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Spray deposit and superficial coverage play key roles in phytosanitary treatments. Their measurement is a quite complex task that requires adding suitable tracers to the mixture (deposit) and using some artificial targets (superficial coverage). With these laboratory tests, unit deposit values, measured in Petri dishes containing silicon oil, were correlated with percentage of covered surface, measured with water sensitive papers (WSPs) after spraying a water solution with red Ponceau at 2% concentration. An Albuz ATR 80 hollow cone nozzle was used at the spraying pressures of 0.3, 0.5, 1.0 and 1.5 MPa. The results showed a significant correlation between unit deposit d u (μL cm−2) and the fraction of covered surface x S ( d u = 2.2095 ⋅ x S − 0.3473 , R2 = 0.761, p-level

Fracturing two-dimensional random fiber networks of different densities (porosities) were statistically analyzed using both high-resolution finite element models and image analysis algorithms. Under small strains, the finite element fracture models revealed that networks with high relative densities were able to localize evolving fractures to small cracks while surprisingly larger cracks were required to localize fractures in networks of lower density. Further, it is indicated that the pore size distribution in fiber networks is rather diverse and can be captured using two mixed Gamma distributions; one part describing the background size distribution containing the vast majority of pores, and a second part describing the size distribution of larger pores and open regions. The second part covers less than 5% of the total network area but seems to be of paramount importance for the network’s global fracture behavior. It seems as a fiber network’s crack sensitivity is related to the average pore size in the second part of the mixed Gamma distribution. The analysis is remarkably consistent with reported experiments.

Biomass is a suitable energy source to reduce the carbon footprint and increase the use of renewable energy. However, the biomass exploitation is still slowed by many technical issues. In most practical applications, such as gasification or combustion devices, it is important to predict the fuel physical behavior in order to determine the emissions and heat release profile as well as for modeling and design purposes. Within this paper, the study of the dimensional evolution of a biomass fuel (beech wood) in pyrolysis and combustion processes were carried out with the use of the image analysis tool. Sizes from 15 mm to 300 mm characteristic length range were employed in the experiments and the collected data were related to the mass loss and temperature evolution of the biomass particle. It was found that for all the fuel sizes employed a similar volume reduction (60%–66%) was obtained at the end of pyrolysis. However, for the small particles with minor intra-particle gradients shrinkage took place mainly at the end of conversion, while for bigger particles the size variation patter was more linear. Furthermore, swelling was detected in the pyrolysis experiments, and it was higher for a bigger particle size, while cracking and fragmentation phenomena was observed for large wood logs combustion in the stove.

An electronic eye (EE) for fast and easy evaluation of grape phenolic ripening has been developed. For this purpose, berries of different grape varieties were collected at different harvest times from veraison to maturity, then an amount of the derived must was deposited on a white sheet of absorbent paper to obtain a sort of paper chromatography. Thus, RGB images of the must spots were collected using a flatbed scanner and converted into one-dimensional signals, named colourgrams, which codify the colour properties of the images. The dataset of colourgrams was used to build calibration models to relate the colour of the images with the phenolic composition of the samples – determined by reference analytical methods – and therefore to follow the ripening trend. Satisfactory calibration models were obtained for the prediction of the most important parameters related to phenolic ripening of grapes, such as colour index, tonality, total anthocyanins content, malvidin-3-O-glucoside and petunidin-3-O-glucoside.

The Long-Term Ecological Research (LTER) observatory HAUSGARTEN, in the eastern Fram Strait, provides us the valuable ability to study the composition of benthic megafaunal communities through the analysis of seafloor photographs. This, in combination with extensive sampling campaigns, which have yielded a unique data set on faunal, bacterial, biogeochemical and geological properties, as well as on hydrography and sedimentation patterns, allows us to address the question of why variations in megafaunal community structure and species distribution exist within regional (60–110km) and local (

This paper proposes ontology based conceptual framework for storage and retrieval of Digitized Museum Artifacts. The proposed framework uses ontology structure for automatic image annotation. It supports semantic retrieval by combining ontological concepts, visual and textual features automatically extracted from images and their textual descriptions. The Ontology-driven analysis module automatically generates annotation for domain objects. This paper also reports a new dataset designed for its evaluation. The dataset consists of images displayed in various galleries of Allahabad museum along with their textual description. We have collected 1200 images and extracted their visual and textual features for the purpose of retrieval.

[ES] Los invariantes generados a partir de momentos extráıdos de una imagen aparecen recurrentemente en la bibliograf́ıa como uno de los métodos más potentes para la descripción de imágenes, y más concretamente de formas. En este art́ıculo se propone el uso de funciones de base continuas a intervalos (PCBF) como alternativa a las bases que se vienen utilizando tradicionalmente en la aplicación de este método, todas ellas continas como puedan ser las muy conocidas de Zernike, Legendre o Tchebichev. El uso de funciones discontinuas se justifica en la naturaleza discontinua de los objetos analizados, en este caso las imágenes: es de sobra conocido que los contornos de los objetos visibles en ellas se caracterizan como discontinuidades en la serie de valores de luminancia cuando nos desplazamos de un lado a otro de dichos contornos. El análisis de tales señales con funciones continuas produce resultados no deseados, como el fenómeno de Gibbs, que pueden ser evitados mediante el uso de funciones como las propuestas, generando mejores aproximaciones a la forma analizada. Adicionalmente, las bases propuestas permiten fácilmente, como se demuestra, la generación de invariantes a rotación, caracteŕıstica altamente deseable para un descriptor de forma, puesto que a priori no se conoce con qué orientación aparecerá esta en la imagen objeto del análisis. La invarianza a traslación y escala se consigue mediante un sencillo proceso de normalización. Se presentan los test que confirman esta hipótesis, comenzando por un análisis del comportamiento de los invariantes ante el ruido en la imagen que permitirá determinar en qué número deben ser extráıdos. A continuación, y una vez definida esta longitud de descripción, se realizan sendos experimentos para determinar el comportamiento de los invariantes propuestos en una tarea de recuperación de imágenes, tanto libres de ruido como corrompidas con distintos grados de ruido gaussiano. Los resultados avalan la hipótesis de idoneidad para la tarea, demostrando que se pueden alcanzar resultados similares a los de la base de referencia, Zernike, utilizando descripciones hasta un 40% más cortas., [EN] Invariants generated departing from moments, previously extracted from an image, appear frequently in the bibliography as one of the most powerful means of describing images, and more precisely shapes. In this paper, the use of Piecewise Continuous Basis Functions (PCBF) is proposed as an alternative to those basis which have been used traditionally in the method of moments, all of them continuous as the well known Zernike, Legendre or Tchebichev basis. The use of discontinuous basis can be justified by the own discontinuous nature of the object of such analysis, namely images: it is thoroughly known that the contours of visible objects are modeled as discontinuities in the series of luminance values as we go from one side of the border to the other. Analyzing such discontinuous objects by means of continuous functions can lead to undesired results, as the Gibbs phenomenon, that can be avoided by simply shifting to discontinuous basis for the analysis, getting better approximations to the described object. Additionally, the proposed basis can easily generate, as shown in this paper, rotation invariants, which is a very desirable feature for a shape descriptor, given that the orientation that the shape will have in an image is not known in advance. Translation and scale invariance is obtained by means of a simple normalization process. Test confirming this hypothesis are presented as well, starting with an analysis of the behavior of the proposed invariantes in noisy environments, which allow to fix the number of invariants that have to be extracted. Next, once this description length has been determined, new experiments are carried out to assess the performance of the proposed invariants in a content based retrieval task, both in a noise free and in noisy environments, having images corrupted with different gaussian noise intensities. Results confirm our hypothesis that these descriptors are very well suited for this task, showing that they can achieve results similar to those obtained using the continuous reference basis, which is Zernike's, but with a description which is roughly a 40% shorter.

International audience; Dry coating is an interesting process for creating added value binary powders composites. The principle is to mix `host' particles with `guest' particles with 10 to 100 times smaller size. But, behind the apparent simplicity of the process, the implied mechanisms are very complex. As a result, it is not possible so far to predict the resulting end‐use properties of the produced composite powder, even if we know the properties of the host and guest powders. This papers aims at illustrating a first step to characterize more precisely the process itself and the result of the coating, i.e. coverage, homogeneity or thickness. In this work we describe the methodology to characterize the dry coating coverage through image analysis by using Environmental Scanning Electron Microscopy. A first part of this paper gives the technical and statistical aspects of the image acquisition in the electron microscope (Philips XL 30). A second part develops the image processing methodology with Matlab Image Processing Toolbox. A third part describes a validation step of the methodology by comparing the automated procedure to a `manual' derivation of the coverage. The presented results imply the coverage of `model' powders: host particles of ca. 100 mu m and guest particles of ca. 10 mu m. This work illustrates the assets of the image analysis to quantitatively characterize the average coverage and its homogeneity in dry coating application, for example when partial breakage or attrition occurs. Further results with the application of this methodology will be published in a further paper.

Image-based data mining (IBDM) and voxel-based analysis (VBA) have shown great promise in the retrospective analysis of routine clinical data, offering a way to analyze a patient population without selection. In radiation therapy, IBDM analyzes whole dose distributions for their effect on a given outcome with no prior assumptions. Recent extensions in IBDM methodologies include per voxel survival analyses, allowing the generation of more robust and testable hypotheses. An essential aspect to produce solid conclusions includes applying the correct statistical techniques to account for multiple testing. There is a growing interest in applying IBDM/VBA techniques in radiation therapy, which is a relatively new area with limited examples in the literature. We present this editorial to discuss guidelines for best practice. In doing so, we highlight 3 recently published papers applying IBDM techniques to radiation therapy data 1 , 2 , 3 and suggest alternative, more robust analysis methods.

In developmental biology, knowledge of cell structure and their (morpho) dynamic behavior, leads to a comprehensive understanding of their conducts and the mechanisms in which they participate. This knowledge is a decisive factor in biological research and also in all drug development steps, medicinal or preventive therapies. Experimental cell analysis is hard, expensive, and time-consuming. To overcome these difficulties, in recent years, several computational object tracking methods, software system and packages have been developed in cell sciences that bring together different disciplines and branches of technologies. Object tracking is the process of locating and monitoring specific object and its behavior in sequential images. In this paper, a comprehensive review on object tracking stages and computational methods that are utilized in terms of cell tracking has been organized. Besides, the available software packages and toolkits, challenges, and their solution in time lapse microscopy images in this scope were reviewed. The aim of describing computational cell tracking methods and tools is that biologist and cell scientists might take advantage of these computational techniques to effort another method to gain complementary information for their question of interest.

