Geometric and form feature recognition tools applied to a design for assembly methodology

The paper presents geometric tools for an automated Design for Assembly (DFA) assessment system. For each component in an assembly a two step features search is performed: firstly (using the minimal bounding box) mass, dimensions and symmetries are identified allowing the part to be classified, according to DFA convention, as either rotational or prismatic; secondly form features are extracted allowing an effective method of mechanised orientation to be determined. Together these algorithms support the fuzzy decision support system, of an assembly-orientated CAD system known as FuzzyDFA

Geometric and form feature recognition tools applied to a design for assembly methodology

International audienceThe paper presents geometric tools for an automated Design for Assembly (DFA) assessment system. For each component in an assembly a two step features search is performed: firstly (using the minimal bounding box) mass, dimensions and symmetries are identified allowing the part to be classified, according to DFA convention, as either rotational or prismatic; secondly form features are extracted allowing an effective method of mechanised orientation to be determined. Together these algorithms support the fuzzy decision support system, of an assembly-orientated CAD system known as FuzzyDFA

Geometric and form feature recognition tools applied to a design for assembly methodology

International audienceThe paper presents geometric tools for an automated Design for Assembly (DFA) assessment system. For each component in an assembly a two step features search is performed: firstly (using the minimal bounding box) mass, dimensions and symmetries are identified allowing the part to be classified, according to DFA convention, as either rotational or prismatic; secondly form features are extracted allowing an effective method of mechanised orientation to be determined. Together these algorithms support the fuzzy decision support system, of an assembly-orientated CAD system known as FuzzyDFA

Point mutations in BCL6 DNA-binding domain reveal distinct roles for the six zinc fingers.

International audienceThe B-cell lymphoma 6 (BCL6) gene encodes a transcriptional repressor containing six C-terminal Krüppel-like zinc fingers. The zinc finger (ZF) cluster is necessary and sufficient for interaction with both DNA and several proteins and for nuclear targeting. However, the functional specificity of the six ZFs in these cellular roles is unknown. To characterize this domain, we mutated individually each ZF of BCL6. Our results reveal that mutation of the two N-terminal ZFs does not impair cognate DNA-binding, cellular localization of the protein nor the transcriptional repression capacity of BCL6. By contrast, mutation of any of the remaining ZFs abolishes the binding of BCL6 to DNA in vitro and the transrepressive function of the protein in vivo. Finally, none of the six mutations affect the interaction between BCL6 and class II histone deacetylases. Thus our experiments demonstrate that BCL6 uses each of the four C-terminus ZFs for binding to a target sequence while the two amino terminal fingers are likely engaged in other unknown function(s)

Applications of simulated annealing to SAR image clustering and classification problems

International audienceThis paper deals with simulated annealing applications to some unsupervised classification problems. First, we present an adaptation of the simulated annealing technique to the clustering problem, and compare its results with those provided by classical c-mean clustering algorithms, which lead only to local optimality. It is shown that simulated annealing technique yields improved results over the standard c-means method.Then, we compare the classified images obtained from two different clustering algorithms: simulated annealing, and K-means guided by initialization from optical data, applied to MAC-Europe'91 images. The study reveals a poor robustness of SAR image classification versus the estimated duster characteristics, and shows that most of culture types were best identified by the global optimization

Segmentation of elevation images based on a morphology approach for agricultural clod detection

International audienceThis study deals with the segmentation of altitude or elevation images, i.e. images of the distance (푧-coordinate) between the surface or objects and the camera plane. Specifically to our soil science application, these images are acquired on agricultural surfaces in order to evaluate their roughness. The cloddy structure being a key factor to characterize soil roughness, the elevation image analysis aims at detecting and identifying the clods as accurately as possible. Now, rather than defining a new segmentation algorithm, we propose to transform the input data so as to take into account the different criteria characterizing the clod objects, namely the relative altitude and a function of the gradient norm. The proposed approach was validated on three agricultural surfaces (two synthetic and one real) and the results compared to those of an algorithm previously developed specifically for the clod identification problem

Automatic clod detection and boundary estimation from Digital Elevation Model images using different approaches

International audienceSoil micro-topography characterization is an important issue for both soil science and remote sensing data interpretation. The objective of present study is to propose and discuss some methods dedicated to the automatic localization of clods (or big aggregates) on Digital Elevation Model images of soil. Two new image processing methods are introduced. The first one deals with the clod detection and the rough estimation of their boundaries. It is based on the adaptation of a famous segmentation algorithm applied to a modified surface enhancing the main features characterizing the clods. The second proposed method deals with the accurate estimation of clod boundaries. Clod boundaries are moved based on dynamic programming. Both proposed methods are validated on laboratory-built surfaces and on an actual surface recorded in an agricultural field. Results show that the proposed methods outperformed previously published methods. The proposed processing of DEM images allows the detection of the aggregates and clods deposited on the soil surface and the accurate estimation of their boundaries. The practice is facilitated by the proposition of default values for the parameters. The implications are the automatic analysis of DEM images that is a step towards micro-topography statistical characterization

