Non-rigid Shape Matching Using Geometry and Photometry

International audienceIn this paper, we tackle the problem of finding correspondences between three-dimensional reconstructions of a deformable surface at different time steps. We suppose that (i) the mechanical underlying model imposes time-constant geodesic distances between points on the surface; and that (ii) images of the real surface are available. This is for instance the case in spatio-temporal shape from videos (e.g. multi-view stereo, visual hulls, etc.) when the surface is supposed approximatively unstretchable. These assumptions allow to exploit both geometry and photometry. In particular we propose an energy based formulation of the problem, extending the work of Bronstein et of. [1]. On the one hand, we show that photometry (i) improves accuracy in case of locally elastic deformations or noisy surfaces and (ii) allows to still find the right solution when [1] fails because of ambiguities (e.g. symmetries). On the other hand, using geometry makes it possible to match shapes that have undergone large motion, which is not possible with usual photometric methods. Numerical experiments prove the efficiency of our method on synthetic and real data

The Young and Bright Type Ia Supernova ASASSN-14lp: Discovery, Early-Time Observations, First-Light Time, Distance to NGC 4666, and Progenitor Constraints

On 2014 Dec. 9.61, the All-Sky Automated Survey for SuperNovae (ASAS-SN or "Assassin") discovered ASASSN-14lp just $\sim2$ days after first light using a global array of 14-cm diameter telescopes. ASASSN-14lp went on to become a bright supernova ($V = 11.94$ mag), second only to SN 2014J for the year. We present prediscovery photometry (with a detection less than a day after first light) and ultraviolet through near-infrared photometric and spectroscopic data covering the rise and fall of ASASSN-14lp for more than 100 days. We find that ASASSN-14lp had a broad light curve ($\Delta m_{15}(B) = 0.80 \pm 0.05$), a $B$-band maximum at $2457015.82 \pm 0.03$, a rise time of $16.94^{+ 0.11 }_{- 0.10 }$ days, and moderate host--galaxy extinction ($E(B-V)_{\textrm{host}} = 0.33 \pm 0.06$). Using ASASSN-14lp we derive a distance modulus for NGC 4666 of $\mu = 30.8 \pm 0.2$ corresponding to a distance of $14.7 \pm 1.5$ Mpc. However, adding ASASSN-14lp to the calibrating sample of Type Ia supernovae still requires an independent distance to the host galaxy. Finally, using our early-time photometric and spectroscopic observations, we rule out red giant secondaries and, assuming a favorable viewing angle and explosion time, any non-degenerate companion larger than $0.34 R_{\textrm{sun}}$.Comment: 12 pages, 9 figures, 4 tables. Accepted to ApJ. Photometric data presented in this submission are included as an ancillary file. For a brief video explaining this paper, see https://www.youtube.com/watch?v=1bOV-Cqs-a

The Young and Bright Type Ia Supernova ASASSN-14lp: Discovery, Early-Time Observations, First-Light Time, Distance to NGC 4666, and Progenitor Constraints

On 2014 Dec. 9.61, the All-Sky Automated Survey for SuperNovae (ASAS-SN or "Assassin") discovered ASASSN-14lp just $\sim2$ days after first light using a global array of 14-cm diameter telescopes. ASASSN-14lp went on to become a bright supernova ($V = 11.94$ mag), second only to SN 2014J for the year. We present prediscovery photometry (with a detection less than a day after first light) and ultraviolet through near-infrared photometric and spectroscopic data covering the rise and fall of ASASSN-14lp for more than 100 days. We find that ASASSN-14lp had a broad light curve ($\Delta m_{15}(B) = 0.796 \pm 0.001_{\textrm{stat}}$), a $B$-band maximum at $2457015.823 \pm 0.030_{\textrm{stat}}$, a rise time of $16.94^{+ 0.11 }_{- 0.11 }$ days, and moderate host--galaxy extinction ($E(B-V)_{\textrm{host}} = 0.329 \pm 0.001_{\textrm{stat}}$). Using ASASSN-14lp we derive a distance modulus for NGC 4666 of $\mu = 30.834 \pm 0.003_{\textrm{stat}} \pm 0.16_{\textrm{syst}}$ corresponding to a distance of $14.68 \pm 0.02_{\textrm{stat}} \pm 1.15_{\textrm{syst}}$ Mpc. However, a tip of the red giant branch distance to the host galaxy should be measured to allow ASASSN-14lp to be added to the calibrating sample of Type Ia supernovae. Finally, using our early-time photometric and spectroscopic data along with our derived light curve properties, we rule out red giant secondaries with limits on the radius of a non-degenerate companion as small as $0.34 \rm{R}_\odot$ for favorable viewing angles and estimates of the explosion time

