Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

© 2016, Springer Science+Business Media New York. We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e.g. a ruler). For the segmentation part, we focus on the graph-based method presented in Bertozzi and Flenner (Multiscale Model Simul 10(3):1090–1118, 2012) which reinterprets classical continuous Ginzburg–Landau minimisation models in a totally discrete framework. To overcome the numerical difficulties due to the large size of the images considered, we use matrix completion and splitting techniques. The scale on the measurement tool is detected via a Hough transform-based algorithm. The method is then applied to some measurement tasks arising in real-world applications such as zoology, medicine and archaeology

New PDE models for imaging problems and applications

Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the mathematical formulation of a myriad of problems describing physical phenomena such as heat propagation, thermodynamic transformations and many more. In imaging, PDEs following variational principles are often considered. In their general form these models combine a regularisation and a data fitting term, balancing one against the other appropriately. Total variation (TV) regularisation is often used due to its edgepreserving and smoothing properties. In this thesis, we focus on the design of TV-based models for several different applications. We start considering PDE models encoding higher-order derivatives to overcome wellknown TV reconstruction drawbacks. Due to their high differential order and nonlinear nature, the computation of the numerical solution of these equations is often challenging. In this thesis, we propose directional splitting techniques and use Newton-type methods that despite these numerical hurdles render reliable and efficient computational schemes. Next, we discuss the problem of choosing the appropriate data fitting term in the case when multiple noise statistics in the data are present due, for instance, to different acquisition and transmission problems. We propose a novel variational model which encodes appropriately and consistently the different noise distributions in this case. Balancing the effect of the regularisation against the data fitting is also crucial. For this sake, we consider a learning approach which estimates the optimal ratio between the two by using training sets of examples via bilevel optimisation. Numerically, we use a combination of SemiSmooth (SSN) and quasi-Newton methods to solve the problem efficiently. Finally, we consider TV-based models in the framework of graphs for image segmentation problems. Here, spectral properties combined with matrix completion techniques are needed to overcome the computational limitations due to the large amount of image data. Further, a semi-supervised technique for the measurement of the segmented region by means of the Hough transform is proposed

State of research in automatic as-built modelling

This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.aei.2015.01.001Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.We acknowledge the support of EPSRC Grant NMZJ/114,DARPA UPSIDE Grant A13–0895-S002, NSF CAREER Grant N. 1054127, European Grant Agreements No. 247586 and 334241. We would also like to thank NSERC Canada, Aecon, and SNC-Lavalin for financially supporting some parts of this research

Extraction robuste de primitives géométriques 3D dans un nuage de points et alignement basé sur les primitives

