This dissertation proposes a novel approach to achieve circumferential three-dimensional (3D) profiling for dark objects by investigating specular micro-texture photometry. A small patch of a target surface in micro-texture level yields different appearance under different illumination. This photometric property can be used to reconstruct the target surface with pixel-level resolution. However, due to the nature of some material, the surface of whom has stronger specular components than diffuse components, making the usage of general microtexture photometry more difficult. On the other hand, without using micro-texture photometry, the conventional circumferential 3D approaches only utilizes the geometric property of the target surface, compared to which, the proposed is able to reconstruct the target surface with finer detail. The original contributions of this dissertation are threefold. To begin with, the specular component in the micro-texture photometry is investigated to propose the pixel-level 3D profiling. The intensities of the same pixel from different images, which are taken under different lighting conditions are different. The specular components are used to recover the surface normal of the corresponding surface patch of the target surface. Consequently, the proposed specular-photometry-based technique produces pixel-wise measurement on surface normal. Furthermore, the conventional circumferential 3D profiling approach is extended with the proposed specular-photometry-based technique. The result of 3D profiling via the conventional approach is sparse due to its nature. On the other hand, the result of 3D profiling from the integration using the surface normal obtained from the proposed specular-photometry-based technique suffers from accumulative error. A new approach is then proposed to use the result from the conventional approach as global constraint, for the purpose of reducing the accumulative error. The proposed approach is able to achieve pixel-resolution globally bounded profiling because of the dense surface normal measurement from the proposed specular-photometry-based technique and the constraints from the conventional approach. Lastly, a system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach. The developed system is not only able to acquire data and but also to provide different lighting conditions for both the specular-photometry-based technique and conventional approach using a digital single-lens reflex camera and different lighting devices. With a step motor to rotate the object for three hundred and sixty degrees, the system is able to achieve circumferential scanning / Doctor of Philosophy / This dissertation explains a novel approach to fulfill circumferential 3D profiling with high resolution for dark objects. With the proposed approach, the resolution is able to achieve micro-texture level. The high resolution measurement is achieved by investigating specular micro-texture photometry. As for dark objects, the specular components dominate the reflection. The usage of photometry is explained as follow. A small patch of a target surface yields different appearance under different illumination. For the material of the surface of dark objects, the surface reflects stronger specular components than diffuse components. The proposed approach utilizes this photometric property to recover the small patch's surface normal using its specular components. The recovered surface normal is then used to calculate the surface profile through integration. The conventional circumferential 3D profiling approach, which can only produce low-resolution measurement, is also adopted in the proposed approach to enhance its performance, as the integration method suffers from accumulative error. The result from the conventional approach serves as a global constraint to bound the final profiling result. A system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach, which is equipped with a step motor to rotate the object for three hundred and sixty degrees for the purpose of circumferential scanning. A series of numerical and experimental studies were conducted to validate the performance of the proposed approach. As it is shown in the result, the proposed approach is able to measure the tire tread with 31µm resolution.