Public concerns and official pressures around environmental protection as well as exhausting petroleum resources have brought about preferences in studying and applying environmental-friendly polymers instead of synthetic petroleum-based polymers. For this aim, biopolymers can have outstanding advantages in biodegradability by saving time, energy and effort spent on increasingly costs of polymeric wastes. Natural starch (being studied in this work), as one of the most abundant natural resources for polymer materials, is inexpensive and biodegradable. As starch necessarily needs for modifications and processing before being used as ideal green polymer material, it is vital to perform feature extraction and defect detection measures in structure by some method like image analysis. Fractal as a new geometry has circumstantially progressed recently in the fields of image processing, physical space–time, medical image analysis, electrochemical patterns, digital images, sounds etc. Box Count Fractal Dimension as a very important and popular part of fractal geometry can be a useful factor in feature extraction and pattern recognition. This paper presents a new method for defect detection in the structure of natural starch modification images using the fractal dimension (FD) along with mean and standard deviation of image color. This is performed via feature extraction based on artificial intelligence by MatLab R2013a for box counting algorithms. Otsu’s graythresh method by MatLab R2013a is applied to binarize the images. The results of proposed methodology are illustrated as log–log curves where the fractal dimensions are recognized by curve fitting (CF) tool with more than 95% accuracy. The outputs express that starch samples’ FD vary in the range from 1.636 to 1.926 among which the last is identified as non-defective polymer. Non-defective feature is of great importance for quality control measures and chemical reactivity being here highlighted as biodegradability. Keywords: Image analysis, Natural starch, Fractal dimension FD, Box counting, Pattern recognition, Computer program

A possible way to increase the energy density in lithium-ion batteries, and, at the same time, reduce the production costs, is to use thicker electrodes. However, transport limitations can occur in thick electrodes, leading to a drawback in performance. A way to mitigate this problem is a more sophisticated microstructure of the electrode, using, e.g., structural gradients. This can, for instance, be achieved by multi-layer casting, i.e., casting and drying of a first layer, and then adding a second layer. An important question is how the interface between the two layers is shaped and how the corresponding microstructure influences the electrochemical performance. In the present paper, two different two-layer cathodes are analyzed and compared to single-layer cathodes of the same thickness. The analysis involved tomographic imaging, a statistical analysis of the 3D microstructure of the active material particle systems with a focus on the interface between the layers, and electrochemical characterization of the active material systems using experimental measurements as well as electrochemical simulations. The analysis showed that at the interface the connectivity of active material particles decreases, which results in higher electric resistivity. This effect is stronger if an intermediate calendering step is performed, i.e., the first layer is calendered before casting the second layer.

Field-based high throughput plant phenotyping has recently gained increased interest in the efforts to bridge the genotyping and phenotyping gap and accelerate plant breeding for crop improvement. In this paper, we introduce a large-scale, integrated robotic cable-driven sensing system developed at University of Nebraska for field phenotyping research. It is constructed to collect data from a 0.4ha field. The system has a sensor payload of 30kg and offers the flexibility to integrate user defined sensing modules. Currently it integrates a four-band multispectral camera, a thermal infrared camera, a 3D scanning LiDAR, and a portable visible near-infrared spectrometer for plant measurements. Software is designed and developed for instrument control, task planning, and motion control, which enables precise and flexible phenotypic data collection at the plot level. The system also includes a variable-rate subsurface drip irrigation to control water application rates, and an automated weather station to log environmental variables. The system has been in operation for the 2017 and 2018 growing seasons. We demonstrate that the system is reliable and robust, and that fully automated data collection is feasible. Sensor and image data are of high quality in comparison to the ground truth measurements, and capture various aspects of plant traits such as height, ground cover and spectral reflectance. We present two novel datasets enabled by the system, including a plot-level thermal infrared image time-series during a day, and the signal of solar induced chlorophyll fluorescence from canopy reflectance. It is anticipated that the availability of this automated phenotyping system will benefit research in field phenotyping, remote sensing, agronomy, and related disciplines., Computers and Electronics in Agriculture, 160, ISSN:0168-1699, ISSN:1872-7107

Frozen vegetables are often exposed to dynamic temperature conditions during cold storage and distribution chain. The resulting ice recrystallization leads to microstructural changes, which is directly linked to the final vegetable quality. To this end, X-ray µCT was applied to visualize and quantify 3D ice crystal changes in carrot over a period of two months of frozen storage with dynamically changing temperature. The studied conditions revealed a significant increase in ice crystal size during the storage period. The mean equivalent diameter of the ice crystals increased from 246 ± 15.9 µm, to 342 ± 13.2 µm, 394 ± 18.5 µm, 525 ± 28.0 µm and 578 ± 27.6 µm at 0 d, 7 d, 14 d, 30 d and 60 d of storage, respectively, while the number of ice crystals decreased. The 3D data on the ice crystals and image analysis presented within this paper provide an insight making it possible to describe microstructure evolution, and for better control cold storage sector of frozen vegetables. ispartof: Journal Of Food Engineering vol:246 pages:232-241 status: published

