Image Methodology Research Articles

Updated Review of Longshore Sediment Transport: Advantages, Disadvantages, and Comparisons Using a Satellite Image Methodology

This review updates the different categories and formulations of the calculation of longshore sediment transport (LST) and summarizes their advantages and disadvantages. Most of these methodologies require calibration for areas different from those studied by their authors. Thus, a method of validation and calibration is presented here by processing satellite images with CoastSat software (release v 2.7) to determine accretion and erosion volumes. This low-cost methodology was applied to Salaverry Beach (Peru) to compare the results of the different formulations. A range of variation between −96% and +68% was observed concerning the error, with van Rijn’s formula being the most accurate for this particular case.

ATTENTION, INTEREST, DESIRE AND ACTION MODEL APPLIED TO EDUCATION FIELD THROUGH THE IMAGE, QUESTION AND ANSWER METHODOLOGY

This article presents the development and result of a reflection and study on the application of the method of attention, interest, desire and action in the teaching-learning process. It begins with a conceptualization and analysis of the current paradigm of education with the advances that neuroscience and constructivism have contributed to education in recent years. Next, the phases of the process are presented and described through the IQA methodology in the educational field. The results reflect the need to consider the emotional phases of students and the need to use methodologies that enhance meaningful, efficient and effective learning. It concludes with the reflections, contributions, and final considerations of this study.

Study on the Gradation of Crushed Sand by Using a Novel Digital Image Processing Methodology

It is a trend to replace the increasing shortage of natural sand with crushed sand for the development of building materials. To meet the construction requirements, it is necessary to measure the shape and gradation of crushed sand that is difficult to control. To solve this problem, a novel digital image processing methodology is proposed in this paper. Through the setting of camera resolution, lens and light source selection, the hardware platform of the measurement system was developed. The shape and gradation characterization method of dynamic falling crushed sand was studied, and the measurement software system was developed. Comparative experimental study of equivalent particle size, study of gradations repeatability experiment, comparative experimental study of dynamic digital image methodology and vibration sieving method of gradations and comparative experimental study before and after correction were conducted, respectively. The experimental results show that the equivalent elliptic Feret short diameter is suitable as the equivalent particle size. The maximum repeatability error appears in the size interval of 1.18–2.36 mm, which is 0.9%. The maximum gradation error is reduced from 6.93% before correction to 2.77% after correction. The maximum error of fineness modulus is reduced from − 0.13 to − 0.01. The accuracy of the developed measurement system can meet the actual engineering requirements, and it can realize the quality monitoring of crushed sand.

License Plate Detection using OCR with KNN

This paper presents associate in nursing economical methodology for the detection of vehicle plate by accurately localizing number plates from pictures. A straight forward downscaling of image methodology is projected initial to boost the speed of vehicle plate localization while not reducing the detection performance compared thereupon victimization the first image. Moreover, a completely unique Line Density Filter (LDF) methodology is projected to identify desired regions and eventually reducing the realm to be analysed for vehicle plate detection. Also, a vehicle plate classifier supported SVMs victimization color prominence options employed to spot the vehicle plate from the image. For performance check, a dataset having thirty pictures taken from completely different scenes under different conditions is additionally used. The data set demonstrates that the projected methodology performs well in terms of accuracy and run-time potency.

Image analysis of shatter and pinning events on hard-to-wet leaf surfaces by drops containing surfactant.

A key challenge for developing computer models of spray retention by plants is to accurately predict how spray drops behave when impacting leaf surfaces. One poorly understood outcome occurs when drops bounce or shatter on impact but leave behind a proportion of the liquid on the surface (designated as pinning). This process is studied via impaction experiments with two hard-to-wet leaf surfaces (fat-hen: Chenopodium album and barnyard grass: Echinochloa crus-galli L. P. Beauv) and one hydrophobic artificial surface (Teflon) using three liquid formulations. Drops that impact upon Teflon underwent pinning shatter events via a well-known mechanism referred to as receding breakup. Drops impacting on leaf surfaces did not undergo receding breakup because the liquid rim was not in direct contact with the leaf surface when it broke into secondary droplets. However, pinning did occur on plant surfaces via a different mechanism, especially when using formulations containing a surfactant. Newly developed image analysis and methodology has allowed quantification of the volume fraction pinned to surfaces when drops shatter. The addition of surfactant can increase both the probability of pinning and the pinned volume when drops shatter on fat-hen or Teflon. However, the surfactants studied did not substantially improve the probability of pinning on barnyard grass. The difference in behaviour between the two leaf surfaces and the underlying mechanism is worth further study. © 2020 Society of Chemical Industry.