Les techniques de reconstruction 3d sont devenues incontournables pour des applicationstelles que la caractérisation et l'analyse de surfaces. Les travaux réalisés au coursde cette thèse ont pour objectif d'améliorer la qualité des reconstructions 3d par stéréophotométrie.Cette méthode repose sur deux principes, l'inversion d'un modèle d'interactionlumière/matière (BRDF) et la configuration d'un système d'éclairage et de prises de vues.Pour des surfaces diffuses, la stéréo-photométrie est réalisée à partir d'un minimum detrois images acquises d'un point de vue fixe pour des directions d'éclairages différentes.Son avantage est d'extraire simultanément les propriétés géométriques et colorimétriquesdes surfaces analysées même en cas de forte rugosité. Néanmoins, son application exige laformulation de plusieurs hypothèses qui sont difficilement respectables dans un contexteréel. Ceci génère des erreurs significatives dans les reconstructions. Pour les réduire, nousproposons différentes contributions qui s'articulent autour de la prise en compte globale de lachaine d'acquisition. Les apports de nos travaux se situent aux niveaux de la caractérisationet de la modélisation du système d'éclairage, du capteur d'acquisition et de l'améliorationde la qualité des images. Nous nous sommes aussi intéressés à l'optimisation des protocolesde prises de vues dans le cas de spécularité surfacique ou d'ombrage dus à la présence derugosité. Les résultats obtenus montrent que la prise en compte de ces caractéristiques dansl'inversion d'un modèle de BRDF permet une nette amélioration des reconstructions et offrela possibilité de réduire la taille des systèmes d'acquisition. / Tridimensional reconstruction method has become essential for applications suchas the characterization and analysis of surfaces. In this thesis, aims are to increase the qualityof 3d reconstructions by photometric stereo. This method is based on two principles, reversinglight-matter interaction model and configuration of a lighting system. With diffuse surfaces,the photometric stereo use three captured images from a fixed point of view for differentillumination directions. Its main advantage is to extract the color and geometric propertiesfor the textured rough surfaces. However, its application requires to make assumptions thatare not credible in real cases. This problem generates significant errors in the reconstructions.To reduce them, we offer various solutions around the overall consideration of the acquisitionchain. Our contribution focuses on the characterization and modeling of the lighting system,the acquisition sensor and improved image quality. We are also interested to optimize acquisitionprotocol in the case of specular surface or shading due to the surface geometry. Ourresults show that the inclusion of these features in the inversion of a BRDF model allowsan improvement of 3d reconstructions as well as the possibility of reducing the size of theacquisition systems.

La thèse est dédiée au problème de la stéréophotométrie non-Lambertienne sans connaissance a priori sur les conditions d’illumination et son application aux images de boîte de Pétri. Pour obtenir une bonne reconstruction de surfaces non-Lambertiennes, il est proposé de traiter une séquence d’entrée en deux étapes: premièrement il faut supprimer les effets spéculaires et obtenir ainsi des images de surface ’pseudo-Lambertienne’. Ensuite dans une deuxième étape à partir de ces images une reconstruction stéréophotométrique Lambertienne sans aucune information préalable sur les directions d’illumination est effectuée. Dans ce travail nous proposons deux méthodes originales respectivement pour la suppression de spécularités et la reconstruction de surface sans information a priori. Les méthodes proposées sont appliquées pour la caractérisation des colonies microbiennes.La spécularités est un effet optique lié à la nature physique complexe des objets. Il est utile pour la perception humaine des objets 3D mais il gêne le processus de traitement automatique d’images. Pour pouvoir appliquer le modèle Lambertien à la stéréophotométrie, les spécularités doivent être supprimées des images d’entrée. Nous proposons donc une méthode originale pour la correction des zones spéculaires adaptée pour une reconstruction ultérieure. L’algorithme proposé est capable de détecter les spécularités comme des valeurs anormalement élevées d’intensité dans une image de la séquence d’entrée, et de les corriger en utilisant les informations des autres images de la séquence et une fonction de correction continue. Cette méthode permet de faire la suppression des spécularités en préservant toutes les autres particularités de distribution de lumière qui sont importantes pour la reconstruction de surface.Après nous proposons une technique de reconstruction stéréophotométrique de surface Lambertienne sans connaissance a priori sur l’illumination. Le modèle mis en œuvre consiste en quatre composantes, deux composantes (albédo et normales) permettent de d´écrire des propriétés de surface et deux autres (intensités des sources de lumière et leurs directions) décrivent illumination. L’algorithme proposé de reconstruction utilise le principe de l’optimisation alternée. Chaque composante du modèle est trouvée itérativement en fixant toutes les variables