The modeling of the perceptual properties of texture plays a fundamental role in tasks where some interaction with subjects is needed. In order to face the imprecision related to these properties, fuzzy sets defined on the domain of computational measures of the corresponding property are usually employed. In this sense, the most interesting approaches show that the combination of different measures as reference sets improve the texture characterization. However, the main drawback of these proposals is that they do not take into account the subjectivity associated with human perception. For example, the perception of a texture property may change depending on the user, and in addition, the image context may influence the global perception of a given property. In this paper, we propose to solve these problems by combining the use of several computational measures in a reference set with adaptation to the subjectivity of human perception. To do this, we propose a generic methodology that automatically transforms any multidimensional fuzzy set modeling a texture property to the particular perception of a new user or to the image context. For this purpose, the information given by the user, or extracted from the textures present in the image, are employed.

Liquid leakage from pipelines is a critical issue in large-scale process plants. Damage in pipelines affects the normal operation of the plant and increases maintenance costs. Furthermore, it causes unsafe and hazardous situations for operators. Therefore, the detection and localization of leakages is a crucial task for maintenance and condition monitoring. Recently, the use of infrared (IR) cameras was found to be a promising approach for leakage detection in large-scale plants. IR cameras can capture leaking liquid if it has a higher (or lower) temperature than its surroundings. In this paper, a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant. Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid, it is applicable for any type of liquid leakage (i.e., water, oil, etc.). In this method, subsequent frames are subtracted and divided into blocks. Then, principle component analysis is performed in each block to extract features from the blocks. All subtracted frames within the blocks are individually transferred to feature vectors, which are used as a basis for classifying the blocks. The k-nearest neighbor algorithm is used to classify the blocks as normal (without leakage) or anomalous (with leakage). Finally, the positions of the leakages are determined in each anomalous block. In order to evaluate the approach, two datasets with two different formats, consisting of video footage of a laboratory demonstrator plant captured by an IR camera, are considered. The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos. The proposed method has high accuracy and a reasonable detection time for leakage detection. The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.

LC3 is one of the most promising alternatives for a high-performance blended cement with a significantly lower clinker content and with potential for worldwide adoption. For this reason, understanding its long-term performance is relevant to ensure serviceability over its lifespan. In this paper, a detailed study of phase assemblage, porosity evolution and microstructural development was conducted on LC3 samples hydrated up to 3 years and compared to 90 days specimens. It was observed that the reaction of clinker phases and metakaolin continues between 90 days and 3 years, even after depletion of portlandite. Stra center dot tlingite was observed in systems with higher metakaolin contents, in addition to hemi and monocarboaluminate. Experimental results were compared with thermodynamical modelling. New insights are obtained on the hydration mechanisms operating at later ages, when only small pores remain filled with pore solution.

Thermophoretic sampling and TEM imaging are common techniques used to characterise soot particles in flames. In this paper, we present a multi-scale evaluation of operating conditions and methodological aspects of these techniques, and based on our own experimental observations, show how these can influence the characterisation of the particles. Regarding the thermophoretic sampling of soot particles in flames, we evaluated the influence of exposure time, transit times, multiple-insertions, probe design and vibrations in the capture of representative samples, and present a series of recommendations. For the nano-structural characterisation of soot particles using HRTEM combined with fringe analysis we evaluated the influence of microscope alignment and image quality in the mapping of fringes and the calculation of metrics, concluding that the fringe lengths and inter-fringe spacing are very sensitive to particle focus. Also, the parameters used in the image transformation process are critical and require optimisation for different magnifications and microscopes. Finally, the effect of beam damage was studied, confirming a time of approximately 6 min during which both nascent and mature particles can be imaged without noticeable nano-structural damage. The use of lower microscope electron voltage can further minimise the impact of beam damage. National Research Foundation (NRF) The authors gratefully acknowledge the financial support provided by the Colombia Scientific Program within the framework of the call Ecosistema Cientifico (Contract No. FP44842-218-2018), and the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Dr Botero thank COLCIENCIAS for the post-doctorate grant 784.

The soil layer is a common issue for energy systems as solar panels or prime mover and driven turbomachines. This phenomenon is usually known as fouling, and it consists of a microscopic layer (usually lower than 1 mm) that wraps the surface of these machines and devices. The presence of a deposited layer determines the modification of the surface characteristics as transparency or roughness with the consequent performance and efficiency reduction. In the present paper, a detailed investigation is carried out on seven measurement methods used for estimating the sand layer morphology and thickness. Each method has been applied for measuring similar soil layers, giving the possibility to compare the methodologies and show the capability of each method to obtain different information from the same specimen. Several guidelines and suggestions about technology, cost, and usefulness of each method are also included.