Frequency tuning of shape perception revealed by classification image analysis.

Classification image analysis is a powerful technique for elucidating linear detection and discrimination mechanisms, but it has primarily been applied to contrast detection. Here we report a novel classification image methodology for identifying linear mechanisms underlying shape discrimination. Although prior attempts to apply classification image methods to shape perception have been confined to simple radial shapes, the method proposed here can be applied to general 2-D (planar) shapes of arbitrary complexity, including natural shapes. Critical to the method is the projection of each target shape onto a Fourier descriptor (FD) basis set, which allows the essential perceptual features of each shape to be represented by a relatively small number of coefficients. We demonstrate that under this projection natural shapes are low pass, following a relatively steep power law. To efficiently identify the observer's classification template, we employ a yes/no paradigm and match the spectral density of the stimulus noise in FD space to the power law density of the target shape. The proposed method generates linear template models for animal shape detection that are predictive of human judgments. These templates are found to be biased away from the ideal, overly weighting lower frequencies. This low-pass bias suggests that higher frequency shape processing relies on nonlinear mechanisms.

Sparking interest in restaurant dishes? Cognitive and affective processes underlying dish design and ecological origin. An fMRI study

This papers aims to verify to what extent the presentation of a restaurant dish and the origin of its food provoke reactions in the consumer's brain during the visualization and the decision-making process, from an exploratory approach. The two independent variables singled out for study were whether the presentation was well or poorly presented and if the ingredients were ecological or non-ecological.The results applying the functional magnetic resonance image (fMRI) methodology reveal that well-presented dishes activate areas in the brain linked to the network of emotions indicating that the visualization in restaurant menus is not a purely cognitive and self-reflexive process but retains a strong affective component. Furthermore, the presence of this component is kept at the moment of choosing a dish, as observed by the activation of the cingulate gyrus, region linked to the regulatory processes of emotions. Hence, research ratifies the existence of an emotional factor during the entire process of decision-making carried out in a restaurant. Yet it is true that exposure to an ecological menu provokes activation of the medial frontal cortex, a region connected to higher reasoning and attention, suggesting that stimuli from well-presented dishes of ecological origin trigger neuronal responses related to high-level cognitive processes.The practical implications derived, along with its limitations and the future research opportunities, are interesting for both developing theory and also practice. Therefore, scholars are encouraged to further test some research proposals (e.g. moderating role of salubrity or simultaneously eye tracking method).

Application of Scanning Capacitance Microscopy on SOI device with wafer edge low yield pattern

As semiconductor technology keeps scaling down, many advanced technology and process were applied in the semiconductor process. Especially for the application of IOT (internet of thing) technology, the low leakage and low power consumption product was the key component for this kind of application. SOI (Silicon-On-Insulator) wafer process is one of the advanced and important branches of the semiconductor manufacturing process. Its intrinsic advantage, low leakage and lower power consuming make it very suitable for personal communication device and IOT which match well with the application requirement. As is well known the SOI wafer is different form the normal bulk silicon wafer. The active sits on the silicon oxide insulator, which makes the final device separate from the substrate. Basically, all of the devices are floating on a nonconductive oxide layer. It comes with many challenges for process and analysis as compared with the conventional bulk silicon process.The most conventional analysis method is not applicable in the SOI device such as the PVC (passive voltage contrast) and current image methodology which are a very powerful and important in the failure analysis.In this paper, scanning capacitance is successfully used as the substitution of the PVC method. The SCM (Scanning Capacitance Microscopy) is a complicated process. Since all of the abnormality or physical change will affect the measured capacitance, then the capacitance signal will theoretically has many information with itself, including open, short and leakage. Through the detailed study, the contact level top-down SCM was successfully applied on the SOI unit. By proper setting of SCM bias condition, it can not only visualize the possible leaky location but also can reveal the possible path. Further nanoprobing and TEM (Transmission Electron Microscopy) have confirmed the SCM analysis.