Heterozygous variants in ZBTB7A cause a neurodevelopmental disorder associated with symptomatic overgrowth of pharyngeal lymphoid tissue, macrocephaly, and elevated fetal hemoglobin

By clinical whole exome sequencing, we identified 12 individuals with ages 3 to 37 years, including three individuals from the same family, with a consistent phenotype of intellectual disability (ID), macrocephaly, and overgrowth of adenoid tissue. All 12 individuals harbored a rare heterozygous variant in ZBTB7A which encodes the transcription factor Zinc finger and BTB-domain containing protein 7A, known to play a role in lympho- and hematopoiesis. ID was generally mild. Fetal hemoglobin (HbF) fraction was elevated 2.2%–11.2% (reference value  6 months) in four of the five individuals for whom results were available. Ten of twelve individuals had undergone surgery at least once for lymphoid hypertrophy limited to the pharynx. In the most severely affected individual (individual 1), airway obstruction resulted in 17 surgical procedures before the age of 13 years. Sleep apnea was present in 8 of 10 individuals. In the nine unrelated individuals, ZBTB7A variants were novel and de novo. The six frameshift/nonsense and four missense variants were spread throughout the gene. This is the first report of a cohort of individuals with this novel syndromic neurodevelopmental disorder

Apprentissage de variétés et applications au traitement de formes et d'images

The amount of data is continuously increasing through online databases such as Flicker1. Not only is the amount of stored data increasing constantly but also the data itself is highly complex. The need for smart algorithms is obvious. Recently, manifold learning has made a strong entry into the computer vision community. This method provides a powerful tool for the analysis of high-dimensional complex data. Manifold learning is based on the assumption that the degrees of freedom of the data are much smaller than the dimension of the data space itself. More specifically, these methods try to recover a submanifold embedded in a high-dimensional space which can even be dimensionally infinite as in the case of shapes. The output of such an algorithm is a mapping into a new space (commonly referred to as feature space) where the analysis of data becomes easier. Often this mapping can be thought of as a parameterization of the dataset. In the first part of this thesis, we introduce the concepts and theory of metric spaces providing the theoretical background to manifold learning. Once the necessary tools are introduced, we will present a survey on linear and non-linear manifold learning algorithms and compare their weak and strong points. In the second part, we will develop two applications using manifold learning techniques. In both applications manifold learning is applied to recover or approximate the metric on the original space data space. In this way distance between points in the original space can be computed using the metric in the feature space. This allows the formulation of distance based optimization problems. In this spirit, we tackle a first problem known under the name of Pre-image problem. We will look at this problem in the context of Kernel PCA and diffusion maps. It turns out, that previous Pre-image methods based on Kernel PCA used a wrong normalization in centred feature space. We propose a more subtle normalization improving previously proposed algorithm for the computation of the Pre-image. We then look at the same problem in the context of diGrâce aux bases de données en ligne, le volume de données ne cesse d accroitre. Non seulement la quantité de donnes augmente mais aussi la complexité des donnes est hautement complexe. Ce fait nécessite le développement d algorithmes performants. Récemment, une nouvelle classe de méthodes connue sous le nom de: "apprentissage de variétés" a été introduite. Ces méthodes présentent un formalisme intéressant et performant pour l analyse de données à très haute dimension. Ces méthode assument que les degrés de liberté dans les données sont bien plus petit que la dimension de l espace des données. Le but de ces algorithmes est retrouve une variété plongée dans un espace à haute dimension (voire infinie). La sortie d un tel algorithme est une fonction transformant les données dans un espace (espace de feature) où l'analyse devient plus facile. Souvent cette fonction est considère comme une para métrisation de la variété. Dans la première partie de ce manuscrit, nous allons introduire les idées principales ainsi que la théorie des espaces métriques. Ceci nous fournira les outils de bases pour les méthodes d'apprentissage de variétés. Par la suite nous présenterons des méthodes linéaires et non- linéaires pour l'apprentissage de variétés et analyserons leurs points forts et faibles. La deuxième partie développera deux applications en utilisant l'apprentissage des variétés. Dans les deux cas l'apprentissage de variétés est appliqué pour approximer le métrique dans l espace initiale. Ainsi la distance entre points dans l'espace originale peut être approximé en utilisant la métrique dans l'espace feature. Ainsi nous pouvant résoudre des problèmes d optimisation basée sur les distances entre points. Dans cette idée nous regardons le premier problème connu sous le nom "problème de la pré-image". Nous analyserons ce problème dans le contexte de la ACP a noyau and la technique des d

