Photo2ClipArt: Image Abstraction and Vectorization Using Layered Linear Gradients

International audienceWe present a method to create vector cliparts from photographs. Our approach aims at reproducing two key properties of cliparts: they should be easily editable, and they should represent image content in a clean, simplified way. We observe that vector artists satisfy both of these properties by modeling cliparts with linear color gradients, which have a small number of parameters and approximate well smooth color variations. In addition, skilled artists produce intricate yet editable artworks by stacking multiple gradients using opaque and semi-transparent layers. Motivated by these observations, our goal is to decompose a bitmap photograph into a stack of layers, each layer containing a vector path filled with a linear color gradient. We cast this problem as an optimization that jointly assigns each pixel to one or more layer and finds the gradient parameters of each layer that best reproduce the input. Since a trivial solution would consist in assigning each pixel to a different, opaque layer, we complement our objective with a simplicity term that favors decompositions made of few, semi-transparent layers. However, this formulation results in a complex combinatorial problem combining discrete unknowns (the pixel assignments) and continuous unknowns (the layer parameters). We propose a Monte Carlo Tree Search algorithm that efficiently explores this solution space by leveraging layering cues at image junctions. We demonstrate the effectiveness of our method by reverse-engineering existing cliparts and by creating original cliparts from studio photographs

Extracting Geometric Structures in Images with Delaunay Point Processes

International audienceWe introduce Delaunay Point Processes, a framework for the extraction of geometric structures from images. Our approach simultaneously locates and groups geometric primitives (line segments, triangles) to form extended structures (line networks, polygons) for a variety of image analysis tasks. Similarly to traditional point processes, our approach uses Markov Chain Monte Carlo to minimize an energy that balances fidelity to the input image data with geometric priors on the output structures. However, while existing point processes struggle to model structures composed of interconnected components, we propose to embed the point process into a Delaunay triangulation, which provides high-quality connectivity by construction. We further leverage key properties of the Delaunay triangulation to devise a fast Markov Chain Monte Carlo sampler. We demonstrate the flexibility of our approach on a variety of applications, including line network extraction, object contouring, and mesh-based image compression

A kinematic-geometric model based on ankles’ depth trajectory in frontal plane for gait analysis using a single RGB-D camera

International audienceThe emergence of RGB-D cameras and the development of pose estimation algorithms offer opportunities in biomechanics. However, some challenges still remain when using them for gait analysis, including noise which leads to misidentification of gait events and inaccuracy.Therefore, we present a novel kinematic-geometric model for spatio-temporal gait analysis, based on ankles’ trajectory in the frontal plane and distance-to-camera data (depth). Our approach consists of three main steps: identification of the gait pattern and modeling via parameterized curves, development of a fitting algorithm, and computation of locomotive indices. The proposed fitting algorithm applies on both ankles’ depth data simultaneously, by minimizing through numerical optimization some geometric and biomechanical error functions. For validation, 15 subjects were asked to walk inside the walkway of the OptoGait, while the OptoGait and an RGB-D camera (Microsoft Azure Kinect) were both recording. Then, the spatiotemporal parameters of both feet were computed using the OptoGait and the proposed model. Validation results show that the proposed model yields good to excellent absolute statistical agreement (0.86≤Rc≤ 0.99). Our kinematic-geometric model offers several benefits: (1) It relies only on the ankles’ depth trajectory both for gait events extraction and spatio-temporal parameters’ calculation; (2) it is usablewith any kind of RGB-D camera or even with 3D marker-based motion analysis systems in absence of toes’ and heels’ markers; and (3) it enables improving the results by denoising and smoothing the ankles’ depth trajectory. Hence, the proposed kinematic-geometric model facilitates the development of portable markerless systems for accurate gait analysis

