[Show abstract][Hide abstract] ABSTRACT: This paper deals with the simulation of microbial degradation of organic matter in soil within the pore space at a microscopic scale. Pore space was analysed with micro- computed tomography and described using a sphere network coming from a geometrical modelling algorithm. The bio- logical model was improved regarding previous work in or- der to include the transformation of dissolved organic com- pounds and diffusion processes. We tested our model using experimental results of a simple substrate decomposition ex- periment (fructose) within a simple medium (sand) in the presence of different bacterial strains. Separate incubations were carried out in microcosms using five different bacterial communities at two different water potentials of −10 and −100 cm of water. We calibrated the biological parameters by means of experimental data obtained at high water con- tent, and we tested the model without changing any parame- ters at low water content. Same as for the experimental data, our simulation results showed that the decrease in water con- tent caused a decrease of mineralization rate. The model was able to simulate the decrease of connectivity between sub- strate and microorganism due the decrease of water content.
[Show abstract][Hide abstract] ABSTRACT: This paper deals with the simulation of microbial degradation in soil
within pore space at microscopic scale. Pore space was described using
sphere network coming from a geometrical modeling algorithm. The
biological model was improved regarding previous work in order to
include transformation of dissolved organic compounds and diffusion
processes. Our model was tested using experimental results of a simple
substrate decomposition (Fructose) within a simple media (the sand).
Diverse microbial communities were inoculated. Separated incubations in
microcosms were carried out using 5 different bacterial communities at 2
different water potentials of -10 cm and -100 cm of water. We calibrated
the biological parameters by means of experimental data obtained at high
water content and we tested the model without any parameters change at
low water content. Same as for experimental data, our simulation results
showed the decrease in water content involved the decrease of
mineralisation. The model was able to simulate the decrease of
connectivity between substrate and microorganism due the decrease of
[Show abstract][Hide abstract] ABSTRACT: This paper focuses on the modeling of soil microstructures using generalized cylinders, with a specific application to pore space. The geometric modeling of these microstructures is a recent area of study, made possible by the improved performance of computed tomography techniques. X-scanners provide very-high-resolution 3D volume images (3–5μm) of soil samples in which pore spaces can be extracted by thresholding. However, in most cases, the pore space defines a complex volume shape that cannot be approximated using simple analytical functions. We propose representing this shape using a compact, stable, and robust piecewise approximation by means of generalized cylinders. This intrinsic shape representation conserves its topological and geometric properties. Our algorithm includes three main processing stages. The first stage consists in describing the volume shape using a minimum number of balls included within the shape, such that their union recovers the shape skeleton. The second stage involves the optimum extraction of simply connected chains of balls. The final stage copes with the approximation of each simply optimal chain using generalized cylinders: circular generalized cylinders, tori, cylinders, and truncated cones. This technique was applied to several data sets formed by real volume computed tomography soil samples. It was possible to demonstrate that our geometric representation supplied a good approximation of the pore space. We also stress the compactness and robustness of this method with respect to any changes affecting the initial data, as well as its coherence with the intuitive notion of pores. During future studies, this geometric pore space representation will be used to simulate biological dynamics.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a geometrical model of soil pore space based on the quantitative analysis of synchrotron X-ray microtomography data. Our model calculated the minimal set of balls that recovered the skeleton of the pore space using Delaunay tessellation, and the simply connected sets of balls that could be considered as potential pore channels. This model (DTM software) was then applied to three-dimensional tomography reconstructions of soil aggregates (~ 5 mm diameter) from two management systems (conventionally tilled soil, namely CTT and grassland soil, namely GL) with a voxel edge length of 3.2 μm and 5.4 μm, respectively. Geometric characteristics such as the frequency distribution of pore radius, length, and tortuosity as well as the retention curve were calculated using our model. The organic matter decomposition was also simulated using DTM approach. The results were compared with pore space statistics obtained during a previously published study on the same data using algorithms based on the medial axis and throat computation (3dma software). The same tendency on the geometrical statistic was obtained using both methods, with more pores of smaller length and diameter calculated for the aggregate from the conventionally tilled site compared to the grassland aggregate. However, the 3dma method generated a larger quantity of voxels (385,673 and 189,250 for CTT and GL, respectively) compared to the amount of balls in DTM (170,250 and 64,273 for CTT and GL, respectively) and shorter channels because of the presence of throats.Highlights► A sophisticated model based on Delaunay triangulation method was presented. ► It extracts soil pores with maximal balls or connected ball chains. ► The model was applied to two 3D contrasting soil images. ► Pore space characteristics of the two soils were very different. ► The results of the model were close to those obtained with a more standard approach.