sauf une et en appliquant des contraintes de structures, valeurs et qualité pour la fonction d’optimisation. Un schéma original de résolution permet de séparer les différents types d’information inclus dans les images d’entrée. Grâce à cette factorisation de matrices, la reconstruction de surface est faite sans connaissance préalable sur les directions de lumière et les propriétés de l’objet reconstruit. L’applicabilité de l’algorithme est prouvée pour des donnés artificielles et des images de bases publiques pour lesquelles la vérité terrain sur les surfaces des objets est disponible.La dernière partie de la thèse est dédiée à l’application de la chaine complète proposée pour le traitement d’images de boîte de Pétri. Ces images sont obtenues en utilisant les sources de lumières complexes qui sont supposées être inconnues pour le processus de reconstruction. L’évaluation de surfaces de colonies microbiennes s’est révélée être une étape importante pour l'analyse visuelle et automatique des colonies. La chaine proposée est efficace pour ce type de données et permet de compléter les informations d'images par de la surface 3D. / The PhD thesis work is dedicated to the problem of uncalibrated non-Lambertian photometric stereo surface reconstruction. The proposed approach consists in two phases: first we correct images of the input sequence from specularities in order to obtain images of pseudo-Lambertian surfaces, and then realize Lambertian photometric stereo reconstruction. In this work we proposed two original methods, respectively, for specularity correction and surface reconstruction with no prior information neither on light sources nor on surface properties. We apply the novel processing to Petri dish images for microbial colonies surface reconstruction.Specularity is an optical effect of a complex physical nature. This effect is useful for human 3D objects perception but it affects automated image processing. In order to be able to apply the Lambertian photometric stereo model, specularities should be removed from the input images. We propose an original method for specular zones correction adapted to estimation of pseudo-Lambertian surface images and further reconstruction. This algorithm is able to detect specularities as abnormally elevated pixel intensity values in an image of the input sequence and to correct the found zones using information from all other images of the sequence and a specific continuous correcting function. This method allows removing specularities while still preserving all other particularities of shading important for the further surface reconstruction.We then propose an original stereo photometric method for Lambertian surface reconstruction with no prior on illuminations. The implemented photometric stereo model consists of four components, two of them (albedo and normals) describe surface properties and the others (light sources intensities and directions) describe illumination. The proposed algorithm of the photometric stereo reconstruction uses the alternating optimization principle. Each model component is found iteratively fixing all variables but one and applying value and quality constraints for the optimization function. The original scheme of resolution allows separating of different information types included in input images. Thanks to such matrix factorization, the surface reconstruction is made with no prior information on lighting directions and the reconstructed objects properties. The applicability of the algorithm is proved using artificially created and open data-sets for which the ground truth information is available.The last part of the thesis is dedicated to the application of the proposed uncalibrated non- Lambertian photometric stereo approach to the Petri dish images. Images are obtained using illuminating sources which are supposed to be unknown for the reconstruction process. Moreover, the reconstructed microbial colonies are very diverse, generally have small size, can be Lambertian or not, and their surface properties are not defined in advance. The results of reconstruction for such complex real-world data add value and importance to the developed approach.

La visió és probablement el nostre sentit més dominant a partir del qual derivem la majoria d'informació del món que ens envolta. A través de la visió podem percebre com són les coses, on són i com es mouen. En les imatges que percebem amb el nostre sistema de visió podem extreure'n característiques com el color, la textura i la forma, i gràcies a aquesta informació som capaços de reconèixer objectes fins i tot quan s'observen sota unes condicions totalment diferents. Per exemple, som capaços de distingir un mateix objecte si l'observem des de diferents punts de vista, distància, condicions d'il·luminació, etc.La Visió per Computador intenta emular el sistema de visió humà mitjançant un sistema de captura d'imatges, un ordinador, i un conjunt de programes. L'objectiu desitjat no és altre que desenvolupar un sistema que pugui entendre una imatge d'una manera similar com ho realitzaria una persona. Aquesta tesi es centra en l'anàlisi de la textura per tal de realitzar el reconeixement de superfícies. La motivació principal és resoldre el problema de la classificació de superfícies texturades quan han estat capturades sota