Statistical Analysis of Spherical Harmonics Representations of Soil Particles

RÉSUMÉ :Grâce aux avancées en micro-tomographie par rayons-X, il est désormais possible d’obtenir des représentations en 3D haute résolution de milliers de particules échantillonnées depuis diverses sources géologiques. La représentation plus précise des particules pourrait éventuellement permettre d’obtenir des simulations numériques plus fidèles des comportements de matériaux granulaires par la méthode des éléments discrets (DEM, Discrete Element Method en anglais). Cependant, l’accès à des descriptions fines demande aussi de développer de nouveaux outils numériques pour la caractérisation géométrique et l’analyse statistique d’ensembles de particules. Ce mémoire se concentre sur la modélisation géométrique des particules de sol par la représentation de leur surface à l’aide de la décomposition en harmoniques sphériques. Plus précisément, nous discutons de l’utilisation des représentations en harmoniques sphériques pour développer un modèle statistique permettant de générer des assemblages virtuels de particules à partir des données de plusieurs centaines de grains. La haute dimension de tels ensembles de données a longtemps été une complication majeure, mais avec les récentes avancées en apprentissage automatique dans l’analyse des mégadonnées, il y a espoir que ces nouveaux algorithmes puissent surmonter cette limitation.----------ABSTRACT : Advancements in X-ray micro-computed tomography allow one to obtain high resolution 3D representations of particles collected from multiple geological sources. The representational power enabled by this new technology could allow for more accurate numerical simulations of granular materials using the celebrated Discrete Element Method (DEM). However, access to realistic representations of particles requires the development of more advanced geometrical and statistical characterization techniques. This thesis focuses on the use of the Spherical Harmonics decomposition of soil particles to model the surface of the particles. More precisely, we discuss the application of the Spherical Harmonics decomposition of particles to develop generative models of virtual assemblies that are calibrated based on datasets made of hundreds of grains. For long, the high dimensionality of the data has been a major challenge to the developpement of such statistical models. However, with recent advances of machine learning algorithms in the context of Big Data, there is hope that these new techniques can be utilized to overcome this limitation and obtain very accurate generative models of assemblies

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management

Rotation Estimation Based on Anisotropic Angular Radon Spectrum

In this letter, we present the anisotropic Angular Radon Spectrum (ARS), a novel feature for global estimation of rotation in a two dimension space. ARS effectively describes collinearity of points and has the properties of translation-invariance and shift-rotation. We derive the analytical expression of ARS for Gaussian Mixture Models (GMM) representing point clouds where the Gaussian kernels have arbitrary covariances. Furthermore, we developed a preliminary procedure for simplification of GMM suitable for efficient computation of ARS. Rotation between point clouds is estimated by searching of maximum of correlation between their spectra. Correlation is efficiently computed from Fourier series expansion of ARS. Experiments on datasets of distorted object shapes, laser scans and on robotic mapping datasets assess the accuracy and robustness to noise in global rotation estimation

Visualizing and Predicting the Effects of Rheumatoid Arthritis on Hands

This dissertation was inspired by difficult decisions patients of chronic diseases have to make about about treatment options in light of uncertainty. We look at rheumatoid arthritis (RA), a chronic, autoimmune disease that primarily affects the synovial joints of the hands and causes pain and deformities. In this work, we focus on several parts of a computer-based decision tool that patients can interact with using gestures, ask questions about the disease, and visualize possible futures. We propose a hand gesture based interaction method that is easily setup in a doctor\u27s office and can be trained using a custom set of gestures that are least painful. Our system is versatile and can be used for operations like simple selections to navigating a 3D world. We propose a point distribution model (PDM) that is capable of modeling hand deformities that occur due to RA and a generalized fitting method for use on radiographs of hands. Using our shape model, we show novel visualization of disease progression. Using expertly staged radiographs, we propose a novel distance metric learning and embedding technique that can be used to automatically stage an unlabeled radiograph. Given a large set of expertly labeled radiographs, our data-driven approach can be used to extract different modes of deformation specific to a disease

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications