Statistical Shape Spaces for 3D Data: A Review

International audienceMethods and systems for capturing 3D geometry are becoming increasingly commonplace–and with them a plethora of 3D data. Much of this data is unfortunately corrupted by noise, missing data, occlusions or other outliers. However, when we are interested in the shape of a particular class of objects, such as human faces or bodies, we can use machine learning techniques, applied to clean, registered databases of these shapes, to make sense of raw 3D point clouds or other data. This has applications ranging from virtual change rooms to motion and gait analysis to surgical planning depending on the type of shape. In this chapter, we give an overview of these techniques, a brief review of the literature, and comparative evaluation of two such shape spaces for human faces

Power and sample size calculation of two-sample projection-based testing for sparsely observed functional data

Projection-based testing for mean trajectory differences in two groups of irregularly and sparsely observed functional data has garnered significant attention in the literature because it accommodates a wide spectrum of group differences and (non-stationary) covariance structures. This article presents the derivation of the theoretical power function and the introduction of a comprehensive power and sample size (PASS) calculation toolkit tailored to the projection-based testing method developed by Wang (2021). Our approach accommodates a wide spectrum of group difference scenarios and a broad class of covariance structures governing the underlying processes. Through extensive numerical simulation, we demonstrate the robustness of this testing method by showcasing that its statistical power remains nearly unaffected even when a certain percentage of observations are missing, rendering it 'missing-immune'. Furthermore, we illustrate the practical utility of this test through analysis of two randomized controlled trials of Parkinson's disease. To facilitate implementation, we provide a user-friendly R package fPASS, complete with a detailed vignette to guide users through its practical application. We anticipate that this article will significantly enhance the usability of this potent statistical tool across a range of biostatistical applications, with a particular focus on its relevance in the design of clinical trials

The Sailor diagram – A new diagram for the verification of two-dimensional vector data from multiple models

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the objective, as in the Taylor diagram, of providing a visual diagnostic tool which allows an easy comparison of simulations by multiple models against a reference dataset. However, the Sailor diagram extends this ability to two-dimensional quantities such as currents, wind, horizontal fluxes of water vapour and other geophysical variables by adding features which allow us to evaluate directional properties of the data as well. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the two orthogonal directions (empirical orthogonal functions; EOFs) which best describe the vector data. Since there is no truncation of the retained EOFs, these orthogonal directions explain the total variability of the original dataset. We test the performance of this new diagram to identify the differences amongst the reference dataset and a series of model outputs by using some synthetic datasets and real-world examples with time series of variables such as wind, current and vertically integrated moisture transport. An alternative setup for spatially varying time-fixed fields is shown in the last examples, in which the spatial average of surface wind in the Northern and Southern Hemisphere according to different reanalyses and realizations from ensembles of CMIP5 models are compared. The Sailor diagrams presented here show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows us also to easily retrieve the individual diagnostics of the different components of the mean squared error and additional diagnostics which can be presented in tabular form.This research has been supported by the Spanish Government’s MINECO grant and ERDF (grant no. CGL2016- 76561-R) and the UPV/EHU (grant no. GIU17/02)

Oktoechos Classification and Generation of Liturgical Music using Deep Learning Frameworks

An important feature of the music repertoire of the Syrian tradition is the system of classifying melodies into eight tunes, called ’oktoechos’. In the oktoechos tradition, liturgical hymns are sung in eight modes or eight colours (known as eight ’niram’ in Indian tradition). In this paper, recurrent neural network (RNN) models are used for oktoechos genre classification with the help of musical texture features (MTF) and i-vectors. The performance of the proposed approaches is evaluated using a newly created corpus of liturgical music in the South Indian language, Malayalam. Long short-term memory (LSTM)-based and gated recurrent unit(GRU)-based experiments report the average classification accuracy of 83.76% and 77.77%, respectively, with a significant margin over the i-vector-DNN framework. The experiments demonstrate the potential of RNN models in learning temporal information through MTF in recognizing eight modes of oktoechos system. Furthermore, since the Greek liturgy and Gregorian chant also share similar musical traits with Syrian tradition, the musicological insights observed can potentially be applied to those traditions. Generation of oktoechos genre music style has also been discussed using an encoder-decoder framework. The quality of the generated files is evaluated using a perception test

