collectively provide the most descriptive presentation of the surface

3D freeform surface measurement on coordinate measuring machine using photometric stereo method

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSurface metrology has been widely used in manufacturing for many years. There has been a wide range of techniques applied for measuring surface topography. A photometric stereo technique is one of the best ways for the analysis of three-dimensional (3D) surface textural patterns. Many published works are concerned the developed approach for recovering the 3D profiles from surface normal. This research not only presents a methodology used to retrieve the profiles of surface roughness standards but also investigates the uncertainty estimation of textural measurement determined by the photometric stereo method. Various input quantities have been studied such as pixel error from recovered 3D surface textural patterns, the power of light source which involved with surface roughness average (Ra) value and the effect of room temperature. The surface roughness standards were utilized as the reference value. In term of increasing accuracy of the reference value, a contact method (stylus instrument) was used to calibrate them. Illumination angles of light source had some influence on the measurement results. A coordinate measuring machine (CMM) was used for holding the light source in order to study the effects of tilt and slant angles. The effect of tilt and slant angles were investigated. The results of these experiments successfully indicated that the angle used in photometric stereo method played an important role to the accuracy level of the roughness measurement results. The surface roughness specimen manufactured by a Computer Numerical Control (CNC) was applied to validate the capability of the photometric stereo system.The royal Thai government, ministry of sciences and technology and national institute of metrology Thailand (NIMT