Compact image vectorization by stochastic approaches

Les artistes apprécient les images vectorielles car elles sont compactes et facilement manipulables. Cependant, beaucoup d’artistes expriment leur créativité en dessinant, en peignant ou encore en prenant des photographies. Digitaliser ces contenus produit des images rasterisées. L’objectif de cette thèse est de convertir des images rasterisées en images vectorielles qui sont facilement manipulables. Nous avons formulé le problème de vectorisation comme un problème de minimisation d’énergie que nous avons défini par deux termes. Le premier terme, plutôt classique, mesure la fidélité de l’image vectorielle générée avec l’image rasterisée d’origine. La nouveauté principale est le second terme qui mesure la simplicité de l’image vectorielle générée. Le terme de simplicité est global et contient des variables discrètes, ce qui rend sa minimisation difficile. Nous avons proposé deux algorithmes de vectorisation : un pour la vectorisation de croquis et un autre pour la vectorisation multicouches d’images couleurs. Ces deux algorithmes commencent par extraire des primitives géométriques (un squelette pour les croquis et une segmentation pour les images couleurs) qu’ils assemblent ensuite pour former l’image vectorielle. Dans la dernière partie de la thèse, nous proposons un nouvel algorithme qui est capable de vectoriser des croquis sans étapes préliminaires : on extrait et assemble les primitives simultanément. Nous montrons le potentiel de ce nouvel algorithme pour une variété de problèmes de vision par ordinateur comme l’extraction de réseaux linéiques, l’extraction d’objets et la compression d’images.Artists appreciate vector graphics for their compactness and editability. However many artists express their creativity by sketching, painting or taking photographs. Digitizing these images produces raster graphics. The goal of this thesis is to convert raster graphics into vector graphics that are easy to edit. We cast image vectorization as an energy minimization problem. Our energy is a combination of two terms. The first term measures the fidelity of the vector graphics to the input raster graphics. This term is a standard term for image reconstruction problems. The main novelty is the second term which measures the simplicity of the vector graphics. The simplicity term is global and involves discrete unknowns which makes its minimization challenging. We propose two stochastic optimizations for this formulation: one for the line drawing vectorization problem and another one for the color image vectorization problem. These optimizations start by extracting geometric primitives (skeleton for sketches and segmentation for color images) and then assembling these primitives together to form the vector graphics. In the last chapter we propose a generic optimization method for the problem of geometric shape extraction. This new algorithm does not require any preprocessing step. We show its efficiency in a variety of vision problems including line network extraction, object contouring and image compression

Vectorisation compacte d’images par approches stochastiques

Artists appreciate vector graphics for their compactness and editability. However many artists express their creativity by sketching, painting or taking photographs. Digitizing these images produces raster graphics. The goal of this thesis is to convert raster graphics into vector graphics that are easy to edit. We cast image vectorization as an energy minimization problem. Our energy is a combination of two terms. The first term measures the fidelity of the vector graphics to the input raster graphics. This term is a standard term for image reconstruction problems. The main novelty is the second term which measures the simplicity of the vector graphics. The simplicity term is global and involves discrete unknowns which makes its minimization challenging. We propose two stochastic optimizations for this formulation: one for the line drawing vectorization problem and another one for the color image vectorization problem. These optimizations start by extracting geometric primitives (skeleton for sketches and segmentation for color images) and then assembling these primitives together to form the vector graphics. In the last chapter we propose a generic optimization method for the problem of geometric shape extraction. This new algorithm does not require any preprocessing step. We show its efficiency in a variety of vision problems including line network extraction, object contouring and image compression.Les artistes apprécient les images vectorielles car elles sont compactes et facilement manipulables. Cependant, beaucoup d’artistes expriment leur créativité en dessinant, en peignant ou encore en prenant des photographies. Digitaliser ces contenus produit des images rasterisées. L’objectif de cette thèse est de convertir des images rasterisées en images vectorielles qui sont facilement manipulables. Nous avons formulé le problème de vectorisation comme un problème de minimisation d’énergie que nous avons défini par deux termes. Le premier terme, plutôt classique, mesure la fidélité de l’image vectorielle générée avec l’image rasterisée d’origine. La nouveauté principale est le second terme qui mesure la simplicité de l’image vectorielle générée. Le terme de simplicité est global et contient des variables discrètes, ce qui rend sa minimisation difficile. Nous avons proposé deux algorithmes de vectorisation : un pour la vectorisation de croquis et un autre pour la vectorisation multicouches d’images couleurs. Ces deux algorithmes commencent par extraire des primitives géométriques (un squelette pour les croquis et une segmentation pour les images couleurs) qu’ils assemblent ensuite pour former l’image vectorielle. Dans la dernière partie de la thèse, nous proposons un nouvel algorithme qui est capable de vectoriser des croquis sans étapes préliminaires : on extrait et assemble les primitives simultanément. Nous montrons le potentiel de ce nouvel algorithme pour une variété de problèmes de vision par ordinateur comme l’extraction de réseaux linéiques, l’extraction d’objets et la compression d’images

Fidelity vs. Simplicity: a Global Approach to Line Drawing Vectorization

International audienceVector drawing is a popular representation in graphic design because of the precision, compactness and editability offered by parametric curves. However, prior work on line drawing vectorization focused solely on faithfully capturing input bitmaps, and largely overlooked the problem of producing a compact and editable curve network. As a result, existing algorithms tend to produce overly-complex drawings composed of many short curves and control points, especially in the presence of thick or sketchy lines that yield spurious curves at junctions. We propose the first vectorization algorithm that explicitly balances fidelity to the input bitmap with simplicity of the output, as measured by the number of curves and their degree. By casting this trade-off as a global optimization, our algorithm generates few yet accurate curves, and also disambiguates curve topology at junctions by favoring the simplest interpretations overall. We demonstrate the robustness of our algorithm on a variety of drawings, sketchy cartoons and rough design sketches

