Study of Structural Characteristics of Ancient Bricks With Neutron Radiography Facility at BTRR

Neutron radiography (NR) has been applied successfully to investigate different types of building materials, rock samples, sculptures, statues or monuments for since long. The utilization of neutron imaging for non-invasive investigations of cultural heritage objects is demonstrated on the example of ancient bricks found in Mahasthangarh and Sonargaon, two key archaeological sites in Bangladesh. The visualization of the internal structure of different brick samples, by means of Neutron Radiography (NR), has been experimented using the BTRR research reactor in Bangladesh - the only neutron imaging facility available in Bangladesh for R D purposes. Manufacturing building materials have become a very good option for business in developing countries like Bangladesh. Among the non-destructive testing (NDT) techniques, neutron radiography is the most common procedure to identify light and organic materials, homogeneity, any inclusion or voids or cracks etc. inside the structure. The radiographic images in a dry condition for individual samples have been investigated. The image analysis was performed using ImageJ software and texture features were extracted using gray level co-occurrence matrix implemented by MATLAB for acquiring qualitative and quantitative information from this inspection technique at a high level of accuracy. The results obtained by neutron imaging provide the statement that the brick sample from Mahasthangarh is more homogeneous inside.

Improved Model Configuration Strategies for Kannada Handwritten Numeral Recognition

Handwritten numeral recognition has been an important area in the domain of pattern classification. The task becomes even more daunting when working with non-Roman numerals. While convolutional neural networks are the preferred choice for modeling the image data, the conception of techniques to obtain faster convergence and accurate results still poses an enigma to the researchers. In this paper, we present new methods for the initialization and the optimization of the traditional convolutional neural network architecture to obtain better results for Kannada numeral images. Specifically, we propose two different methods- an encoderdecoder setup for unsupervised training and weight initialization, and a particle swarm optimization strategy for choosing the ideal architecture configuration of the CNN. Unsupervised initial training of the architecture helps for a faster convergence owing to more task-suited weights as compared to random initialization while the optimization strategy is helpful to reduce the time required for the manual iterative approach of architecture selection. The proposed setup is trained on varying handwritten Kannada numerals. The proposed approaches are evaluated on two different datasets: a standard Dig-MNIST dataset and a custom-built dataset. Significant improvements across multiple performance metrics are observed in our proposed system over the traditional CNN training setup. The improvement in results makes a strong case for relying on such methods for faster and more accurate training and inference of digit classification, especially when working in the absence of transfer learning.

A Bayesian Approach to Morphological Models Characterization

Morphological models are commonly used to describe microstructures observed in heterogeneous materials. Usually, these models depend upon a set of parameters that must be chosen carefully to match experimental observations conducted on the microstructure. A common approach to perform the parameters determination is to try to minimize an objective function, usually taken to be the discrepancy between measurements computed on the simulations and on the experimental observations, respectively. In this article, we present a Bayesian approach for determining the parameters of morphological models, based upon the definition of a posterior distribution for the parameters. A Monte Carlo Markov Chains (MCMC) algorithm is then used to generate samples from the posterior distribution and to identify a set of optimal parameters. We show on several examples that the Bayesian approach allows us to properly identify the optimal parameters of distinct morphological models and to identify potential correlations between the parameters of the models.

Morphological Assessments of Root Apex of Permanent Mandibular First and Second Premolars in a Turkish Population

There is no apical morphological data being available for mandibular first or second premolars in the Turkish population. The aims of the study were (I) to assess apical morphological data of mandibular first and second premolars in a Turkish population at a young-adult age range (II) to analyze potential correlations between the size and position of the apical foramina (AF). Extracted sound teeth were collected from an adult volunteer population as willing to donate. Morphological data were obtained from specimens using a stereomicroscope. The number, size, shape, and position of AF and frequency of accessory foramina were quantified. Mann-Whitney U and Spearman's rank correlation tests were performed (α=0.05). A total of 237 teeth were investigated. The majority of the specimens had one major AF. The frequency of major AF was between 1–3 for both groups. The median AF size in mandibular first and second premolars were 55,180 µm2 and 67,483 µm2, respectively. The majority of foramina shape was irregular for the mandibular first premolars whereas, was oval for the second premolars. The median location of AF with respect to the anatomic apex was 664 µm in mandibular first premolars and 677 µm in mandibular second premolars. The size and location of AF mostly overlap between the mandibular first and second premolars. The shape of the AF might be the only relevant variation concerning the apical morphology between the mandibular first and second premolars in young adults. The interaction between the size and location of AF in mandibular premolars of young adults seems not significant