Analysis of Image Compression using Wavelets

In this paper significant features of wavelet transform in compression of images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression is being studied it has been found that maximum improvement in picture quality with higher compression ratio is achieved by wavelet based image compression In this paper examined a basic concept of wavelets; wavelet transform and discrete wavelet transform and also deliberate the principle of image compression and image methodology. The objective is to select the appropriate mother wavelet during the transform stage towards compression the gray image and the quality of reconstructed image has been estimated in terms of image quality metrics PSNR and CR and also computes compression ratio at different level of decompositions of DWT. Haar, Daubechies and Biorthogonal, Coiflets and Symlet wavelet have been applied to an image and their qualitative and quantitative analysis results has been compared in terms of PSNR values, MSE and compression ratios. In this paper going to reduce the size of gray image with maintain good picture quality, this property is helpful to storage and transmission of data over internet.

Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach.

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

Online biospeckle assessment without loss of definition and resolution by motion history image

The application of the dynamic laser speckle as a reliable instrument to achieve maps of activity in biological material is available in literature optics and laser. The application, particularly in live specimens, such as animals and human beings necessitated some approaches to avoid the kinking of the bodies, which creates changes in the patterns undermining the biological activity under monitoring. The adoption of online techniques circumvented the noise generated by the kinking, however, with considerable reduction in the resolution and definition of the activity maps. This work presents a feasible alternative to the routine online methods based on the Motion History Image (MHI) methodology. The adoption of MHI was tested in biological and non-biological samples and compared with online as well as offline procedures of biospeckle image analysis. Tests on paint drying was associated to alcohol volatilization, and tests on a maize seed and on growing of roots confirmed the hypothesis that the MHI would be able to implement an online approach without the reduction of resolution and definition on the resultant images, thereby presenting in some cases results that were comparable to the offline procedures.

Análise computacional para auxílio ao diagnóstico de osteoartrite de coluna lombar baseado em redes neurais artificiais

OBJETIVOS: Conhecer as vantagens da utilizacao das redes neurais artificiais no reconhecimento de padroes em radiografias de coluna lombar na incidencia perfil para auxiliar no diagnostico da osteoartrite primaria. METODO: Estudo transversal, descritivo, analitico, de abordagem quantitativa e com enfase diagnostica. O conjunto de treinamento foi composto por imagens coletadas no periodo de janeiro a julho de 2009 de pacientes submetidos a radiografias digitais de coluna lombar na incidencia em perfil provenientes de um servico de radiologia localizado no municipio de Criciuma (SC). Das 260 imagens coletadas, foram excluidas: as radiografias distorcidas, as patologias que alteram a arquitetura da coluna lombar e os padroes de dificil caracterizacao, resultando em um total de 206 imagens. O banco de imagens (n = 206) foi subdividido, resultando em 68 radiografias para a etapa de treinamento, 68 para testes e 70 para validacao. Foi utilizada uma rede neural hibrida baseada em mapas auto-organizaveis de Kohonen e redes Multilayer Perceptron. RESULTADOS: Apos 90 ciclos, foi realizada a validacao com o melhor teste, alcancando acuracia de 62,85%, sensibilidade de 65,71% e especificidade de 60%. CONCLUSAO: Apesar da demonstracao de uma eficacia mediana, por se tratar de estudo de carater inovador, seus valores mostram um futuro promissor da tecnica utilizada, com sugestao para trabalhos futuros com abrangencia na metodologia de processamento das imagens e ciclos com uma quantidade maior de radiografias.

COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE

Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.

A Novel Image Methodology for Interpretation of Gold Immunochromatographic Strip

Gold immunochromatographic strip assay is a rapid, simple, single-copy and on-site method. Quantitative Interpretation of the strip can provide more information than the traditional qualitative or semiquantitative strip assay. The paper aims to develop an image based assay method for quantitative determination of trace concentrations by gold immunochromatographic strip. The image of gold immunochromatographic strip is taken by CCD, and, after the proper filter and window cutting, the test line and control line is segmented by the genetic fast fuzzy c-means(FCM) clustering algorithm. In order to improve the measure property, based on Lambert-beer law, the relative reflective integral optical density(RIOD) is selected as the feature by which the interference in the test and control lines can be canceled out each other. The proposed method is applied to the quantitative detection of human chorionic gonadotropin (hCG) as a model. Firstly, the segmentation performance of the genetic fast FCM clustering algorithm is compared with threshold method and FCM clustering algorithm in terms of the peak signal-to-noise ratio (PSNR). Furthermore, the comparison of the blind experiment between the proposed method and commercial quantitative instrument swp-sc1 is carried out. This method is shown to deliver a result comparable and even superior to existing techniques.