In this paper we deal with the experimental evaluation of tampered image detection algorithms. These algorithms aim at establishing if any manipulation has been carried out on a digital image. In detail, we focus on the evaluation of the CASIA Tampered Image Detection Evaluation (CASIA TIDE) public dataset of images, the de facto standard for evaluating these class of algorithms. Our analysis has been performed using the algorithm of Lin et al. for JPEG tampered image detection as benchmark. The results proved that the images of the dataset contain some statistical artifacts that may help the detection process. To confirm this, we first used this dataset to evaluate the performance of the Lin et al. algorithm. According to our results, the considered algorithm performs very well on this dataset. Some variants of the original algorithm have been developed expressly tuned on these artifacts. These variants performed better than their original counterpart. Then a new unbiased dataset has been assembled and a new set of experiments has been executed with these images. The results showed that the performance of the algorithm and its variants radically decreased, proving that the CASIA TIDE statistical artifacts cause interferences on the detection process. This problem is particularly important in the biometric field, because many image-based biometric systems rely on the assumption that input images have not been manipulated. Indeed, a faithful experimental evaluation must be based on unbiased input dataset to get well founded results. Therefore, the selection of a reliable image tampering detection algorithm is crucial. A preliminary version of this work has been presented in Cattaneo and Roscigno (2014) [6].

The present paper reports the first dynamic image analysis-based feedback control of continuous twin-screw wet granulation process. Granulation of the blend of lactose and starch was selected as a model process. The size and size distribution of the obtained particles were successfully monitored by a process camera coupled with an image analysis software developed by the authors. The validation of the developed system showed that the particle size analysis tool can determine the size of the granules with an error of less than 5 µm. The next step was to implement real-time feedback control of the process by controlling the liquid feeding rate of the pump through a PC, based on the real-time determined particle size results. After the establishment of the feedback control, the system could correct different real-life disturbances, creating a Process Analytically Controlled Technology (PACT), which guarantees the real-time monitoring and controlling of the quality of the granules. In the event of changes or bad tendencies in the particle size, the system can automatically compensate the effect of disturbances, ensuring proper product quality. This kind of quality assurance approach is especially important in the case of continuous pharmaceutical technologies.

Background and Objective: Visual expression of invasive breast cancer with immunohistochemistry (IHC) allows evaluation of CerbB2 receptors, such that CerbB2 mutated breast carcinomas are suitable for targeted therapy. Breast tumors are evaluated in four different scores as 0, 1, 2, 3 to decide if it is suitable for the CerbB2 protein specific treatment or not. Pathologists try to decide the scores by eye, which is laborious, and error-prone work with high inter-observer variability. Methods: We propose cell based image analysis termine the CerbB2/HER2 scores in breast tissue images in accordance with ASCO/CAP recommendations, automatically. The proposed ASCO/CAP recommendations compliant image analysis approach provides an explainable artificial intelligence solution for HER2 tissue scoring. Firstly, tissue images are separated into hematoxylin and diaminobenzidine color channels with color deconvolution. Cell nuclei and boundaries are segmented with a hybrid multi-level thresholding and radial line based method on hematoxylin channel. Following ASCO/CAP recommendations, cell based features representing the intensity and completeness of circumferential membrane staining are extracted with the proposed Membrane Intensity Histogram (MIH) method. Extracted features are, then, fed into a classifier, such as, k-nearest neighbours, decision trees and long-short term memory, to determine cell based HER2 scores. Individual cell scores are combined according to ASCO/CAP recommendations to obtain the final CerbB2/HER2 tissue score. Another contribution of the paper is the introduction of two publicly available image data sets on CerbB2/HER2 tissue scoring. Clinical data sets, ITU-MED-1 and ITU-MED-2, are created by digitizing IHC slides from real patients, that have ground truth CerbB2/HER2 scores. Result: The proposed automatic scoring method is tested on these clinical data sets, as well as, on a HER2 Contest data set. Performance of the proposed explainable artificial intelligence approach for HER2 tissue scoring is evaluated and compared with state-of-the-art techniques in the literature. Conclusion: Results suggest that, the proposed method is highly effective in HER2 tissue scoring on both balanced and unbalanced data sets. Significance: A hand-crafted feature extraction approach for CerbB2/HER2 scoring is proposed which provides an explainable artificial intelligence framework. The proposed HER2 scoring method can be adapted to updates in ASCO/CAP recommendations without the need for re-training and/or re-designing the model. Moreover, two publicly available data sets, namely, ITU-MED-1 and ITU-MED-2 are introduced with corresponding score labels.

This work applies reconstruction methods based on a genetic algorithm to derive 3D material properties, namely porosity and percolation fraction, in irradiated U-Pu-Zr fuel with minor actinides. We provide two-dimensional experimental data regarding the radial distribution of fission gas bubbles in the fuel and apply the algorithm successfully developed in a companion paper to reconstruct the fuel pore structure in 3D which is unknown a priori. The algorithm returned a set of best structures that constituted the best candidate solutions representing the pore phase. From these, it was possible to extract statistics on the 3D percolation fraction of the reference medium and infer a mean value, the related uncertainty, and an upper and lower bound of the percolation fraction. The algorithm proved able to infer this 3D property from 2D information of the metallic fuel with confidence intervals, thus establishing a path to infer 3D properties directly from 2D experimental images. The knowledge of such a relationship can be used to extrapolate the percolation threshold with confidence interval, which is a crucial property in defining microstructure-based fission gas release models of metallic fuels.