Dans ce projet, nous étudions les problèmes de rétro-ingénierie et de contrôle de la qualité qui jouent un rôle important dans la fabrication industrielle. La rétro-ingénierie tente de reconstruire un modèle 3D à partir de nuages de points, qui s’apparente au problème de la reconstruction de la surface 3D. Le contrôle de la qualité est un processus dans lequel la qualité de tous les facteurs impliqués dans la production est abordée. En fait, les systèmes ci-dessus nécessitent beaucoup d’intervention de la part d’un utilisateur expérimenté, résultat souhaité est encore loin soit une automatisation complète du processus. Par conséquent, de nombreux défis doivent encore être abordés pour atteindre ce résultat hautement souhaitable en production automatisée. La première question abordée dans la thèse consiste à extraire les primitives géométriques 3D à partir de nuages de points. Un cadre complet pour extraire plusieurs types de primitives à partir de données 3D est proposé. En particulier, une nouvelle méthode de validation est proposée pour évaluer la qualité des primitives extraites. À la fin, toutes les primitives présentes dans le nuage de points sont extraites avec les points de données associés et leurs paramètres descriptifs. Ces résultats pourraient être utilisés dans diverses applications telles que la reconstruction de scènes on d’édifices, la géométrie constructive et etc. La seconde question traiée dans ce travail porte sur l’alignement de deux ensembles de données 3D à l’aide de primitives géométriques, qui sont considérées comme un nouveau descripteur robuste. L’idée d’utiliser les primitives pour l’alignement arrive à surmonter plusieurs défis rencontrés par les méthodes d’alignement existantes. Ce problème d’alignement est une étape essentielle dans la modélisation 3D, la mise en registre, la récupération de modèles. Enfin, nous proposons également une méthode automatique pour extraire les discontinutés à partir de données 3D d’objets manufacturés. En intégrant ces discontinutés au problème d’alignement, il est possible d’établir automatiquement les correspondances entre primitives en utilisant l’appariement de graphes relationnels avec attributs. Nous avons expérimenté tous les algorithmes proposés sur différents jeux de données synthétiques et réelles. Ces algorithmes ont non seulement réussi à accomplir leur tâches avec succès mais se sont aussi avérés supérieus aux méthodes proposées dans la literature. Les résultats présentés dans le thèse pourraient s’avérér utilises à plusieurs applications.In this research project, we address reverse engineering and quality control problems that play significant roles in industrial manufacturing. Reverse engineering attempts to rebuild a 3D model from the scanned data captured from a object, which is the problem similar to 3D surface reconstruction. Quality control is a process in which the quality of all factors involved in production is monitored and revised. In fact, the above systems currently require significant intervention from experienced users, and are thus still far from being fully automated. Therefore, many challenges still need to be addressed to achieve the desired performance for automated production. The first proposition of this thesis is to extract 3D geometric primitives from point clouds for reverse engineering and surface reconstruction. A complete framework to extract multiple types of primitives from 3D data is proposed. In particular, a novel validation method is also proposed to assess the quality of the extracted primitives. At the end, all primitives present in the point cloud are extracted with their associated data points and descriptive parameters. These results could be used in various applications such as scene and building reconstruction, constructive solid geometry, etc. The second proposition of the thesis is to align two 3D datasets using the extracted geometric primitives, which is introduced as a novel and robust descriptor. The idea of using primitives for alignment is addressed several challenges faced by existing registration methods. This alignment problem is an essential step in 3D modeling, registration and model retrieval. Finally, an automatic method to extract sharp features from 3D data of man-made objects is also proposed. By integrating the extracted sharp features into the alignment framework, it is possible implement automatic assignment of primitive correspondences using attribute relational graph matching. Each primitive is considered as a node of the graph and an attribute relational graph is created to provide a structural and relational description between primitives. We have experimented all the proposed algorithms on different synthetic and real scanned datasets. Our algorithms not only are successful in completing their tasks with good results but also outperform other methods. We believe that the contribution of them could be useful in many applications

A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors

Machine learning methods for 3D object classification and segmentation

Field of study: Computer science.Dr. Ye Duan, Thesis Supervisor.Includes vita."July 2018."Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation. The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset. The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation.Includes bibliographical references (pages 116-140)

Efficient Dense Registration, Segmentation, and Modeling Methods for RGB-D Environment Perception

