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Broad ion beam - scanning electron microscopy (BIB-SEM) is an important technology for quantitative description of pore space and detailed investigation of pore structure in mudstones and shales. To obtain quantitative data from BIB-SEM images, image processing is required. However, the effect of different image processing approaches on the segmentation results has not been well documented yet. Particularly the interpretation of pore boundaries approaching the maximum image resolution can represent a major challenge. This study addresses the variability in image processing by comparing conventional thresholding with advanced machine learning methods and analysing the sensitivity of results to different segmentation. The test dataset consists of large area BIB-SEM maps of 12 Middle Miocene mudstone samples from the Vienna Basin, Austria. Segmentation of pore space and organic matter was performed using the thresholding method in ImageJ and the machine learning-based pixel classification in ilastik. The results show that the selected greyscale thresholding algorithm in ImageJ significantly affected the resolved porosity (1.7% to 2.8% variation) relative to the average total porosity of 4.5%. Compared to ImageJ, ilastik provides comparable results with better efficiency in separating pores from organic matter. However, the obtained pore information is inevitably influenced by variations in boundary delineation and feature identification due to inherent differences in the image processing methods and varying representativeness of image maps themselves. Overall, the study findings highlight the importance of carefully selecting image processing methods and considering their potential impact on the accuracy and reliability of pore characterization in mudstones and shales.

This paper provides an overview of automatic fabric fault detection approaches that have been developed in recent years. Fabric fault detection is very popular topic of automation moreover quality control is one of the important features in textile industry. The performance of the projected idea is evaluated by using different techniques of patterned fabric images with different types of common fabric defects. Moreover detection methods were also evaluated in real time using a model automation specification system. This research paper will be useful for both researchers and practitioners in the field of image processing and computer vision to understand the uniqueness of the different defect detection methods. The recognition receives a digital fabric image from the image acquisition device and transforms it to a binary image using the restoration and threshold methods. This research presents a technique that decreases physical exertion. This image processing method was performed using “MATLAB 7.10”. Therefore, this study uses a textile fault detector with a systematic vision approach for image processing.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

The presence of offshore wind farms causes downstream regions of reduced wind velocity, i.e. wind farm (cluster) wakes, which can affect the power of wind farms downstream. Engineering models are now being used to simulate the effects of these wakes, and an important requirement for model validation is a comparison with full-field measurements. Our objective in this paper is to parametrize and validate two engineering wake models with long-range lidar measurements. We use a long-range scanning Doppler lidar to scan the near wake region of a 400 MW offshore wind farm and compare the wind velocities in the wake to the outputs of two engineering models: FarmFlow and flappy. We adapt FarmFlow to solve the flow in highly unstable atmospheres by modifying the boundary conditions, which enables the comparison of velocity profiles behind the farm. The models perform qualitatively well in predicting the wake deficit and shape close to the farm and at lower heights. They predict higher wake losses within the farm when compared to production power data in a strongly unstable atmospheric case. However, the current analysis is limited due to the lack of inflow measurements for model initialization, compounded by limited data availability. We discuss the possibilities and limitations of long-range scanning lidar data for cluster wake model validation and the need for inflow measurements for model initialization. We conclude that with detailed inflow measurements, scanning long-range lidars could serve as a good tool for the validation of wind farm wake models.

In order to solve the problem that all levels of civil aviation meteorological centers are unable to provide all the possible meteorological information services that may reach the low-altitude airspace for general aviation flight, this paper proposes a real-time low-altitude meteorological intelligence image collection and processing method. Based on this method, a real-time low-altitude meteorological intelligence image collection system can be designed and implemented. The real-time low-altitude meteorological intelligence image collection system is an information system that provides real-time low-altitude airspace visibility and weather condition information services for image-based flight photography, transmission, processing, and meteorological information dissemination. It includes the image collection subsystem and image processing subsystem, meteorological information publishing Web Server. Among them: The image collection subsystem is mainly used for automatically taking images of the surrounding environment at regular intervals, and transmitting the photographs and their time and place information to the image processing system installed in the low-altitude navigational information service department through the wireless communication device; the image processing subsystem collects images and related information for further processing; the meteorological information publication Web server mainly provides general airline users with services such as consultation, inquiry, and submission of low-altitude meteorological intelligence information of relevant airspace related to images.