A prolonged and exceptionally intense air mass advection event transporting biomass burning aerosols generated in Alaska affected Ny-Ålesund in the mid of July 2015. This paper reports the morphochemical characteristics and mixing state of individual aerosol particles collected during the event. To this aim aerosol samples were collected on nucleopore polycarbonate membrane filters using a DEKATI 12-stage low volume impactor and analyzed by scanning electron microscopy (SEM) techniques. Results of SEM investigations depict a complex aerosol characterized by an external mixing between a main part of carbonaceous organic particles (tar balls and organic particles), lower ammonium sulfate and minor potassium chloride and mineral dust amounts. The carbonaceous particles are spherical to slightly elongated and the organic particles show an internal mixing of low density organics and/or ammonium sulfate upon denser nuclei. Most particles are in the accumulation mode size range although the size and the morphology of the chloride and the sulfate salts evidence the growth of these species both in the air and upon the sampling membranes. Individual particle analyses were complemented by aerosol size distribution (Aerodynamic Particle Sizer, Scanning Mobility Particle Sizer) and optical (Particle Soot Absorption Photometer, nephelometer) measurements at ground level in order to retrieve the optical and radiative properties of the aerosol in the atmosphere and to predict the fate and behaviour of particles upon deposition at ground level. Individual particle analyses were also compared with bulk chemical analyses on daily sampling filters and back-trajectory analyses of the air mass movement in order to enucleate distinct sources of the aerosol during the long range transport.

P. domestica endocarps biometric features were measured by image analysis techniques.A total of 134 size, shape, color and texture descriptors were measured on each endocarp.The data were statistically processed applying the stepwise Linear Discriminant Analysis.Hypothetical synonymy groups were identified.This is the first computer vision application on plum biodiversity study. The identification of plum varieties is generally done on the base of distinctive plant traits such as shape, size, and fruit drupe color identified during the variety registration, following official descriptors. In this paper, image analysis techniques were applied to study endocarps variability of 23 Prunus domestica cultivars from Sardinia. Digital images were acquired and analysed using a macro specifically developed to measure morpho-colorimetric endocarps features. The data were later statistically processed applying the stepwise Linear Discriminant Analysis (LDA) to implement a statistical classifier able to classify each variety and identify plausible synonymy groups.The present study represent the first attempt to investigate the morphology and morphometry of plum endocarps in order to characterize the whole Sardinian plum agrobiodiversity. It is also the evidence of the usefulness of image analysis techniques in taxonomic investigations too, as well as for the conservation and enhancement of traditional plums for consumer satisfaction.

[EN] It is impossible to take accurate measurements in photogrammetry without first removing the distortion in images. This paper presents a methodology for correcting radial and tangential distortion and for determining the PBS (Point of Best Symmetry) without knowledge of the interior orientation parameters (IOPs). An analytical plumb-line calibration method is used, measuring only the coordinates of points on straight lines, regardless of the position and direction of these lines within the image. Points belonging to multiple lines can also be used since the effects on their X and Y coordinates are calculated independently. The results obtained on an image of a common scene, taken with a handheld non-metric camera, show a high degree of accuracy even with a minimum number of observables. And its application on a calibrated grid for engineering purposes with a semi-metric camera, results optimal even using a single image. (C) 2016 Elsevier Ltd. All rights reserved.

In this paper, we apply a variety of experimental techniques to investigate the influence of the counter-current mode on bubble column hydrodynamics. We study an air–water bubble column, which is 5.3 m in height and has an inner diameter of 0.24 m, and we consider gas superficial velocities in the range of 0.004–0.20 m/s and liquid superficial velocities up to −0.09 m/s. The experimental investigation consists of holdup, gas disengagement, image analysis and optical probe measurements. The holdup measurements are compared with the literature and are used to investigate the flow regime transition. The gas disengagement measurements are used to further investigate the flow regime transition and study the structure of the holdup curve. The image analysis is used to study the bubble shapes and size distributions near the sparger and in the developed region of the column; in particular, the image analysis is applied to different gas velocities in the homogeneous regime in both the batch and counter-current modes. The optical probe is used to acquire radial profiles of the local properties (i.e., local void fraction and bubble rise velocity) to study the flow properties and further investigate the flow regime transition. Comparing the results from the different techniques, the influence of the gas superficial velocity and the liquid superficial velocity is discussed considering all main aspects of the two-phase flow, from the local flow properties to the global flow features. The counter-current mode is found to increase the holdup, reduce the bubble rise velocity, destabilize the homogeneous regime and change the local flow properties.

In this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties. The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network ( http://phenocam.sr.unh.edu/ ), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel-based image analysis available in the phenopix R package.