Dynamic application of digital image and colour processing in characterizing flame radiation features

In this work, the experimental investigation of the dynamic flame properties of flame flickering and equivalence ratio sensing of a combustion process was done. In particular, the time-varied flame properties were examined using a novel digital image and colour processing methodology. This technique makes use of the observed correlation between a digital image colour signal and physical flame radiation characteristics in the visible wavelength domain. Aspects of RGB and HSV colour modelling principles were applied to show that the addition of colour identification in the image processing of high-speed flame image data could yield three useful parameters which are related to the dynamic behaviour of different flame emanating components. First, the validity of the colour identities for tracking the yellowish-red diffusion and greenish-blue premixed flame colourations were examined by comparing their respective flickering frequency profiles. Then, the usefulness of the extracted Rdiffusion, Gpremixed and Bpremixed colour signals to abstractly represent the behaviour of soot, C2* and CH* emission characteristics in a dynamic flame transition from diffusion to stoichiometric premixed condition was demonstrated. In particular, the colour signal ratio Bpremixed/Gpremixed was correlated to exemplify the approximate time-varied state of the equivalence ratio from the imaged combustion phenomenon.

Epicuticular Phenolics Over Guard Cells: Exploitation for in situ Stomatal Counting by Fluorescence Microscopy and Combined Image Analysis

Guard cells emit an alkali-induced, blue fluorescence upon excitation by ultraviolet radiation (emission maximum energy at 365nm). Fluorescence emission of guard cells was brighter than that of the neighbouring epidermal cells in a number of wild and cultivated plants including conifers, but the relative fluorescence intensity and quality was species-dependent. Three representative plants possessing stomatal complexes which differed morphologically were studied: Olea europaea, Vicia faba and Triticum aestivum. Immersing leaves of these plants in chloroform for 30s (thereby removing epicuticular waxes) significantly reduced the intensity of the fluorescence emitted by guard cells. This indicates that guard cell fluorescence could be due to either an increased concentration of fluorescing compounds (probably wax-bound phenolics), or a thicker cuticular layer covering the guard cells. Given that the alkali-induced blue fluorescence of the guard cells is a common characteristic of all plants examined, it could be used as a rapid and convenient method for in situ measurements of the number, distribution and size of stomatal complexes. The proposed experimental procedure includes a single coating of the leaf surface by, or immersion of the whole leaf in, a 10% solution of KOH for 2min, washing with distilled water, and direct observation of the leaf surface under the fluorescence microscope. Fluorescence images were suitable for digital image analysis and methodology was developed for stomatal counting using Olea europaea as a model species.

Experiential Learning and Customer Needs in the Undergraduate Marketing Research Course

Research was conducted among undergraduates in the target market for a marketing research course to find ways of improving the course image, content, and teaching methodology. This study is unique because it applies the principles of marketing research to a course on marketing research, using the class to identify what students want from their marketing research course. Because the research was personally relevant to the students, they were highly motivated in a course that is historically unpopular. Students saw how they could work to improve their own experience and that of their peers, and they began to appreciate the value of carefully conceived and executed research in effective and successful service design.

NEURAL NETWORKS IN CHEMOTHERAPY-INDUCED DELAYED NAUSEA - A PILOT-STUDY USING POSITRON EMISSION TOMOGRAPHY

Acute but not delayed nausea is well controlled by serotonergic receptor-antagonists implicating different mediation of delayed as compared to acute nausea and vomiting. We report on differences in central neural activity using positron emission tomographic (PET) measurements of regional cerebral blood flow (rCBF) in two chemotherapy treated cancer patients during delayed severe and mild nausea. Subtractive image methodology where rCBF during moderate nausea was subtracted from rCBF during mild nausea revealed increased neural activity in the anterior hypothalamus, the vermis, the anterior cingulate gyrus and the thalamus as well as decreased activity in the pens and the substantia nigra. These results may reveal central nervous system substrates underlying delayed nausea and vomiting and are consistent with an interpretation in terms of activation both of serotonergic and noradrenergic but not dopaminergic transmitter systems. It is concluded that noradrenergic mediation of delayed nausea is not an unlikely possibility.