A gradient-based approach for clod segmentation

It is acknowledged that surface micro-topography has a large impact on soil properties. Therefore a few studies have focused on surface roughness, soil height changes, and soil cloddiness characterization. The parameters estimated on soil measurements are mostly based on statistics characterizing the surface as a whole. The shortcoming of such approach is to miss some local soil height changes and non-stationarities. The present study introduces a new method to identify clods on a seedbed surface digital elevation model (DEM). It relies on the search of a specific kind of object with a priori known properties. An individual clod, immediately after tillage, is assumed to be represented by a bump, with highest gradients located on the edges, more or less near its base, depending on its shape. Given that we work with an image of heights (DEM image), then clods are recognizable as structures presenting rather medium or high grey level values, but inside clods grey level values are non homogeneous. Therefore, classical approaches of segmentation based on the search of homogeneous regions fail. Methods based on edge detection or combining both approaches in a global criterion fail as well since these edges are present not only at clod base but also inside clods. Thus, a specific algorithm was developed to search the clods on a DEM. The first step is the selection of the pixels of highest gradient values using the hypothesis that plausibly clod boundaries would go through some of these points. The second step is the hierarchy of the elevation contours. Clod boundaries are the biggest level lines, according to inclusion ordering, including only one smallest inner contour. This method of clods segmentation was assessed with the help of a soil scientist and was applied to compute clods characterizing parameters. Two kinds of tilled soil surfaces were included in this study: an artificial surface made in the laboratory to have a controlled roughness and a real seedbed surface made by tillage operations in the field. We also studied the impacts of the main parameters of the method. Indeed, two main parameters (number of selected pixels and minimum length of the elevation contour) have an influence on the identification performance and on computer time. A compromise needs to be found between a moderate computer time, a good detection rate and a low false positive rate. Furthermore, the method is also sensitive to the three dimensional height gradient computations (with first- and second-order errors) and the performance of the algorithm is assessed according to computation choice. The algorithm does not seem to be applicable to the identification of aggregates smaller than 7 mm on DEMs of 1mm sampling, which surely contain some noise measurement. The main limit of the proposed method is the failure to identify clods embedded within another piece of relief that can be a greater clod or a hollow border. Indeed, the presupposition sustaining the segmentation is that the clods have an almost horizontal basis, around which it is possible to get a closed elevation contour. Its main interest is to provide a map of clods location and to enable shape measurements in order to characterize the clods

Using simulated annealing algorithm to move clod boundaries on seedbed digital elevation model

International audienceA seedbed has a cloddy structure that is highly connected to its random roughness. Identifying and characterizing the clods is thus a preliminary step in surface roughness measurement and modelling. The aim of this paper is to propose an algorithm, based on the simulated annealing optimization, to refine the clod delineation estimated on a seedbed surface Digital Elevation Model (DEM). In our case, the DEM image was recorded on a real seedbed immediately after tillage, and we assume an initialization for the clods boundaries. The proposed method is based on a cost function to minimize, introducing four main characteristics of the clod boundary, respectively related to the mean of the DEM gradient norms on the boundary (f1), the standard deviation of the DEM gradient norms on the boundary (f2), the standard deviation of the DEM values on the boundary (f3) and L2-norm of the DEM values on the boundary (f4). In our case, the relative weights of previous criteria have been learned using a target reference that is a manual delineation of individual clods completed by a soil scientist on a sub-part of the DEM image. The cost function minimization is then achieved using the simulated annealing technics. The result performance is measured in term of the overlap rate. Further study shows the key feature of the f4 criterion. Then, the influence of the weighting coefficients was studied using based new cost function. We finally conclude on the possibility of improving the clod boundaries of a large surface using the cost function parameters learned on a training sub-surface

Investigation of salt precipitation dynamic in porous media by X-ray and Neutron dual-modality imaging

International audienceIn this work, a new dual modality monitoring technique is presented to demonstrate its interest to investigate the salt precipitation dynamics induced by gas flow-through drying. It consists of imaging simultaneously a core flood using both Neutron and X-ray beams. A method to calibrate and process the two signals is presented. It takes advantage of the difference in attenuation between the two ionizing radiations to quantify the different phase saturations and compositions as well as the reduction of porosity caused by salt precipitation. A set of experiments has been conducted at the NeXT-Grenoble beamline of the Institute Laue-Langevin facilities ( ILL , France). Experiments were conducted on a homogeneous rock sample of Bentheimer sandstone using dry nitrogen and a 100 g/L KBr brine. The two first experiments aimed to calibrate the dual modality for the different phases. The last two experiments have been conducted with a brine capillary contact maintained at the gas outlet. Experimental data have given new insights into the organization of the three phases (the brine, the gas, and the precipitated salt) when a salt bank is formed in the sample. These quantities computed using dual-modality imaging show great similarities with published work. The salt accumulation was used to estimate the flow rate of brine pumped through the capillary contact to compensate for the brine evaporation in the gas phase. Observations have shown that a reduction of the initial porosity in some sections of the sample by 12–14% was enough to trigger a gas draw-down characterized by the migration of the salt toward the gas inlet. In some conditions (low gas inlet pressure for example), the rise of the water could be fast enough to form a second salt bank higher in the sample. It has been observed that the formation of the second salt bank could spread the precipitated salt in a less damaging configuration for the gas flow, triggering a phase of gas build-up characterized by the withdrawal of the water. These phases of gas draw-down and build-up could alternate until the sample clogs