Made available in DSpace on 2018-11-27T06:00:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-03-15 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) European Regional Development Funds (ERDF), through the Operational Programme 'Thematic Factors of Competitiveness'(COMPETE) Portuguese Funds, through the Fundacao para a Ciencia e a Tecnologia (FCT) Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed for identification of features such as echogenicity, texture and surface in such plaques. In this article, a review of the most important methodologies that have been developed to identify the main components of atherosclerotic plaques in images is presented. Hence, computational algorithms that take into consideration the analysis of the plaques echogenicity, image processing techniques, clustering algorithms and supervised classification used for segmentation, i.e. identification, of the atherosclerotic plaque components in ultrasound, computerized tomography and magnetic resonance images are introduced. The main contribution of this paper is to provide a categorization of the most important studies related to the segmentation of atherosclerotic plaques and its components in images acquired by the most used imaging modalities. In addition, the effectiveness and drawbacks of each methodology as well as future researches concerning the segmentation and classification of the atherosclerotic lesions are also discussed. (C) 2015 Elsevier Ltd. All rights reserved. Minist Educ Brazil, CAPES Fdn, BR-70040020 Brasilia, DF, Brazil Univ Estadual Paulista, BR-15054000 Sj Do Rio Preto, Brazil Univ Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, P-4200465 Oporto, Portugal Univ Estadual Paulista, BR-15054000 Sj Do Rio Preto, Brazil CAPES: 0543/13-6 Portuguese Funds, through the Fundacao para a Ciencia e a Tecnologia (FCT): FCOMP-01-0124-FEDER-028160/PTDC/BBB- BMD/3088/2012

This paper aims to study the effect of geological condition on fragmentation produced by blasting in a marble quarry in Simpang Pulai, Perak, Malaysia; as the condition of the in-situ rock mass is one of the major factors influencing fragmentation degree. Parameters taken into account are chosen from the Rock Mass Rating (RMR) system: discontinuity length, strength of intact material, spacing, weathering, separation and infilling. Geological mapping is done at a bench blasting site before blasting operation is carried out to obtain the structural RMR values of the selected site; the value of strength of intact material is obtained through Uniaxial Compression Strength (UCS) test. After blasted, muckpiles photos are taken to evaluate the degree of fragmentation using image processing software, WipFrag. Fragmentation assessment data from a number of different blasts are then compared and are linked to their respective UCS and structural data to evaluate the impact of these properties on resultant fragments size. It is found that strength of rock mass, discontinuities’ direction against the slope face and its structural properties in a blast location do affect the size of fragments produced.

Digital pathology is set to revolutionise traditional approaches diagnosing and researching diseases. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this paper, we show that the colour distribution of the positive immunohis-tochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this, we propose two novel luminance adaptive biomarker detection methods. We present experimental results to show that the luminance adaptive approach significantly improves biomarker detection accuracy and that random forest based techniques have the best performances.

The paper introduces a new patch-based dissimilarity measure for image comparison employing an approximation strategy. It extends the Average Common Sub-matrix measure computing the exact dissimilarity among images. In the exact method, dissimilarity between two images is obtained by considering the average area of the biggest square sub-matrices in common between the images, by exact match of the extracted sub-matrices pixel by pixel. As an extension, the proposed dissimilarity measure computes an approximate match between the sub-matrices, which is obtained by omitting a controlled number of pixels at a given column offset inside the sub-matrices. The proposed dissimilarity measure is extensively compared with other well-known approximate methods for image comparison in the state-of-the-art. Experiments demonstrate the superiority of the proposed approximate measure in terms of execution time with respect to the exact method, and in terms of retrieval precision with respect to the other state-of-the-art methods.

In this paper, a method for the direct quantitative analysis of amplified DNA via q-Polymerase Chain Reaction (qPCR) in miniaturised silicon-based chip system is presented. The designed tool presented here allows the automatic extraction of meaningful information from input fluorescent images by means of digital image processing algorithm. In particular, a smart mathematical model, optimizing the integration of all the analysis steps of the fluorescence data from on chip multiple real time PCR, is described. Such a tool is able to load the digital input images, select and smartly detect the region of interest for fluorescence, elaborate the data input, calculate the average fluorescence values and finally, plot the fitted curve as output, giving also, for each well, both the Cycle Threshold (CT) and Slope parameters. Keywords: Image analysis, qPCR, LabonChip, Edge detection, Hough transform

International audience; The aim of this paper is to demonstrate that morphological particle properties inside the polymer matrix are responsible for the variability of mechanical properties (elasticity, strength, resilience) and fire retardancy properties. Ultrafine kaolinites were modified and employed to obtain composites of polyamide. These composites were characterized by means of mechanical (tensile static and dynamic tests) and fire retardancy properties (cone calorimeter). Their morphological properties differed significantly according to the aspect ratio and surface treatment of the kaolinites. These morphologies, characterized by the particle dispersion (interparticle distance ID) and size distribution (median diameter MD) in the polymer matrix, were directly related to the mechanical properties. The experimental results demonstrate the sensitivity of strength, resilience and flammability to particle dispersion and distribution. The yield stress decreases with the increase of MD, the resilience decreases with the increase of ID with a critical ID value from which the composite became brittle, and the pHRR increases with the increase of ID.

New technologies in precision viticulture are increasingly being used to improve grape quality. One of the main challenges being faced by the scientific community in viticulture is early yield prediction. Within this framework, flowering as well as fruit set assessment is of special interest since these two physiological processes highly influence grapevine yield. In addition, an accurate fruit set evaluation can only be performed by means of flower counting. Herein a new methodology for segmenting inflorescence grapevine flowers in digital images is presented. This approach, based on mathematical morphology and pyramidal decomposition, constitutes an outstanding advance with respect to other previous approaches since it can be applied on images with uncontrolled background. The algorithm was tested on 40 images of 4 different Vitis vinifera L. varieties, and resulted in high performance. Specifically, values for Precision and Recall were 83.38% and 85.01%, respectively. Additionally, this paper also proposes a comprehensive study on models for estimating actual flower number per inflorescence. Results and conclusions that are developed in the literature and treated herewith are also clarified. Furthermore, the use of non-linear models as a promising alternative to previously-proposed linear models is likewise suggested in this study., Authors would like to thank the ADER agency of the La Rioja regional government and the Spanish Ministry for Economy and Competitiveness in the funding of the projects VINETICS and AGL2011-23673, respectively.