One perspective for artificial intelligence research is to build machines that perform tasks autonomously in our complex everyday environments. This setting poses challenges to the development of perception skills: A robot should be able to perceive its location and objects in its surrounding, while the objects and the robot itself could also be moving. Objects may not only be composed of rigid parts, but could be non-rigidly deformable or appear in a variety of similar shapes. Furthermore, it could be relevant to the task to observe object semantics. For a robot acting fluently and immediately, these perception challenges demand efficient methods. This theses presents novel approaches to robot perception with RGB-D sensors. It develops efficient registration, segmentation, and modeling methods for scene and object perception. We propose multi-resolution surfel maps as a concise representation for RGB-D measurements. We develop probabilistic registration methods that handle rigid scenes, scenes with multiple rigid parts that move differently, and scenes that undergo non-rigid deformations. We use these methods to learn and perceive 3D models of scenes and objects in both static and dynamic environments. For learning models of static scenes, we propose a real-time capable simultaneous localization and mapping approach. It aligns key views in RGB-D video using our rigid registration method and optimizes the pose graph of the key views. The acquired models are then perceived in live images through detection and tracking within a Bayesian filtering framework. An assumption frequently made for environment mapping is that the observed scene remains static during the mapping process. Through rigid multi-body registration, we take advantage of releasing this assumption: Our registration method segments views into parts that move independently between the views and simultaneously estimates their motion. Within simultaneous motion segmentation, localization, and mapping, we separate scenes into objects by their motion. Our approach acquires 3D models of objects and concurrently infers hierarchical part relations between them using probabilistic reasoning. It can be applied for interactive learning of objects and their part decomposition. Endowing robots with manipulation skills for a large variety of objects is a tedious endeavor if the skill is programmed for every instance of an object class. Furthermore, slight deformations of an instance could not be handled by an inflexible program. Deformable registration is useful to perceive such shape variations, e.g., between specific instances of a tool. We develop an efficient deformable registration method and apply it for the transfer of robot manipulation skills between varying object instances. On the object-class level, we segment images using random decision forest classifiers in real-time. The probabilistic labelings of individual images are fused in 3D semantic maps within a Bayesian framework. We combine our object-class segmentation method with simultaneous localization and mapping to achieve online semantic mapping in real-time. The methods developed in this thesis are evaluated in experiments on publicly available benchmark datasets and novel own datasets. We publicly demonstrate several of our perception approaches within integrated robot systems in the mobile manipulation context.Effiziente Dichte Registrierungs-, Segmentierungs- und Modellierungsmethoden für die RGB-D Umgebungswahrnehmung In dieser Arbeit beschäftigen wir uns mit Herausforderungen der visuellen Wahrnehmung für intelligente Roboter in Alltagsumgebungen. Solche Roboter sollen sich selbst in ihrer Umgebung zurechtfinden, und Wissen über den Verbleib von Objekten erwerben können. Die Schwierigkeit dieser Aufgaben erhöht sich in dynamischen Umgebungen, in denen ein Roboter die Bewegung einzelner Teile differenzieren und auch wahrnehmen muss, wie sich diese Teile bewegen. Bewegt sich ein Roboter selbständig in dieser Umgebung, muss er auch seine eigene Bewegung von der Veränderung der Umgebung unterscheiden. Szenen können sich aber nicht nur durch die Bewegung starrer Teile verändern. Auch die Teile selbst können ihre Form in nicht-rigider Weise ändern. Eine weitere Herausforderung stellt die semantische Interpretation von Szenengeometrie und -aussehen dar. Damit intelligente Roboter unmittelbar und flüssig handeln können, sind effiziente Algorithmen für diese Wahrnehmungsprobleme erforderlich. Im ersten Teil dieser Arbeit entwickeln wir effiziente Methoden zur Repräsentation und Registrierung von RGB-D Messungen. Zunächst stellen wir Multi-Resolutions-Oberflächenelement-Karten (engl. multi-resolution surfel