diferents condicions, com ara distància de la càmera o direcció de la il·luminació. D'aquesta forma s'aconsegueix reduir els errors de classificació provocats per aquests canvis en les condicions de captura.En aquest treball es presenta detalladament un sistema de reconeixement de textures que ens permet classificar imatges de diferents superfícies capturades en diferents condicions. El sistema proposat es basa en un model 3D de la superfície (que inclou informació de color i forma) obtingut mitjançant la tècnica coneguda com a 4-Source Colour Photometric Stereo (CPS). Aquesta informació és utilitzada posteriorment per un mètode de predicció de textures amb l'objectiu de generar noves imatges 2D de les textures sota unes noves condicions. Aquestes imatges virtuals que es generen seran la base del nostre sistema de reconeixement, ja que seran utilitzades com a models de referència per al nostre classificador de textures.El sistema de reconeixement proposat combina les Matrius de Co-ocurrència per a l'extracció de característiques de textura, amb la utilització del Classificador del veí més proper. Aquest classificador ens permet al mateix temps aproximar la direcció d'il·luminació present en les imatges que s'utilitzen per testejar el sistema de reconeixement. És a dir, serem capaços de predir l'angle d'il·luminació sota el qual han estat capturades les imatges de test. Els resultats obtinguts en els diferents experiments que s'han realitzat demostren la viabilitat del sistema de predicció de textures, així com del sistema de reconeixement. / This thesis is concerned with the application of texture analysis to discriminate between textured surfaces. The main motivation is the problem of classifying textured surfaces imaged under varying geometries, i.e. distance from the sensor and illumination direction, as well as the necessity of finding reliable methods of reducing classification errors caused by changes in the geometry's properties. In texture analysis one must distinguish between image texture and surface texture. Image texture is what appears in the 2D image of a physical object, while surface texture refers to the variation of the physical and geometric properties of the imaged surface which give rise to the image texture. Changes in the imaging geometry can significantly alter the appearance of the surface, implying significant variations in the image texture. And one still has to perform the task of recognition from the image texture. In this thesis, after analysing different strategies, we integrate the surface texture information derived by colour photometric stereo (CPS) into a complete model-based texture classification system. Photometric stereo is the technique which allows us to obtain surface texture information from a few images of the same surface imaged under various illumination directions. Basically, the main idea of our strategy consists of creating, by means of the surface texture information, a virtual' database of image textures against which we compare unknown test images in order to classify them. Note that we do not use the surface texture information directly to perform classification, but we use it to create new images which are the references for our training and classification process. Furthermore, the classification system allows us to guess the approximate direction of the illumination used to capture the test images.The proposed prediction methods, as well as the model-based texture classification system, are tested and evaluated. A set of real surface textures containing a wide variety of relatively smooth and very rough surfaces are used in this thesis as our image database.

Cette thèse traite de la reconstruction 3D par stéréophotométrie, qui consiste à utiliser plusieurs photographies d'une scène prises sous le même angle, mais sous différents éclairages. Nous nous intéressons dans un premier temps à des techniques robustes pour l'estimation des normales à la surface, et pour leur intégration en une carte de profondeur. Nous étudions ensuite deux situations où le problème est mal posé : lorsque les éclairages sont inconnus, ou lorsque seuls deux éclairages sont utilisés. La troisième partie est consacrée à l'étude de modèles plus réalistes, à la fois en ce qui concerne les éclairages et la réflectance de la surface. Ces trois premières parties nous amènent aux limites de la formulation classique de la stéréophotométrie : nous introduisons finalement, dans la partie 4, une reformulation variationnelle et différentielle du problème qui permet de dépasser ces limites. / This thesis tackles the photometric stereo problem, a 3D-reconstruction technique consisting in taking several pictures of a scene under different lightings. We first focus on robust techniques for estimating the normals to the surface, and for integrating these normals into a depth map. Then, we study two situations where the problem is ill-posed: when lightings are unknown and when only two images are used. Part 3 is devoted to more realistic models, in terms of lightings and of surface reflectance. These first three parts bring us to the limits of the usual formulation of photometric stereo: we eventually introduce in Part 4 a variational and differential reformulation of this problem which allows us to overcome these limits.