An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings

We performed an experimental review of current diarization systems for the conversational telephone speech (CTS) domain. In detail, we considered a total of eight different algorithms belonging to clustering-based, end-to-end neural diarization (EEND), and speech separation guided diarization (SSGD) paradigms. We studied the inference-time computational requirements and diarization accuracy on four CTS datasets with different characteristics and languages. We found that, among all methods considered, EEND-vector clustering (EEND-VC) offers the best trade-off in terms of computing requirements and performance. More in general, EEND models have been found to be lighter and faster in inference compared to clustering-based methods. However, they also require a large amount of diarization-oriented annotated data. In particular EEND-VC performance in our experiments degraded when the dataset size was reduced, whereas self-attentive EEND (SA-EEND) was less affected. We also found that SA-EEND gives less consistent results among all the datasets compared to EEND-VC, with its performance degrading on long conversations with high speech sparsity. Clustering-based diarization systems, and in particular VBx, instead have more consistent performance compared to SA-EEND but are outperformed by EEND-VC. The gap with respect to this latter is reduced when overlap-aware clustering methods are considered. SSGD is the most computationally demanding method, but it could be convenient if speech recognition has to be performed. Its performance is close to SA-EEND but degrades significantly when the training and inference data characteristics are less matched.Comment: 52 pages, 10 figure

PCA-enhanced methodology for the identification of partial discharge locations

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly

Impact of media investments on brands’ market shares : a compositional data analysis approach

L’objectif de cette thèse CIFRE, réalisée avec la société d’études de marché BVA en collaboration avec le constructeur automobile Renault, est de mesurer l’impact des investissements media pour différents canaux (télévision, affichage, etc.) sur les parts de marché de différentes marques, en prenant en compte la concurrence et les potentiels effets croisés et synergies entre ces marques, ainsi qu’en tenant compte du prix des véhicules, du contexte réglementaire (i.e. prime à la casse), et des effets retard de la publicité.Nous avons puisé dans les littératures marketing et statistique pour développer, comparer et interpréter plusieurs modèles qui respectent la contrainte de somme unitaire des parts de marché. Une application concrète au marché automobile français est présentée, pour laquelle nous montrons que les parts de marché des marques sont plus ou moins sensibles aux investissements publicitaires consentis dans chaque canal, et qu’il existe de synergies entre certaines marques.The aim of this CIFRE thesis, realized with the market research institute BVA in collaboration with the automobile manufacturer Renault, is to build a model in order to measure the impact of media investments of several channels (television, outdoor, etc.) on the brands’ market shares, taking into account the competition and the potential cross effects and synergies between brands, as well as accounting for the price, the regulatory context (scrapping incentive), and the lagged effects of advertising. We have drawn from marketing and statistical literatures to develop, compare and interpret several models which respect the unit sum constraint of market shares. A practical application to the French automobile market is presented, for which it is shown that brands’ market shares are more or less sensitive to advertising investments made in each channel, and that synergies between brands exist

Suivi des changements des utilisations/occupations du sol en milieu urbain par imagerie satellitale de résolution spatiale moyenne : le cas de la région métropolitaine de Montréal