Visualisation of cochlear histopathology in three-dimensions has been long desired in the field of hearing research. This paper outlines a technique that has made this possible and shows a research application in the field of hearing protection after cochlear implantation. The technique utilises robust immunofluorescent labelling followed by effective tissue clearing and fast image acquisition using Light Sheet Microscopy. We can access the health of individual components by immunofluorescent detection of proteins such as myosin VIIa to look at cochlear hair cells, NaKATPase alpha 3 to look at spiral ganglion neurons, and IBA1 to look at macrophages within a single cochlea, whilst maintaining the integrity of fine membranous structures and keeping the cochlear implant in place. This allows the tissue response to cochlear implantation to be studied in detail, including the immune reaction to the implant and the impact on the structure and health of neural components such as hair cells. This technique reduces time and labour required for sectioning of cochleae and can allow visualisation of cellular detail. Use of image analysis software allows conversion of high-resolution image stacks into three-dimensional interactive data sets so volumes and numbers of surfaces can be measured. Immunofluorescent whole cochlea labelling and Light Sheet Microscopy have the capacity to be applied to many questions in hearing research of both the cochlea and vestibular system.

X-ray computed tomography (XCT) is a powerful characterization tool that has been used for examination of both tristructural-isotropic (TRISO) coated particles and fuel forms. Rapid, nondestructive imaging of complete specimens by XCT provides significant characterization advantages in both speed and comprehensiveness over traditional materialographic methods. This paper discusses a method that has been developed to analyze XCT images of pebble fuel forms containing ~18,000 TRISO particles each. The XCT images are three-dimensional with contrast based on relative x-ray transparency. In the case of TRISO fuel forms, XCT image intensity distinguishes between the high-density kernel, the medium-density SiC, and the low-density matrix and carbon layers. Image analysis software has been developed to process XCT data sets to extract spatial distribution information, including particle nearest-neighbor distances, local packing fraction, and fuel-free zone thickness in TRISO fuel forms. Although this type of high-resolution XCT analysis is likely not feasible as a quality control measurement for commercial TRISO fuel, it is highly useful in generating feedback for the development of fuel form fabrication processes.

In this paper we present an experimental technique and a novel colourimetric image analysis algorithm to economically evaluate particle residence times within regions of batch granulators for use in compartmental population balance models. Residence times are extracted using a simple mixing model in conjunction with colourimetric data. The technique is applied to the mixing of wet coloured granules (binary and ternary systems) in a laboratory scale mixer. The resulting particle concentration evolutions were in qualitative agreement with those from the mixing model. It was seen that the algorithm was most stable in the case of the binary colour experiments. Lastly, simulations using the Discrete Element Method (DEM) were also performed to further validate the assumptions made in the analysis of the experimental results. Particle concentrations from the simulations showed the same trends as the experiment and highlighted the importance of particle size distributions on the DEM residence times.

Loosening local equilibrium assumptions in two-phase flow in porous media gives rise to new, unknown variables. More specifically, when loosening the local thermal equilibrium assumption, one has to describe the heat transfer between multiple phases, present at the same mathematical point. In this paper, we calibrate a macro-scale mathematical model which is free of local equilibrium assumptions to experimental observations. We emphasize the correct determination and upscaling of necessary input parameters from the experimental data achieved by image analysis. By choosing an appropriate scaling parameter, we are able to reproduce experimental measurements satisfactorily. This is a first step towards quantifying heat transfer in two-phase flow in porous media. Ultimately, our aim is to find the limits of the applicability of local equilibrium assumptions in two-phase flow in porous media.

Understanding the mechanisms of deformation and failure of soils is essential in geotechnical engineering. In particular, strain localisation is a key issue for the failure of soils. In laboratory mechanical testing, localised deformation can be investigated efficiently through full-field measurements of displacement and strain in soil specimens. The objective of this study is to investigate the deformation and the failure of a soil under triaxial compression using in situ X-ray (i.e., X-ray scanning during the loading test) computed tomography (CT) and digital image correlation (DIC). This paper is concerned with the characterisation of internal strains and with strain localisation patterning in wide-grained sand undergoing triaxial compression at different confinement pressures using Three Dimensional (3D) volumetric DIC. Complete 3D images of specimens have been recorded at several stages throughout the triaxial tests and analysed using 3D Volumetric DIC in order to obtain 3D incremental displacement and strain fields. Based on the results of the combined X-ray tomography and DIC, the deformation process and especially the strain localisation are quantitatively characterised in space and time under different triaxial compression conditions. (C) 2015 The Japanese Geotechnical Society. Production and hosting by Elsevier Boy. All rights reserved. (Less)

Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping.

Due to the advent of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. Several general-purpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic Block-Based image segmentation techniques.

In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle A * algorithm (FAA *), an advanced A * algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV