Fractal analysis is one of the rapidly evolving branches of mathematics and finds its application in different analyses such as pore space description. It constitutes a new approach to the issue of their natural irregularity and roughness. To be properly applied, it should be encompassed by an error estimation. The article presents and verifies uncertainties along with imperfections connected with image analysis and expands on the possible ways of their correction. One of key aspects of such research is finding both appropriate place and the number of photos to take. A coarse- grained sandstone thin section was photographed and then pictures were combined into one, bigger image. Fractal parameters distributions show their change and suggest that the accurately gathered group of photos include both highly and less porous regions. Their amount should be representative and adequate to the sample. The resolution influence on the fractal dimension and lacunarity values was examined. For SEM limestone images obtained using backscattered electrons, magnification in the range of 120x to 2000x was used. Additionally, a single pore was examined. The acquired results point to the fact that the values of fractal dimension are similar to a wide range of magnifications, while lacunarity changes each time. This is connected with changing homogeneity of the image. The article also undertakes a problem of determining fractal parameters spatial distribution based on binarization. The available methods assume that it is carried out after or before the image division into rectangles to create fractal dimension and lacunarity values for interpolation. An individual binarization, although time consuming, provides better results that resemble reality to a closer degree. It is not possible to define a single, correct methodology of error elimination. A set of hints has been presented that can improve results of further image analysis of pore space.
The flow structure around rising single air bubbles in water and their characteristics, such as equivalent diameter, rising velocity and shape, was investigated using Particle Image Velocimetry (PIV) and Shadowgraphy in a transparent apparatus with a volume of 120 mL. The effect of different volumetric gas flow rates, ranging from 4 μL/min to 2 mL/min on the liquid velocity was studied. Ellipsoidal bubbleswere observedwith a rising velocity of 0.25–0.29m/s. It was found that a Kármán vortex street existed behind the rising bubbles. Furthermore, the wake region expanded with increasing volumetric gas flow rate as well as the number and size of the vortices.
An automated method for crack identification and quantitative description of crack systems in concrete was developed in order to aid a service life assessment of concrete elements in structures. Flat polished specimens for crack analysis were impregnated with epoxy resin containing fluorescent dye. The examination of the crack system was performed in ultraviolet light using a stereomicroscope and an Image Pro Plus image analysis system on specimens cored out of several concrete structures. The laboratory tests were performed on cast specimens to establish correlations between water penetration and chloride diffusion and crack system parameters. The analysis of cracks in concrete cores taken from structures resulted in interesting conclusions based on the crack width distribution and crack localization with respect to steel reinforcement. The method was found very effective to support standard concrete diagnostics methods.
The article proposes a method for measuring discomfort glare which uses numerical description of the phenomenon in the form of a digital luminance distribution map recorded on a CCD array. Essential procedures for determining partial quantities which are necessary for calculation of UGR index are discussed in detail, along with techniques for measuring position index and size of light sources, with regard to the parameters of the registering system and coordinates of the images of the sources on the array.
The paper presents test results for the assessment of the tracer content in a three-component (green peas, sorghum, maize) feed mixture that is based on the fluorescent method. The homogeneity of mixtures was determined on the basis of the maize content (as the key component), which was treated with fluorescent substance: tinopal, rhodamine B, uranine and eosin. The key components were wet-treated with fluorescent substances with different concentrations. Feed components were mixed in a vertical funnel-flow mixer. 10 samples were collected from each mixed batch. Samples were placed in a chamber equipped with UV light and, then, an image recorded as BMP file was generated. The image was analysed by means of the software programme Patan. On the basis of the analyses conducted, data on the maize content marked with a fluorescent marker were obtained. Additionally, the content of the key component was determined in a conventional manner – using an analytical scale. Results indicate the possibility of using this method for homogeneity assessment of the three-component grain mixture. From these tests, fluorescent substances that can be applied in the case of maize as a key component, together with their minimum concentrations, were identified: tinopal 0.3%, rhodamine B 0.001%.
Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.
Image analysis allows to acquire a number of valuable quantitative informations on the observed structure and make appropriate conclusions. So far, a large part of analyzed images came only from light microscopes, where it was a possibility of accurately distinguish the different phases on the plane. However, the problem happened in the case of the observation of images obtained by scanning electron microscopy. In this case, the presence of various shades of gray, and the spaciousness of the image attained. To perform the analysis the matrix images of the ausferritic ductile iron were used. Full analysis was carried out using the computer program MicroMeter 1.03. Results obtained in the analysis were related directly to the results from X-ray diffraction. Obtained as a result of the analysis were related directly to the results from X-ray diffractometer. The following technique has weaknesses, including the misinterpretation by the operator microscope or program. After all, it was possible to obtain similar results to the result that has been obtained from X-ray diffractometer.
This article provides a comparison of a three methods that can be used for calculating effective coverage of image quality assessment database. The aim of this metric is to show how well the database is filled with variety of images. For each image in the database the Spatial Information (SI) and Colorfulness (CF) metric is calculated. The area of convex hull containing all the points on SI x CF plane is indication of total coverage of the database, but it does not show how efficiently this area is utilized. For this purpose an effective coverage was introduced. An analysis is performed for 16 databases - 13 publicaly available and 3 artificial created for the purpose of showing advantages of the effective coverage.
Monitoring of activated sludge flocs may provide important information for effective operation and control of wastewater treatment. The research objective is to demonstrate methodology for activated sludge image processing aimed to describe morphological characteristics of activated sludge flocs. The proposed software- -based method was presented and verified by analysis of several activated sludge samples. The results show high efficiency of image segmentation and floc recognition of more than 94% floc components. The analysis of a series of 50 pictures gives rapid and reliable results and can be performed in an automatic or semiautomatic mode. Given inherent heterogeneity of activated sludge flocs, multiple and repeated sample images capture (processing of 50 pictures at a time, repeated at least 4 times ) is recommended.
Analysis of the shape and location of abrasive grain tips as well as their changes during the grinding process, is the basis for forecasting the machining process results. This paper presents a methodology of using the watershed segmentation in identifying abrasive grains on the abrasive tool active surface. Some abrasive grain tips were selected to minimize the errors of detecting many tips on a single abrasive grain. The abrasive grains, singled out as a result of the watershed segmentation, were then analyzed to determine their geometric parameters. Moreover, the statistical parameters describing their locations on the abrasive tool active surface and the parameters characterizing intergranular spaces were determined.
This paper proposes a new approach to the processing and analysis of medical images. We introduce the term and methodology of medical data understanding, as a new step in the way of starting from image processing, and followed by analysis and classification (recognition). The general view of the situation of the new technology of machine perception and image understanding in the context of the more well known and classic techniques of image processing, analysis, segmentation and classification is shown below
The paper discusses possible applications of the percolation theory in analysis of the microstructure images of polycrystalline materials. Until now, practical use of this theory in metallographic studies has been an almost unprecedented practice. Observation of structures so intricate with the help of this tool is far from the current field of its application. Due to the complexity of the problem itself, modern computer programmes related with the image processing and analysis have been used. To enable practical implementation of the task previously established, an original software has been created. Based on cluster analysis, it is used for the determination of percolation phenomena in the examined materials. For comparative testing, two two-phase materials composed of phases of the same type (ADI matrix and duplex stainless steel) were chosen. Both materials have an austenitic - ferritic structure. The result of metallographic image analysis using a proprietary PERKOLACJA.EXE computer programme was the determination of the content of individual phases within the examined area and of the number of clusters formed by these phases. The outcome of the study is statistical information, which explains and helps in better understanding of the planar images and real spatial arrangement of the examined material structure. The results obtained are expected to assist future determination of the effect that the internal structure of two-phase materials may have on a relationship between the spatial structure and mechanical properties.
In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.
Particle Image Velocimetry is getting more and more often the method of choice not only for visualization of turbulent mass flows in fluid mechanics, but also in linear and non-linear acoustics for non-intrusive visualization of acoustic particle velocity. Particle Image Velocimetry with low sampling rate (about 15Hz) can be applied to visualize the acoustic field using the acquisition synchronized to the excitation signal. Such phase-locked PIV technique is described and used in experiments presented in the paper. The main goal of research was to propose a model of PIV systematic error due to non-zero time interval between acquisitions of two images of the examined sound field seeded with tracer particles, what affects the measurement of complex acoustic signals. Usefulness of the presented model is confirmed experimentally. The correction procedure, based on the proposed model, applied to measurement data increases the accuracy of acoustic particle velocity field visualization and creates new possibilities in observation of sound fields excited with multi-tonal or band-limited noise signals.
The paper provides statistical analysis of the photographs of four various granular materials (peas, pellets, triticale, wood chips). For analysis, the (parametric) ANOVA and the (nonparametric) Kruskal-Wallis tests were applied. Additionally, the (parametric) two-sample t-test and (non-parametric) Wilcoxon Rank-Sum Test for pairwise comparisons were performed. In each case, the Bonferroni correction was used. The analysis shows a statistical evidence of the presence of differences between the respective average discrete pixel intensity distributions (PID), induced by the histograms in each group of photos, which cannot be explained only by the existing differences among single granules of different materials. The proposed approach may contribute to the development of a fast inspection method for comparison and discrimination of granular materials differing from the reference material, in the production process.