De nos jours les cartes d’utilisation/occupation du sol (USOS) à une échelle régionale sont habituellement générées à partir d’images satellitales de résolution modérée (entre 10 m et 30 m). Le National Land Cover Database aux États-Unis et le programme CORINE (Coordination of information on the environment) Land Cover en Europe, tous deux fondés sur les images LANDSAT, en sont des exemples représentatifs. Cependant ces cartes deviennent rapidement obsolètes, spécialement en environnement dynamique comme les megacités et les territoires métropolitains. Pour nombre d’applications, une mise à jour de ces cartes sur une base annuelle est requise. Depuis 2007, le USGS donne accès gratuitement à des images LANDSAT ortho-rectifiées. Des images archivées (depuis 1984) et des images acquises récemment sont disponibles. Sans aucun doute, une telle disponibilité d’images stimulera la recherche sur des méthodes et techniques rapides et efficaces pour un monitoring continue des changements des USOS à partir d’images à résolution moyenne. Cette recherche visait à évaluer le potentiel de telles images satellitales de résolution moyenne pour obtenir de l’information sur les changements des USOS à une échelle régionale dans le cas de la Communauté Métropolitaine de Montréal (CMM), une métropole nord-américaine typique. Les études précédentes ont démontré que les résultats de détection automatique des changements dépendent de plusieurs facteurs tels : 1) les caractéristiques des images (résolution spatiale, bandes spectrales, etc.); 2) la méthode même utilisée pour la détection automatique des changements; et 3) la complexité du milieu étudié. Dans le cas du milieu étudié, à l’exception du centre-ville et des artères commerciales, les utilisations du sol (industriel, commercial, résidentiel, etc.) sont bien délimitées. Ainsi cette étude s’est concentrée aux autres facteurs pouvant affecter les résultats, nommément, les caractéristiques des images et les méthodes de détection des changements. Nous avons utilisé des images TM/ETM+ de LANDSAT à 30 m de résolution spatiale et avec six bandes spectrales ainsi que des images VNIR-ASTER à 15 m de résolution spatiale et avec trois bandes spectrales afin d’évaluer l’impact des caractéristiques des images sur les résultats de détection des changements. En ce qui a trait à la méthode de détection des changements, nous avons décidé de comparer deux types de techniques automatiques : (1) techniques fournissant des informations principalement sur la localisation des changements et (2)techniques fournissant des informations à la fois sur la localisation des changements et sur les types de changement (classes « de-à »). Les principales conclusions de cette recherche sont les suivantes : Les techniques de détection de changement telles les différences d’image ou l’analyse des vecteurs de changements appliqués aux images multi-temporelles LANDSAT fournissent une image exacte des lieux où un changement est survenu d’une façon rapide et efficace. Elles peuvent donc être intégrées dans un système de monitoring continu à des fins d’évaluation rapide du volume des changements. Les cartes des changements peuvent aussi servir de guide pour l’acquisition d’images de haute résolution spatiale si l’identification détaillée du type de changement est nécessaire. Les techniques de détection de changement telles l’analyse en composantes principales et la comparaison post-classification appliquées aux images multi-temporelles LANDSAT fournissent une image relativement exacte de classes “de-à” mais à un niveau thématique très général (par exemple, bâti à espace vert et vice-versa, boisés à sol nu et vice-versa, etc.). Les images ASTER-VNIR avec une meilleure résolution spatiale mais avec moins de bandes spectrales que LANDSAT n’offrent pas un niveau thématique plus détaillé (par exemple, boisés à espace commercial ou industriel). Les résultats indiquent que la recherche future sur la détection des changements en milieu urbain devrait se concentrer aux changements du couvert végétal puisque les images à résolution moyenne sont très sensibles aux changements de ce type de couvert. Les cartes indiquant la localisation et le type des changements du couvert végétal sont en soi très utiles pour des applications comme le monitoring environnemental ou l’hydrologie urbaine. Elles peuvent aussi servir comme des indicateurs des changements de l’utilisation du sol. De techniques telles l’analyse des vecteurs de changement ou les indices de végétation son employées à cette fin.Nowadays land use/land cover maps at regional scale are commonly generated with satellite data of medium spatial resolution (between 10 m and 30m). The National Land Cover Database (NLCD) in the United States and the Coordination of Information on the Environment (CORINE) Land Cover program in Europe, both based on LANDSAT images, are two typical examples. However, these maps become rapidly