Comprendre, analyser et modéliser l'environment 3D à partir d'images provenant de caméras et d'appareils photos est l'un des défis majeurs actuel de recherche en vision par ordinateur. Cette thèse s'interesse à plusieurs aspects géométriques et photometriques liés à la reconstruction de surface à partir de plusieurs caméras calibrées. La reconstruction 3D est vue comme un problème de rendu inverse, et vise à minimiser une fonctionnelle d'énergie afin d'optimiser un maillage triangulaire représentant la surface à reconstruire. L'énergie est définie via un modèle génératif faisant naturellement apparaître des attributs tels que la visibilité ou la photométrie. Ainsi, l'approche présentée peut indifférement s'adapter à divers cas d'application tels que la stéréovision multi-vues, la stéréo photométrique multi-vues ou encore le “shape from shading” multi-vues. Plusieurs approches sont proposées afin de résoudre les problèmes de correspondances de l'apparence pour des scènes non Lambertiennes, dont l'apparence varie en fonction du point de vue. La segmentation, la stéréo photométrique ou encore la réciprocité d'Helmholtz sont des éléments étudiés afin de contraindre la reconstruction. L'exploitation de ces contraintes dans le cadre de reconstruction multi-vues permet de reconstruire des modèles complets 3D avec une meilleure qualité. / Understanding, analyzing and modeling the 3D world from 2D pictures and videos is probably one of the most exciting and challenging problem of computer vision. In this thesis, we address several geometric and photometric aspects to 3D surface reconstruction from multi-view calibrated images. We first formulate multi-view shape reconstruction as an inverse rendering problem. Using generative models, we formulate the problem as an energy minimization method that leads to the non-linear surface optimization of a deformable mesh. A particular attention is addressed to the computation of the discrete gradient flow, which leads to coherent vertices displacements. We particularly focus on models and energy functionals that depend on visibility and photometry. The same framework can then be equally used to perform multi-view stereo, multi-view shape from shading or multi-view photometric stereo. Then, we propose to exploit different additional information to constraint the problem in the non-Lambertian case, where the appearance of the scene depends on the view-point direction. Segmentation for instance can be used to segment surface regions sharing similar appearance or reflectance. Helmholtz reciprocity can also be applied to reconstruct 3D shapes of objects of any arbitrary reflectance properties. By taking multiple image-light pairs around an object, multi-view Helmholtz stereo can be performed. Using this constrained acquisition scenario and our deformable mesh framework, it is possible to reconstruct high quality 3D models.

This thesis treats static and dynamic surface characterization using radiometric and Fourier optical methods. A Fourier optical method has been developed for real time image processing in paper production and printing applications. It has been shown that the method can be used to measure crepe frequency, an important parameter in tissue paper production, as well as to monitor the wire mark pattern at paper web velocities of up to 20 m/s. The wire mark pattern has been used to measure dimensional variations across a paper web. These are important for the mechanical properties of paper. Imaging of the moving surfaces onto a spatial light modulator, necessary for Fourier optical analysis of opaque objects, constitutes a motion blur problem. This problem has been solved by means of optical motion compensation using a rotating mirror. A rotating mirror system has also been developed for the inspection of small particles fixed to a rotating sample disc. The optical motion compensation configurations have made exposure times of more than two orders of magnitude longer than the exposure time without compensation possible. A light scattering model for opaque objects, for example coated paper, has also been developed and verified, with a coefficient of determination between theory and measurement ranging from r2=0.84 to r2=0.98, on various paper samples. The light scattering model has been used in the development of an instrument based on the photometric stereo principle. In this instrument the reflectance (or color) and topography of opaque samples are determined from two or more images of the sample illuminated from different directions. The method has been successfully used for studies of the relation between topography and print results in gravure and flexographic printing. Comparisons of surface height profiles measured with the photometric stereo method and profiles obtained with mechanical and optical scanning stylus instruments have shown coefficients of determination of up to r2=0.97. The main advantages of the method are the high speed, the scalability and the ability to obtain reflectance and surface height maps of a surface simultaneously.