[Show abstract][Hide abstract] ABSTRACT: This study is the follow-up to a previous one devoted to soil pore space modelling. In the previous study, we proposed algorithms to represent soil pore space by means of optimal piecewise approximation using simple 3D geometrical primitives: balls, cylinders, cones, etc. In the present study, we use the ball-based piecewise approximation to simulate biological activity. The basic idea for modelling pore space consists in representing pore space using a minimal set of maximal balls (Delaunay spheres) recovering the shape skeleton. In this representation, each ball is considered as a maximal local cavity corresponding to the “intuitive” notion of a pore as described in the literature. The space segmentation induced by the network of balls (pores) is then used to spatialise biological dynamics. Organic matter and microbial decomposers are distributed within the balls (pores). A valuated graph representing the pore network, organic matter and microorganism distribution is then defined. Microbial soil organic matter decomposition is simulated by updating this valuated graph. The method has been implemented and tested on real data. As far as we know, this approach is the first one to formally link pore space geometry and biological dynamics. The long-term goal is to define geometrical typologies of pore space shape that can be attached to specific biological dynamic properties. This paper is a first attempt to achieve this goal.
[Show abstract][Hide abstract] ABSTRACT: During the past 10 years, soil scientists have started to use 3D Computed Tomography in order to gain a clearer understanding of the geometry of soil structure and its relationships with soil properties. We propose a geometric model for the 3D representation of pore space and a practical method for its computation. Our basic idea consists in representing pore space using a minimal set of maximal balls (Delaunay spheres) recovering the shape skeleton. In this representation, each ball could be considered as a maximal local cavity corresponding to the “intuitive” notion of a pore as described in the literature. The space segmentation induced by the network of balls (pores) was then used to spatialize biological dynamics. Organic matter and microbial decomposers were distributed within the balls (pores). A valuated graph representing the pore network, organic matter and distribution of micro-organisms was then defined. Microbial soil organic matter decomposition was simulated by updating this valuated graph. The method was implemented and tested using real CT images. The model produced realistic simulated results when compared with data in the literature in terms of the water retention curve and carbon mineralization. A decrease in water pressure decreased carbon mineralization, which is also in accordance with findings in the literature. From our results we showed that the influence of water pressure on decomposition is a function of organic matter distribution in the pore space. As far as we know, this is the approach to have linked pore space geometry and biological dynamics in a formal way. Our next goal will be to compare the model with experimental data of decomposition using different soil structures, and to define geometric typologies of pore space shape that can be attached to specific biological and dynamic properties.
[Show abstract][Hide abstract] ABSTRACT: Only in the last decade have geoscientists started to use 3D computed tomography (CT) images of soil for better understanding and modeling of soil properties. In this paper, we propose one of the first approaches to allow the definition and computation of stable (intrinsic) geometric representations of structures in 3D CT soil images. This addresses the open problem set by the description of volume shapes from discrete traces without any a priori information. The basic concept involves representing the volume shape by a piecewise approximation using simple volume primitives (bowls, cylinders, cones, etc.). This typical representation is assumed to optimize a criterion ensuring its stability. This criterion includes the representation scale, which characterizes the trade-off between the fitting error and the number of patches. We also take into account the preservation of topological properties of the initial shape: the number of connected components, adjacency relationships, etc. We propose an efficient computation method for this piecewise approximation using cylinders or bowls. For cylinders, we use optimal region growing in a valuated adjacency graph that represents the primitives and their adjacency relationships. For bowls, we compute a minimal set of Delaunay spheres recovering the skeleton. Our method is applied to modeling of a coarse pore space extracted from 3D CT soil images. The piecewise bowls approximation gives a geometric formalism corresponding to the intuitive notion of pores and also an efficient way to compute it. This geometric and topological representation of coarse pore space can be used, for instance, to simulate biological activity in soil.
[Show abstract][Hide abstract] ABSTRACT: This paper presents an innovative approach for defining and computing stable (intrinsic) representations describing volume shapes from discrete traces without any a priori information. We assume that the discrete trace of the volume shape is defined by a binary 3D image where all marked points define the shape. Our basic idea is to describe the corresponding volume using a set of patches of volume primitives (bowls, cylinders, cones…). The volume primitives representation is assumed to optimize a criterion ensuring its stability and including a characterization of its scale (trade-off: fitting errors/number of patches). Our criterion takes also into account the preservation of topological properties of the initial shape representation (number of connected components, adjacency relationships…). We propose an efficient computing way to optimize this criterion using optimal region growing in an adjacency valuated graph representing the primitives and their adjacency relationships. Our method is applied to the modelling of porous media from 3D soil images. This new geometrical and topological representation of the pore network can be used to characterize soil properties.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we show how to extract reliable informations about the shape of 3D objects, obtained from volume medical images. We present an optimal region-growing strategy, that makes use of the differential characteristics of the object surface, and achieves a stable segmentation into a set of patches of quadratic surfaces. We show how this segmentation can be used to recognize and locate a target sub-structure on a global anatomic structure.
[Show abstract][Hide abstract] ABSTRACT: Thin nets are the lines where the grey level function is locally extremum in a given direction. Recently, we have shown that it is possible to characterize the thin nets using differential properties of the image surface. However, the method failed when these structures present different widths. In this paper we show that, the extraction process of the thin nets, having different width, requires a multi-scale analysis of the image. To design the fusion process of the multi-scale information, we will study the behavior of the differential properties of the image surface, in particular the curvatures, in scale space. We illustrate the efficiency of the proposed multi-scale approach by extracting roads of different widths in satellite images.