Line Drawing Interpretation in a Multi-View Context

International audienceMany design tasks involve the creation of new objects in the context of an existing scene. Existing work in computer vision only provides partial support for such tasks. On the one hand, multi-view stereo algorithms allow the reconstruction of real-world scenes, while on the other hand algorithms for line-drawing interpretation do not take context into account. Our work combines the strength of these two domains to interpret line drawings of imaginary objects drawn over photographs of an existing scene. The main challenge we face is to identify the existing 3D structure that correlates with the line drawing while also allowing the creation of new structure that is not present in the real world. We propose a labeling algorithm to tackle this problem , where some of the labels capture dominant orientations of the real scene while a free label allows the discovery of new orientations in the imaginary scene. We illustrate our algorithm by interpreting line drawings for urban planing, home remodeling, furniture design and cultural heritage

Expression of a Mutant Lamin A That Causes Emery-Dreifuss Muscular Dystrophy Inhibits In Vitro Differentiation of C2C12 Myoblasts

Autosomal dominantly inherited missense mutations in lamins A and C cause several tissue-specific diseases, including Emery-Dreifuss muscular dystrophy (EDMD) and Dunnigan-type familial partial lipodystrophy (FPLD). Here we analyze myoblast-to-myotube differentiation in C2C12 clones overexpressing lamin A mutated at arginine 453 (R453W), one of the most frequent mutations in EDMD. In contrast with clones expressing wild-type lamin A, these clones differentiate poorly or not at all, do not exit the cell cycle properly, and are extensively committed to apoptosis. These disorders are correlated with low levels of expression of transcription factor myogenin and with the persistence of a large pool of hyperphosphorylated retinoblastoma protein. Since clones mutated at arginine 482 (a site responsible for FPLD) differentiate normally, we conclude that C2C12 clones expressing R453W-mutated lamin A represent a good cellular model to study the pathophysiology of EDMD. Our hypothesis is that lamin A mutated at arginine 453 fails to build a functional scaffold and/or to maintain the chromatin compartmentation required for differentiation of myoblasts into myocytes

A human coronavirus OC43 variant harboring persistence-associated mutations in the S glycoprotein differentially induces the unfolded protein response in human neurons as compared to wild-type virus

AbstractWe have reported that human respiratory coronavirus OC43 (HCoV-OC43) is neurotropic and neuroinvasive in humans and mice, and that neurons are the primary target of infection in mice, leading to neurodegenerative disabilities. We now report that an HCoV-OC43 mutant harboring two persistence-associated S glycoprotein point mutations (H183R and Y241H), induced a stronger unfolded protein response (UPR) and translation attenuation in infected human neurons. There was a major contribution of the IRE1/XBP1 pathway, followed by caspase-3 activation and nuclear fragmentation, with no significant role of the ATF6 and eIF2-alpha/ATF4 pathways. Our results show the importance of discrete molecular viral S determinants in virus–neuronal cell interactions that lead to increased production of viral proteins and infectious particles, enhanced UPR activation, and increased cytotoxicity and cell death. As this mutant virus is more neurovirulent in mice, our results also suggest that two mutations in the S glycoprotein could eventually modulate viral neuropathogenesis

Prospective and multicentre study of radiofrequency treatment in anal fistula

International audienceBACKGROUND: The most effective treatment for anal fistula is fistulotomy, but it involves a risk of anal incontinence. To reduce this morbidity, sphincter-sparing treatments have been developed, but their success in real life is often less than 50%. The aim is to determine clinical healing rate 6 months after radiofrequency treatment. PATIENTS AND METHODS: We planned to evaluate 50 patients from 3 French proctology centres. Treatment efficacy was evaluated at 6 and 12 months by means of clinical and magnetic resonance imaging examination. We evaluated morbidity and healing prognostic factors. RESULTS: Fifty patients with a mean age of 51years [22-82] were included. Eleven patients had a low transsphincteric fistula(LTS), 21 high transsphincteric fistula(HTS), 8 complex fistula and 9 Crohn’s disease fistula. After 6 months, 17 patients (34.7%) had a clinically healed fistula, including 5 (45.5%) with LTS fistula, 7 (33.3%) with HTS fistula, 1 (12.5%) with complex fistula, 4 (44.4%) with Crohn’s disease, with no significant difference between these fistula types (p:0.142). At 12 months, the healing rate was identical. MRI in 15 out of 17 clinically healed patients showed a deep remission of 73.3% at 12 months. Energy power was associated with the success of the treatment. There was an 8.2% incidence of post-surgical complications with 4.1% being abscesses (one required surgical management). Postoperative pain was minor. No new cases or deterioration of continence have been shown. CONCLUSION: Radiofrequency is effective in less than 50% of the cases as an anal fistula treatment in this first prospective study, with low morbidity and no effect on continence. Clinical healing was deep (MRI) in ¾ at 1 year. The increase in energy power during the procedure seems to be a key point to be analysed to optimise results