A Cosmic Watershed: the WVF Void Detection Technique

On megaparsec scales the Universe is permeated by an intricate filigree of clusters, filaments, sheets and voids, the Cosmic Web. For the understanding of its dynamical and hierarchical history it is crucial to identify objectively its complex morphological components. One of the most characteristic aspects is that of the dominant underdense Voids, the product of a hierarchical process driven by the collapse of minor voids in addition to the merging of large ones. In this study we present an objective void finder technique which involves a minimum of assumptions about the scale, structure and shape of voids. Our void finding method, the Watershed Void Finder (WVF), is based upon the Watershed Transform, a well-known technique for the segmentation of images. Importantly, the technique has the potential to trace the existing manifestations of a void hierarchy. The basic watershed transform is augmented by a variety of correction procedures to remove spurious structure resulting from sampling noise. This study contains a detailed description of the WVF. We demonstrate how it is able to trace and identify, relatively parameter free, voids and their surrounding (filamentary and planar) boundaries. We test the technique on a set of Kinematic Voronoi models, heuristic spatial models for a cellular distribution of matter. Comparison of the WVF segmentations of low noise and high noise Voronoi models with the quantitatively known spatial characteristics of the intrinsic Voronoi tessellation shows that the size and shape of the voids are succesfully retrieved. WVF manages to even reproduce the full void size distribution function.Comment: 24 pages, 15 figures, MNRAS accepted, for full resolution, see http://www.astro.rug.nl/~weygaert/tim1publication/watershed.pd

Editing faces in videos

Editing faces in movies is of interest in the special effects industry. We aim at producing effects such as the addition of accessories interacting correctly with the face or replacing the face of a stuntman with the face of the main actor. The system introduced in this thesis is based on a 3D generative face model. Using a 3D model makes it possible to edit the face in the semantic space of pose, expression, and identity instead of pixel space, and due to its 3D nature allows a modelling of the light interaction. In our system we first reconstruct the 3D face, which is deforming because of expressions and speech, the lighting, and the camera in all frames of a monocular input video. The face is then edited by substituting expressions or identities with those of another video sequence or by adding virtual objects into the scene. The manipulated 3D scene is rendered back into the original video, correctly simulating the interaction of the light with the deformed face and virtual objects. We describe all steps necessary to build and apply the system. This includes registration of training faces to learn a generative face model, semi-automatic annotation of the input video, fitting of the face model to the input video, editing of the fit, and rendering of the resulting scene. While describing the application we introduce a host of new methods, each of which is of interest on its own. We start with a new method to register 3D face scans to use as training data for the face model. For video preprocessing a new interest point tracking and 2D Active Appearance Model fitting technique is proposed. For robust fitting we introduce background modelling, model-based stereo techniques, and a more accurate light model

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

Higher-order Losses and Optimization for Low-level and Deep Segmentation

Regularized objectives are common in low-level and deep segmentation. Regularization incorporates prior knowledge into objectives or losses. It represents constraints necessary to address ill-posedness, data noise, outliers, lack of supervision, etc. However, such constraints come at significant costs. First, regularization priors may lead to unintended biases, known or unknown. Since these can adversely affect specific applications, it is important to understand the causes & effects of these biases and to develop their solutions. Second, common regularized objectives are highly non-convex and present challenges for optimization. As known in low-level vision, first-order approaches like gradient descent are significantly weaker than more advanced algorithms. Yet, variants of the gradient descent dominate optimization of the loss functions for deep neural networks due to their size and complexity. Hence, standard segmentation networks still require an overwhelming amount of precise pixel-level supervision for training. This thesis addresses three related problems concerning higher-order objectives and higher-order optimizers. First, we focus on a challenging application—unsupervised vascular tree extraction in large 3D volumes containing complex ``entanglements" of near-capillary vessels. In the context of vasculature with unrestricted topology, we propose a new general curvature-regularizing model for arbitrarily complex one-dimensional curvilinear structures. In contrast, the standard surface regularization methods are impractical for thin vessels due to strong shrinking bias or the complexity of Gaussian/min curvature modeling for two-dimensional manifolds. In general, the shrinking bias is one well-known example of bias in the standard regularization methods. The second contribution of this thesis is a characterization of other new forms of biases in classical segmentation models that were not understood in the past. We develop new theories establishing data density biases in common pair-wise or graph-based clustering objectives, such as kernel K-means and normalized cut. This theoretical understanding inspires our new segmentation algorithms avoiding such biases. The third contribution of the thesis is a new optimization algorithm addressing the limitations of gradient descent in the context of regularized losses for deep learning. Our general trust-region algorithm can be seen as a high-order chain rule for network training. It can use many standard low-level regularizers and their powerful solvers. We improve the state-of-the-art in weakly-supervised semantic segmentation using a well-motivated low-level regularization model and its graph-cut solver

