Scale-space Feature Extraction on Digital Surfaces

International audienceA classical problem in many computer graphics applications consists in extracting significant zones or points on an object surface,like loci of tangent discontinuity (edges), maxima or minima of curvatures, inflection points, etc. These places have specific localgeometrical properties and often called generically features. An important problem is related to the scale, or range of scales,for which a feature is relevant. We propose a new robust method to detect features on digital data (surface of objects in Z^3 ),which exploits asymptotic properties of recent digital curvature estimators. In [1, 2], authors have proposed curvature estimators(mean, principal and Gaussian) on 2D and 3D digitized shapes and have demonstrated their multigrid convergence (for C^3 -smoothsurfaces). Since such approaches integrate local information within a ball around points of interest, the radius is a crucial parameter.In this article, we consider the radius as a scale-space parameter. By analyzing the behavior of such curvature estimators as the ballradius tends to zero, we propose a tool to efficiently characterize and extract several relevant features (edges, smooth and flat parts)on digital surfaces

Décomposition volumique d'images pour l'étude de la microstructure de la neige

Les avalanches de neige sont des phénomènes naturels complexes dont l'occurrence s'explique principalement par la structure et les propriétés du manteau neigeux. Afin de mieux comprendre les évolutions de ces propriétés au cours du temps, il est important de pouvoir caractériser la microstructure de la neige, notamment en termes de grains et de ponts de glace les reliant. Dans ce contexte, l'objectif de cette thèse est la décomposition d'échantillons de neige en grains individuels à partir d'images 3-D de neige obtenues par microtomographie X. Nous présentons ici deux méthodes de décomposition utilisant des algorithmes de géométrie discrète. Sur la base des résultats de ces segmentations, certains paramètres, comme la surface spécifique et la surface spécifique de contact entre grains sont ensuite estimés sur des échantillons de neiges variées. Ces méthodes de segmentation ouvrent de nouvelles perspectives pour la caractérisation de la microstructure de la neige, de ses propriétés, ainsi que de leur évolution au cours du temps.Snow avalanches are complex natural phenomena whose occurrence is mainly due to the structure and properties of the snowpack. To better understand the evolution of these properties over time, it is important to characterize the microstructure of snow, especially in terms of grains and ice necks that connect them. In this context, the objective of this thesis is the decomposition of snow samples into individual grains from 3-D images of snow obtained by X-ray microtomography. We present two decomposition methods using algorithms of discrete geometry. Based on the results of these segmentations, some parameters such as the specific surface area and the specific contact area between grains are then estimated from samples of several snow types. These segmentation methods offer new outlooks for the characterization of the microstructure of snow, its properties, and its time evolution

Interactive Curvature Tensor Visualization on Digital Surfaces

International audienceInteractive visualization is a very convenient tool to explore complex scientific data or to try different parameter settings for a given processing algorithm. In this article, we present a tool to efficiently analyze the curvature tensor on the boundary of potentially large and dynamic digital objects (mean and Gaussian curvatures, principal curvatures , principal directions and normal vector field). More precisely, we combine a fully parallel pipeline on GPU to extract an adaptive triangu-lated isosurface of the digital object, with a curvature tensor estimation at each surface point based on integral invariants. Integral invariants being parametrized by a given ball radius, our proposal allows to explore interactively different radii and thus select the appropriate scale at which the computation is performed and visualized

Versatile and efficient pore network extraction method using marker-based watershed segmentation

© 2017 American Physical Society, http://dx.doi.org/10.1103/PhysRevE.96.023307Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of 5003 voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided

Computer simulations of realistic three-dimensional microstructures

A novel and efficient methodology is developed for computer simulations of realistic two-dimensional (2D) and three-dimensional (3D) microstructures. The simulations incorporate realistic 2D and 3D complex morphologies/shapes, spatial patterns, anisotropy, volume fractions, and size distributions of the microstructural features statistically similar to those in the corresponding real microstructures. The methodology permits simulations of sufficiently large 2D as well as 3D microstructural windows that incorporate short-range (on the order of particle/feature size) as well as long-range (hundred times the particle/feature size) microstructural heterogeneities and spatial patterns at high resolution. The utility of the technique has been successfully demonstrated through its application to the 2D microstructures of the constituent particles in wrought Al-alloys, the 3D microstructure of discontinuously reinforced Al-alloy (DRA) composites containing SiC particles that have complex 3D shapes/morphologies and spatial clustering, and 3D microstructure of boron modified Ti-6Al-4V composites containing fine TiB whiskers and coarse primary TiB particles. The simulation parameters are correlated with the materials processing parameters (such as composition, particle size ratio, extrusion ratio, extrusion temperature, etc.), which enables the simulations of rational virtual 3D microstructures for the parametric studies on microstructure-properties relationships. The simulated microstructures have been implemented in the 3D finite-elements (FE)-based framework for simulations of micro-mechanical response and stress-strain curves. Finally, a new unbiased and assumption free dual-scale virtual cycloids probe for estimating surface area of 3D objects constructed by 2D serial section images is also presented.Ph.D.Committee Chair: Arun M. Gokhale; Committee Member: David Frost; Committee Member: Meilin Liu; Committee Member: Burton R Patterson; Committee Member: Min Zho

All-in-one aerial image enhancement network for forest scenes

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.Award-winningPostprint (published version

光学機器を用いた微小凝集体の現場観測 : 乱流と凝集体の関係

東京海洋大学修士学位論文 平成27年度(2015) 海洋環境保全学 第2328号指導教員: 山崎秀勝全文公表年月日: 2018-04-09東京海洋大学201