[Show abstract][Hide abstract] ABSTRACT: We establish a theoretical link between the 3D edge detection and the local surface approximation using uncertainty. As a practical application of the theory, we present a method for computing typical curvature features from 3D medical images. We use the uncertainties inherent in edge (and surface) detection in 2- and 3-dimensional images determined by quantitatively analyzing the uncertainty in edge position, orientation and magnitude produced by the multidimensional (2-D and 3-D) versions of the Monga-Deriche-Canny recursive separable edge-detector. These uncertainties allow to compute local geometric models (quadric surface patches) of the surface, which are suitable for reliably estimating local surface characteristics, for example, Gaussian and Mean curvature. We demonstrate the effectiveness of our methods compared to previous techniques. These curvatures are then used to obtain more structured features such as curvature extrema and lines of curvature extrema. The final goal is to extract robust geometric features on which registration and/or tracking procedures can rely.
[Show abstract][Hide abstract] ABSTRACT: Principal component analysis (PCA) has been widely used in the reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many other algorithms such as EM (expectation-maximization), ANN (artificial neural network), probabilistic models, statistical analysis, etc., and has its own developments, such as MPCA (moving PCA), MS-PCA (multi-scale PCA), etc. PCA and its derivatives have a wide range of applications, from face detection, to change analysis. Change detection with PCA shows, however, a major difficulty, that is, result interpretation. A new PCA method is developed, namely MB-PCA (multi-block PCA), in order to overcome this problem. Experimental results demonstrate the interest of the approach as a new way to use PCA.
Image Analysis and Processing, 2003.Proceedings. 12th International Conference on; 10/2003
[Show abstract][Hide abstract] ABSTRACT: Conventional stereo algorithms often fail in accurately reconstructing a 3D object because the image data do not provide enough information about the geometry of the object. We propose a way to incorporate a priori information in a reconstruction process from a sequence of calibrated face images. A 3D mesh modeling the face is iteratively deformed in order to minimize an energy function. Differential information extracted from the object shape is used to generate an adaptive mesh. We also propose to explicitly incorporate a priori constraints related to the differential properties of the surface where the image information cannot yield an accurate shape recovery.
Image and Vision Computing 03/2000; · 1.96 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Classification is one of the major issue in image analysis and processing for remote sensing applications. Though classification based on texture analysis-landuse, forests, cities, etc.-is the purpose of numerous works, classification of curvilinear networks is hardly processed. However, it is of major interest, in particular for image indexing and image matching, because it is a main feature whose global shape does not change with sensors nor point of view. This paper introduces a new approach aiming at: (i) building the networks from extracted curvilinear-like features; and (ii) classifying them into roads, highways, rivers. The main idea is to use a decision tree taking into account a priori knowledge. Classification and graph building are achieved simultaneously using a hypothesis generation/propagation scheme. The resulting network is encoded as a graph with a multi-scale description. Illustrations given on satellite optical SPOT images show encouraging results
Image Analysis and Processing, 1999. Proceedings. International Conference on; 02/1999
[Show abstract][Hide abstract] ABSTRACT: We propose a way to incorporate a priori information in a 3D
stereo reconstruction process from a pair of calibrated face images. A
3D mesh modeling the surface is iteratively deformed in order to
minimize an energy function. Differential information about the object
shape is used to generate an adaptive mesh that can fulfil the compacity
and the accuracy requirements. Moreover in areas where the stereo
information is not reliable enough to accurately recover the surface
shape, because of inappropriate texture or bad lighting conditions, we
incorporate geometric constraints related to the differential properties
of the surface, that can be intuitive or refer to predefined geometric
properties of the object to be reconstructed. They can be applied to
scalar fields, such as curvature values, or structural features, such as
crest lines. Therefore, we generate a 3D face model using computer
vision techniques that is compact, accurate and consistent with the a
priori knowledge about the underlying surface
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on; 09/1998
[Show abstract][Hide abstract] ABSTRACT: This paper presents a project the purpose of which is to develop a
satellite image processing framework for automatic and precise
assessment of damage caused by flooding. The authors use optical and
synthetic aperture radar images. An application is made to Poyang Lake
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a framework for automatic extraction,
description and recognition of communication networks (road and river
networks) from satellite SPOT images. The main idea is to use a hybrid
process to progressively go from low level detection to high level
description of the networks. Results are presented on a SPOT satellite
image around Poyang Lake, in China
[Show abstract][Hide abstract] ABSTRACT: . Conventional stereo algorithms often fail in accurately reconstructing a 3D object because the image data do not provide enough information about the geometry of the object. We propose a way to incorporate a priori information in a reconstruction process from a sequence of calibrated face images. A 3D mesh modeling the face is iteratively deformed in order to minimize an energy function in a snake-like process. Differential information about the object shape is used to generate an anisotropic mesh that can both fulfill the compacity and the accuracy requirements. Moreover, in areas where the stereo information is not reliable enough to accurately recover the surface shape, because of inappropriate texture or bad lighting conditions, we propose to incorporate some geometric constraints related to the differential properties of the surface. These constraints can be intuitive or can refer to some predefined geometric properties of the object to be reconstructed. They can be applied to s...