Développement d'un algorithme de cinématique d'interaction appliqué sur un bras robotique dans un contexte de coopération humain-robot

Ce mémoire présente le développement d’un algorithme de cinématique d’interaction dans un contexte de coopération humain-robot. Cet algorithme vise à faciliter le contrôle de bras robotiques en évitant les collisions, singularités et limites articulaires du robot lorsqu’il est contrôlé par un humain. La démarche adoptée pour l’ateinte de l’objectif est le prototypage d’un algorithme sur une structure robotique simple, la validation expérimentale de ce prototype, la généralisation de l’algorithme sur une base robotique à 6 degrés de liberté et la validation de l’algorithme final en le comparant expérimentalement avec un algorithme similaire. Premièrement, le prototype d’algorithme est développé sur un bras robotisé Jaco, de l’entreprise Kinova, duquel le poignet a été retiré. Cette architecture permet le déplacement de l’effecteur selon 3 degrés de liberté en translation. L’algorithme développé sur cette base robotique permet : — l’évitement des collisions avec les objets présents dans l’environnement de travail, — l’évitement des limitations articulaires — et l’évitement des singularités propres à l’architecture du robot. Les performances de l’algorithme sont ensuite validées lors d’expérimentations dans lesquelles il a été démontré que l’algorithme a permis une réduction d’approximativement 50% du temps de complétion d’une tâche donnée tout en réduisant l’attention que l’utilisateur doit porter sur le contrôle du robot comparativement à l’attention portée à l’accomplissement de la tâche demandée. Ensuite, des améliorations sont apportées à l’infrastructure : — une méthode de numérisation de l’environnement de travail est ajoutée, — un meilleur algorithme de détection de collisions et de mesure des distances minimales entre les membrures du robot et les obstacles présents dans l’environnement de travail est implémenté. De plus, une méthode de balayage de l’environnement de travail à l’aide d’une caméra Kinect ainsi qu’un algorithme de segmentation de nuage de points en polygones convexes sont présentés. Des tests effectués avec l’algorithme prototype ont été effectués et ont révélé que bien que des imperfections au niveau de la méthode de balayage existent, ces modifications de l’infrastructure peuvent améliorer la facilité avec laquelle l’algorithme de cinématique d’interaction peut être implémenté. Finalement, l’algorithme implémenté sur une architecture robotique à six degrés de liberté est présenté. Les modifications et les adaptations requises pour effectuer la transition avec la version initiale de l’algorithme sont précisées. Les expérimentations ont validé la performance de l’algorithme vis à vis un autre algorithme de contrôle pour l’évitement de collisions. Elles ont démontré une amélioration de 25% en terme de temps requis pour effectuer une tâche donnée comparé aux temps obtenus avec un algorithme de ressorts-amortisseurs virtuels

Biochronology and paleontology of mid-Cretaceous radiolarians from northern Apennines (Italy) and Betic cordillera (Spain)

Highly diverse radiolarian faunas of middle Cretaceous age have been recovered from pelagic and hemipelagic sequences recording the Barremian-Turonian interval in Mediterranean Regions. Severa! lithologies (limestones, cherty limestones, marls and siliceous shales) were thoroughly examined for radiolarian occurrences in continuous successions of deep-water facies. The study includes localities in the Umbria-Marche Apennines (Apulian Block) and on the External Zones of the Betic Cordillera (Southern lberian Paleomargin). The taxonomy and biochronology of the Mid-Cretaceous radiolarians has been studied in order to construct a precise radiolarian zonation in the Western Mediterranean, on the basis of their vertical distribution. Only the true sequence of species in the fossil record allows one to establish the order in which they evolved. Therefore a detailed biochronological analysis was used as a basis for tracing evolutionary lineages and to elucidate the phylogenetic relationships of the examined taxa. Finally, generic and suprageneric classifications have been partly revised based on my own analysis of the faunal succession. The biochronology has been carried out by means of Unitary Association Method (Guex 1977, 1991 ). A database recording the appearance of 303 species in 29 superposed horizons selected from six hundred samples of seven sections has been used to establish a sequence of 21 Unitary Associations. Each of these associations is defined by the totality of characteristic species pairs. The biochronological analysis has allowed the definition of nine new radiolarian biochronologic units for the middle Cretaceous, each of which is labelled either as a zone or a subzone. These biochronologic units are tied to chronostratigraphy by means of planktonic Foraminifera and calcareous nannofossils previously studied by other authors at the same localities. Two major radiolarian faunal changes coincide with well established major Cretaceous oceanic anoxic events (OAE): early Aptian to late Albian (OAE lA- OAE IC) and Cenomanian-Turonian boundary (OAE 2). All radiolarian species (303) used in the biochronology have been described with complete synonymies. Illustrations include several specimens of each species in order to elucidate the morphologic variability. Three families, 16 genera and 84 species are described as new