Ana M. Herrera-Navarro

Morphological Connected Filtering on Viscous Lattices

This paper deals with the notion of connectivity in viscous lattices. In particular, a new family of morphological connected filters, called connected viscous filters is proposed. Connected viscous filters are completely determined by two criteria: size parameter and connectivity. The connection of these filters is defined on viscous lattices in such a way that they verify several properties of the traditionally known filters by reconstruction. Moreover, reconstruction algorithms used to implement filters by reconstruction can also be employed to implement these new filters. We also show that connected viscous filters have a behavior similar to filters with reconstruction criteria. The interest of these new connected filters is illustrated with different examples.

A Probabilistic Measure of Circularity

The circle is a useful morphological structure: in many situations, the importance is focused on the measuring of the similarity of a perfect circle against the object of interest. Traditionally, the well-known geometrical structures are employed as useful geometrical descriptors, but an adequate characterization and recognition are deeply affected by scenarios and physical limitations (such as resolution and noise acquisition, among others). Hence, this work proposes a new circularity measure which offers several advantages: it is not affected by the overlapping, incompleteness of borders, invariance of the resolution, or accuracy of the border detection. The propounded approach deals with the problem as a stochastic non-parametric task; the maximization of the likelihood of the evidence is used to discover the true border of the data that represent the circle. In order to validate the effectiveness of our proposal, we compared it with two recently effective measures: the mean roundness and the radius ratio.

Detection and Measurement of the Intracellular Calcium Variation in Follicular Cells

This work presents a new method for measuring the variation of intracellular calcium in follicular cells. The proposal consists in two stages: (i) the detection of the cells nuclei and (ii) the analysis of the fluorescence variations. The first stage is performed via watershed modified transformation, where the process of labeling is controlled. The detection process uses the contours of the cells as descriptors, where they are enhanced with a morphological filter that homogenizes the luminance variation of the image. In the second stage, the fluorescence variations are modeled as an exponential decreasing function, where the fluorescence variations are highly correlated with the changes of intracellular free Ca2+. Additionally, it is introduced a new morphological called medium reconstruction process, which helps to enhance the data for the modeling process. This filter exploits the undermodeling and overmodeling properties of reconstruction operators, such that it preserves the structure of the original signal. Finally, an experimental process shows evidence of the capabilities of the proposal.

A New Measure of Circularity Based on Distribution of the Radius

The measures most commonly used in current literature to compute the roundness of digital objects are derivations of the form factor based on area and perimeter computations. However, these measures are highly dependent on image resolution and sensitive to shape variations. In this article, a new measure is proposed. This measure takes into consideration the dominant geometry of objects, avoiding the use of such parameters as area, perimeter and Ferrets diameter. The proposed measure is easy to compute, and since it is a distribution of probability based on the radius, it is invariant to abrupt changes in contours or to shape resolution. In order to show the performance of this measure, it is compared with three other recently proposed measures: factor shape, which is recommended by the American Standard Test Measurement, mean roundness and radius ratio.

Size and spatial distributions characterization of graphite nodules based on connectivity by dilations

Microstructure in graphite nodules plays a fundamental role in mechanical properties in cast iron. Traditional measures used to study spheroid graphite are nodules density, nodularity, volume fraction and mean size. However, sometimes these parameters do not permit a good characterization of the microstructure since they do not allow the discrimination of different regions. In fact, other measures such as size and spatial distributions enable a better understanding of mechanical properties that can be obtained either by altering certain processing variables or through various heat treatments. In the present paper a method to characterize graphite nodules microstructure based on the connectivity generated by dilations is introduced. This approach, which takes into account size and spatial distributions of graphite, permits to relate the microstructure of graphite nodules with the wear behavior.

Multiple Kinect V2 Calibration

In this paper, we propose a method to easily calibrate multiple Kinect V2 sensors. It requires the cameras to simultaneously observe a 1D object shown at different orientations (three at least) or a 2D object for at least one acquisition. This is possible due to the built-in coordinate mapping capabilities of the Kinect. Our method follows five steps: image acquisition, pre-calibration, point cloud matching, intrinsic parameters initialization, and final calibration. We modeled radial and distortion parameters of all the cameras, obtaining a root mean square re-projection error of 0.2 pixels on the depth cameras and 0.4 pixels on the color cameras. To validate the calibration results we performed point cloud fusion with color and 3D reconstruction using the depth and color information from four Kinect sensors.

Morphological segmentation approaches of directional structures based on connections

Abstract. The multiscale morphological approaches for segmenting directional structures are proposed. First, the use of the composition of connections to extract the directional structures of the image is investigated. We show that even though the composition of connectivities enables the correct determination of the main directional structures, the requirement of the scales for segmenting the image makes this algorithm more or less complex to apply. Then, a morphological image segmentation approach is proposed based on the concept of connectivity in a viscous lattice sense. Two functions are computed to characterize the directional structures: viscosity and orientation. The viscosity function codifies the different scales of the structure and is computed from the supremum of directional erosions. This function contains the sizes of the longest lines that can be included in the structure. To determine the directions of the line segments, the orientation function is employed. By combining both images—viscosity and orientation functions— an orientation partition function is created. This last function contains information of the maxima of the viscosity function and their orientation. Finally, the elements of the orientation partition function are merged according to some criteria, using a histogram-based segmentation approach to compute an optimal partition.

Background Subtraction Model Based on Adaptable MOG

Mixture of Gaussian (MOG) approach is a powerful estimation and prediction background subtraction model. Nevertheless, although it has been improved by using several algorithms such as Expectation Maximization (EM), it is still susceptible to sudden changes in light conditions effects. In this paper, we analyze the MOG approach in order to explore its strengths and weaknesses in order to create a new robust algorithm. Our proposal consists on a new algorithm based on a dynamic selection of convergence ratio, which use the expected proportion between movement and fixed zones of scene. This proportion is used as an extra criterion to detect the maximum direction of Entropy in EM algorithm. The algorithm suits best convergence ration due to global changes in scene. Finally, in an experimental model, our approach is tested in outdoors and indoors scenarios, where luminance conditions has changed. Results show the adaptability of our approach to several dynamic scenarios.

A New Approach for Measuring the Distribution of Graphite Nodules Based on Singular Value Decomposition

Abstract Ductile cast iron is a family of ferrous metals widely used in automotive industry due to its wide range of mechanical properties, which are related to the microstructure of metal matrix and the presence of graphite particles (known as nodules). Particularly, the size and distribution of nodules in cast iron are related to wear behavior. In the present paper a new method for determining the degree of distribution of graphite nodules is presented, based on the orthogonal information of the nodules sparsest. The measure helps to characterize the resistance to the wear such that it increases when a uniform distribution is reached. 1. Introduction Due to the low manufacturing cost, mechanical properties and easy fabrication, cast irons have been utilized widely in automobile industry [1,2]. Ductile iron is characterized by having a metallic ferritic matrix that contains graphite particles (known as nodules) throughout the material [3]. Previous works have demonstrated that the factors that most affect the mechanical properties of the cast iron are mainly related with the microstructure of the matrix, the morphology the size and the distribution of the nodules [3, 4]

Connected Viscous Filters

This paper deals with the notion of connectivity in viscous lattices. In particular, a new family of morphological connected filters, called connected viscous filters is proposed. Connected viscous filters are completely determined by two criteria: size parameter and connectivity. The connection of these filters is defined on viscous lattices in such a way that they verify several properties of the traditionally known filters by reconstruction. Moreover, reconstruction algorithms used to implement filters by reconstruction can also be used to implement these new filters. The interest of these new connected filters is illustrated with different examples.