obsolete, especially in highly dynamic areas such as mega cities and metropolitan areas. In many applications, such as to monitor the water quality affected by the Land use/Land cover (LULC) change, the spread of invasive species, policy making for city managers, annual updating of LULC maps is required. Since 2007, the USGS offers access to ortho-rectified LANDSAT imagery free of charge. Both archived (since 1984) and recently acquired images are available. Without doubt, such data availability will stimulate the research on fast and cost effective methods and techniques for “continuous” regional land cover/use map updating using medium resolution satellite imagery. The objective of this research was to evaluate the potential of such medium resolution satellite imagery for providing information on changes useful for the continuous updating of LULC maps at a regional scale in the case of the Montreal Metropolitan Community (MMC) area, a typical North American metropolis. Previous studies have demonstrated that many factors could affect the results of automatic change detection such as: (1) the characteristics of the images (spatial resolution, spectral bands, etc.); (2) the method itself used to automatically detect changes; and (3) the complexity of the landscape. In the study site except for the Central Business District (CBD) and some commercial streets, land uses (industrial, commercial, residential, etc.) are well delimited. Thus this study was focused on the other factors affecting change detection results, namely, the characteristics of the images and the method of change detection. We used 6 spectral bands of LANDSAT TM/ETM+ with 30 m spatial resolution and 3 spectral bands of ASTER-VNIR with 15 m spatial resolution to evaluate the impact of image characteristics on change detection. Concerning the change detection method, we decided to compare two types of automatic techniques: (1) techniques providing information principally on the location of changed areas,and (2) techniques providing information on both the location of changed areas and the type of changes ("from-to" classes). The main conclusions of this research are as follows: Change detection techniques such as image differencing or change vector analysis applied to LANDSAT multi-temporal imagery provide an accurate picture of changed areas in a fast and efficient manner. They can thus be integrated in a continuous monitoring system for a rapid evaluation of the volume of changes. The produced maps could be helpful to guide the acquisition of high spatial resolution imagery if a detailed identification of the type of changes is required. Change detection techniques such as principal component analysis and post-classification comparison applied to LANDSAT multi-temporal imagery could provide a relatively accurate picture of “from-to” classes but at a very general thematic level (for example, built-up to green space and vice-versa, forest lands to bare soil and vice-versa, etc.). ASTER images with better spatial resolution but with less spectral bands than LANDSAT images do not provide more detailed thematic information (for example forest land to commercial or industrial areas). The results indicate that future research should be focused on the detection of changes in the vegetation cover as medium resolution imagery is highly sensitive to this type of surface cover. Maps indicating the location and the type of changes in vegetation cover are in itself very useful for various applications, such as environmental monitoring or urban hydrology, and can be used as indicators on land use changes. Techniques such as change vector analysis or vegetation indices could be used to this end

Wearable Sensors Applied in Movement Analysis

Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges

Modelling of the topsoil organic carbon content by analysing the potential of spectroscopic techniques for digital soil mapping

Soil organic carbon represents the largest terrestrial carbon pool, being one of the most relevant components in the carbon cycle budget and climate change feedbacks. The scientific community and policymakers expressed the need for spatially information about its distribution. This work aims to develop statistical methods to quantify topsoil organic carbon by using spectroscopic data as a tool for digital soil mapping. Firstly, it was explored the capacity of spectroscopy for map soil organic carbon content at regional scale using topsoil samples from Galicia (NW-Spain). Next, it was developed a spatially non-stationary approach that allows mapping soil organic carbon content and also identifying the factors more relevant for its accumulation in Europe. Finally, it was evaluated the capacity of digital soil mapping methods for monitoring the soil organic carbon stocks expected under different climate change scenarios using for such purpose legacy data from Santa Cruz Island (Galapagos)