maps, MRSMaps) als eine kompakte Repräsentation von RGB-D Messungen vor, die unseren effizienten Registrierungsmethoden zugrunde liegt. Bilder können effizient in dieser Repräsentation aggregiert werde, wobei auch mehrere Bilder aus verschiedenen Blickpunkten integriert werden können, um Modelle von Szenen und Objekte aus vielfältigen Ansichten darzustellen. Für die effiziente, robuste und genaue Registrierung von MRSMaps wird eine Methode vorgestellt, die Rigidheit der betrachteten Szene voraussetzt. Die Registrierung schätzt die Kamerabewegung zwischen den Bildern und gewinnt ihre Effizienz durch die Ausnutzung der kompakten multi-resolutionalen Darstellung der Karten. Die Registrierungsmethode erzielt hohe Bildverarbeitungsraten auf einer CPU. Wir demonstrieren hohe Effizienz, Genauigkeit und Robustheit unserer Methode im Vergleich zum bisherigen Stand der Forschung auf Vergleichsdatensätzen. In einem weiteren Registrierungsansatz lösen wir uns von der Annahme, dass die betrachtete Szene zwischen Bildern statisch ist. Wir erlauben nun, dass sich rigide Teile der Szene bewegen dürfen, und erweitern unser rigides Registrierungsverfahren auf diesen Fall. Unser Ansatz segmentiert das Bild in Bereiche einzelner Teile, die sich unterschiedlich zwischen Bildern bewegen. Wir demonstrieren hohe Segmentierungsgenauigkeit und Genauigkeit in der Bewegungsschätzung unter Echtzeitbedingungen für die Verarbeitung. Schließlich entwickeln wir ein Verfahren für die Wahrnehmung von nicht-rigiden Deformationen zwischen zwei MRSMaps. Auch hier nutzen wir die multi-resolutionale Struktur in den Karten für ein effizientes Registrieren von grob zu fein. Wir schlagen Methoden vor, um aus den geschätzten Deformationen die lokale Bewegung zwischen den Bildern zu berechnen. Wir evaluieren Genauigkeit und Effizienz des Registrierungsverfahrens. Der zweite Teil dieser Arbeit widmet sich der Verwendung unserer Kartenrepräsentation und Registrierungsmethoden für die Wahrnehmung von Szenen und Objekten. Wir verwenden MRSMaps und unsere rigide Registrierungsmethode, um dichte 3D Modelle von Szenen und Objekten zu lernen. Die räumlichen Beziehungen zwischen Schlüsselansichten, die wir durch Registrierung schätzen, werden in einem Simultanen Lokalisierungs- und Kartierungsverfahren (engl. simultaneous localization and mapping, SLAM) gegeneinander abgewogen, um die Blickposen der Schlüsselansichten zu schätzen. Für das Verfolgen der Kamerapose bezüglich der Modelle in Echtzeit, kombinieren wir die Genauigkeit unserer Registrierung mit der Robustheit von Partikelfiltern. Zu Beginn der Posenverfolgung, oder wenn das Objekt aufgrund von Verdeckungen oder extremen Bewegungen nicht weiter verfolgt werden konnte, initialisieren wir das Filter durch Objektdetektion. Anschließend wenden wir unsere erweiterten Registrierungsverfahren für die Wahrnehmung in nicht-rigiden Szenen und für die Übertragung von Objekthandhabungsfähigkeiten von Robotern an. Wir erweitern unseren rigiden Kartierungsansatz auf dynamische Szenen, in denen sich rigide Teile bewegen. Die Bewegungssegmente in Schlüsselansichten werden zueinander in Bezug gesetzt, um Äquivalenz- und Teilebeziehungen von Objekten probabilistisch zu inferieren, denen die Segmente entsprechen. Auch hier liefert unsere Registrierungsmethode die Bewegung der Kamera bezüglich der Objekte, die wir in einem SLAM Verfahren optimieren. Aus diesen Blickposen wiederum können wir die Bewegungssegmente in dichten Objektmodellen vereinen. Objekte einer Klasse teilen oft eine gemeinsame Topologie von funktionalen Elementen, die durch Formkorrespondenzen ermittelt werden kann. Wir verwenden unsere deformierbare Registrierung, um solche Korrespondenzen zu finden und die Handhabung eines Objektes durch einen Roboter auf neue Objektinstanzen derselben Klasse zu übertragen. Schließlich entwickeln wir einen echtzeitfähigen Ansatz, der Kategorien von Objekten in RGB-D Bildern erkennt und segmentiert. Die Segmentierung basiert auf Ensemblen randomisierter Entscheidungsbäume, die Geometrie- und Texturmerkmale zur Klassifikation verwenden. Wir fusionieren Segmentierungen von Einzelbildern einer Szene aus mehreren Ansichten in einer semantischen Objektklassenkarte mit Hilfe unseres SLAM-Verfahrens. Die vorgestellten Methoden werden auf öffentlich verfügbaren Vergleichsdatensätzen und eigenen Datensätzen evaluiert. Einige unserer Ansätze wurden auch in integrierten Robotersystemen für mobile Objekthantierungsaufgaben öffentlich demonstriert. Sie waren ein wichtiger Bestandteil für das Gewinnen der RoboCup-Roboterwettbewerbe in der RoboCup@Home Liga in den Jahren 2011, 2012 und 2013

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)