Estimating the Parameters of a Stochastic Geometrical Model for Multiphase Flow Images Using Local Measures

This paper presents a new method for estimating the parameters of a stochastic geometric model for multiphase flow image processing using local measures. Local measures differ from global measures in that they are only based on a small part of a binary image and consequently provide different information of certain properties such as area and perimeter. Since local measures have been shown to be helpful in estimating the typical grain elongation ratio of a homogeneous Boolean model, the objective of this study was to use these local measures to statistically infer the parameters of a more complex non-Boolean model from a sample of observations. An optimization algorithm is used to minimize a cost function based on the likelihood of a probability densityof local measurements. The performance of the model is analysed using numerical experiments and real observations. The errors relative to real images of most of the properties of the model-generated images are less than 2%. The covariance and particle size distribution are also calculated and compared.

Two-Step Method for Assessing Similarity of Random Sets

The paper concerns a new statistical method for assessing dissimilarity of two random sets based on one realisation of each of them. The method focuses on shapes of the components of the random sets, namely on the curvature of their boundaries together with the ratios of their perimeters and areas. Theoretical background is introduced and then, the method is described, justified by a simulation study and applied to real data of two different types of tissue - mammary cancer and mastopathy.

A Novel Texture Descriptor: Circular Parts Local Binary Pattern

Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been used in pattern recognition and computer vision applications. However, the LBP gets information from a restricted number of local neighbors which is not enough to describe texture information, and the other descriptors that get a large number of local neighbors suffer from a large dimensionality and consume much time. In this regard, we propose a novel descriptor for texture classification known as Circular Parts Local Binary Pattern (CPLBP) which is designed to enhance LBP by extending the area of neighborhood from one to a region of neighbors using polar coordinates that permit to capture more discriminating relationships that exists amongst the pixels in the local neighborhood which increase efficiency in extracting features. Firstly, the circle is divided into regions with a specific radius and angle. After that, we calculate the average gray-level value of each part. Finally, the value of the center pixel is compared with these average values. The relevance of the proposed idea is validate in databases Outex 10 and 12. A complete evaluation on benchmark data sets reveals CPLBP's high performance. CPLBP generates the score of 99.95 with SVM classification.

Local Measures Distribution for the Estimation of the Elongation Ratio of the Typical Grain in Homogeneous Boolean Models

We introduce a particular localization of the Minkowski functionals to characterize and discriminate different random spatial structures. The aim of this paper is to present a method estimating the typical grain elongation ratio in a homogeneous Boolean model. The use of this method is demonstrated on a range of Boolean models of rectangles featuring fixed and random elongation ratio. An optimization algorithm is performed to determine the elongation ratio which maximize the likelihood function of the probability density associated with the local perimeter measure. Therefore, the elongation ratio of the typical grain can be deduced.

SIMULATION OF LARGE AGGREGATE PARTICLES SYSTEM WITH A NEW MORPHOLOGICAL MODEL

For the development of a new porous material such as catalytic carrier, the control of the textural properties is of fundamental importance. In order to move towards rational synthesis, it is necessary to better understand the physical phenomena that generate a defined solid structure. A contribute to this purpose can be achieved by studying the aggregation process inside colloidal suspensions, leading to porosity generation: this phenomenon can be described with a Brownian dynamics model that, for any set of chemical parameters, gives access to the mass distribution and the fractal dimension of colloidal aggregates. However, this model cannot be used for the simulation of large colloidal systems, due to its high computational time, limiting comparison with analytical methods, which probe the whole multi-scale system. This problem is solved by developing a new aggregation morphological model, wherein the fractal dimension is tuned with two compactness parameters. An efficient simulation algorithm is proposed in case of spheres, for which the fractal dimension of the generated aggregates varies between 1.2 and 3. Brownian dynamics results are used to parametrize this purely geometric model, in order to constrain the size and the morphology of the aggregates created. The large numerical solid will be representative of the textural properties of a real solid and will give more information on the porous network. It could be used, for example, to simulate diffusive transport coupled with chemical reaction and to study the impact of the geometry of the porous system on the catalytic performance.

Personal Reflections on the Life of Hans Jørgen Gottlieb Gundersen

Professor Hans Jørgen G. Gundersen MD, DMSc (1943–2021) was a pioneering stereologist whose work has inspired and influenced researchers across the world for almost half a century. He was a charismatic character and one of the founding fathers of modern stereology, whose achievements and contributions are fondly remembered below by colleagues and co-workers. It was an enormous pleasure to be in his company and although future generation will miss this opportunity, his work will live on, to inspire and influence future generations of researchers.