Non-rigid Shape Matching Using Geometry and Photometry

International audienceIn this paper, we tackle the problem of finding correspondences between three-dimensional reconstructions of a deformable surface at different time steps. We suppose that (i) the mechanical underlying model imposes time-constant geodesic distances between points on the surface; and that (ii) images of the real surface are available. This is for instance the case in spatio-temporal shape from videos (e.g. multi-view stereo, visual hulls, etc.) when the surface is supposed approximatively unstretchable. These assumptions allow to exploit both geometry and photometry. In particular we propose an energy based formulation of the problem, extending the work of Bronstein et of. [1]. On the one hand, we show that photometry (i) improves accuracy in case of locally elastic deformations or noisy surfaces and (ii) allows to still find the right solution when [1] fails because of ambiguities (e.g. symmetries). On the other hand, using geometry makes it possible to match shapes that have undergone large motion, which is not possible with usual photometric methods. Numerical experiments prove the efficiency of our method on synthetic and real data

Apprentissage de variétés et applications au traitement de formes et d'images

extrait du résumé français : Grâce aux bases de données en ligne, le volume de données ne cesse d accroitre. Non seulement la quantité de donnes augmente mais aussi la complexité des donnes est hautement complexe. Ce fait nécessite le développement d algorithmes performants. Récemment, une nouvelle classe de méthodes connue sous le nom de: "apprentissage de variétés" a été introduite. Ces méthodes présentent un formalisme intéressant et performant pour l analyse de données à très haute dimension. Ces méthode assument que les degrés de liberté dans les données sont bien plus petit que la dimension de l espace des données. Le but de ces algorithmes est retrouve une variété plongée dans un espace à haute dimension (voire in nie). La sortie d un tel algorithme est une fonction transformant les données dans un espace (espace de feature) où l'analyse devient plus facile. Souvent cette fonction est considère comme une para métrisation de la variété. Dans la première partie de ce manuscrit, nous allons introduire les idées principales ainsi que la théorie des espaces métriques. Ceci nous fournira les outils de bases pour les méthodes d'apprentissage de variétés. Par la suite nous présenterons des méthodes linéaires et non- linéaires pour l'apprentissage de variétés et analyserons leurs points forts et faibles. La deuxième partie développera deux applications en utilisant l'apprentissage des variétés. Dans les deux cas l'apprentissage de variétés est appliqué pour approximer le métrique dans l espace initiale. Ainsi la distance entre points dans l'espace originale peut être approximé en utilisant la métrique dans l'espace feature. Ainsi nous pouvant résoudre des problèmes d optimisation basée sur les distances entre points. Dans cette idée nous regardons le premier problème connu sous le nom "problème de la pré-image"extrait du résumé anglais : The amount of data is continuously increasing through online databases such as Flicker1. Not only is the amount of stored data increasing constantly but also the data itself is highly complex. The need for smart algorithms is obvious. Recently, manifold learning has made a strong entry into the computer vision community. This method provides a powerful tool for the analysis of high-dimensional complex data. Manifold learning is based on the assumption that the degrees of freedom of the data are much smaller than the dimension of the data space itself. More speci cally, these methods try to recover a submanifold embedded in a high-dimensional space which can even be dimensionally in nite as in the case of shapes. The output of such an algorithm is a mapping into a new space (commonly referred to as feature space) where the analysis of data becomes easier. Often this mapping can be thought of as a parameterization of the dataset. In the rst part of this thesis, we introduce the concepts and theory of metric spaces providing the theoretical background to manifold learning. Once the necessary tools are introduced, we will present a survey on linear and non-linear manifold learning algorithms and compare their weak and strong points. In the second part, we will develop two applications using manifold learning techniques. In both applications manifold learning is applied to recover or approximate the metric on the original space data space. In this way distance between points in the original space can be computed using the metric in the feature space. This allows the formulation of distance based optimization problems. In this spirit, we tackle a rst problem known under the name of Pre-image problem. We will look at this problem in the context of Kernel PCA and diffusion maps. It turns out, that previous Pre-image methods based on Kernel PCA used a wrong normalization in centred feature space.MARNE-LA-VALLEE-ENPC-BIBL. (774682303) / SudocSudocFranceF

Normalization and preimage problem in gaussian kernel PCA

International audienceKernel PCA has received a lot of attention over the past years and showed usefull for many image processing problems. In this paper we analyse the issue of normalization in Kernel PCA for the pre-image problem. We present a geometric interpretation of the normalization process for the gaussian kernel. As a consequence, we could formulate a correct normalization criterion in centered feature space. Furthermore, we show how the proposed normalization criterion improves previous pre-image methods for the task of image denoising