Wood failure percentage (WFP) is an important index for evaluating the bond strength of plywood. Currently, the method used for detecting WFP is visual inspection, which lacks efficiency. In order to improve it, image processing methods are applied to wood failure detection. The present study used thresholding and K-means clustering algorithms in wood failure detection, and four kinds of plywood were manufactured to analyze the influences of wood species and adhesive. Results show that the detection by K-means clustering method is more accurate than thresholding method; it could better correlate with visual inspection results, while the detection results by thresholding method could not reflect the fluctuation of visual inspection results with types of plywood. Moreover, both analyses of the influence of adhesive and wood species show that thresholding method based detection results are more affected by adhesive color, veneer color and permeability of poplar and eucalyptus veneer (mean absolute error compared with visual inspection: PF-Eucalyptus: 15.77 %; PF-Poplar: 25.42 %; UF-Eucalyptus: 30.55 %; UF-Poplar: 21.48 %); whereas K-means clustering method based detection results show no significant change as adhesive and wood species varies (PF-Eucalyptus: 11.07 %; PF-Poplar: 10.22 %; UF-Eucalyptus: 14.77 %; UF-Poplar: 8.50 %). It can be concluded that K-means clustering method has better compatibility for different adhesive and wood species in wood failure detection.

Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model baseof the CDSS.

The objective of this study is to quantify the in vivo kinematics of the superficial femoral artery (SFA) caused by hip and knee flexion by utilizing vascular and skeletal magnetic resonance (MR) imaging and image processing methods. Seven male healthy volunteers (56 ± 5 years old) were imaged using contrast-enhanced MR angiography in the supine and bent-leg positions using a GE Signa Excite 1.5-T scanner. Coregistered SFA coordinates from these two body positions provide the relative motion of the SFA with respect to the femur as a result of hip and knee flexion. With 86 ± 6° hip and 39 ± 6° knee flexion angles, the proximal portion of the SFA moved significantly inferiorly while the distal portion of the SFA stayed immobile in relation to the femur due to hip and knee flexion. From the supine position to the bent-leg position, the top of the SFA moved 23.5 ± 7.0 mm closer to the bottom of the SFA (p < 0.05), resulting in shortening and buckling of the SFA. The differential translations of points along the SFA indicate shortening and buckling along the length of the vessel. They also indicate that the SFA is more tethered to the bony structures at the knee compared to the thigh. The in vivo SFA kinematics obtained in this study provides insight into the interaction between the SFA and the surrounding musculoskeletal structures.

Budaya aksara Lampung pada saat ini sudah jarang digunakan sehingga terancam punah. Untuk melestarikan budaya tersebut, maka dilakukan penelitian mengenai huruf aksara Lampung secara digital dengan metode pengolahan citra dan pengenalan pola. Pada penelitian ini, terbagi menjadi dua tahap yaitu tahap pelatihan dan tahap pengujian. Pada tahap pelatihan dilakukan pengolahan citra dan pelatihan jaringan saraf tiruan (JST) backpropagation. Salah satu tahapan dalam pengolahan citra adalah deteksi tepi. Dalam menganalisis karakter huruf aksara Lampung menggunakan metode deteksi tepi untuk melihat pola garis huruf tersebut. Metode pengolahan citra yang digunakan dalam mendeteksi tepi citra adalah metode deteksi tepi Roberts dan metode deteksi tepi Sobel. Hasil pengolahan citra akan masuk ke tahap pelatihan JST. Bobot yang didapat dari pelatihan kemudian akan digunakan pada tahap pengujian. Metode Sobel dapat mengenali karakter aksara Lampung lebih baik, dimana hasil dari pengujian karakter aksara Lampung memberikan nilai presentase error sebesar 28.5% dengan metode deteksi Roberts dan 14.5% dengan metode deteksi Sobel.

Image binarization is one of the fundamental methods in image processing and it is mainly used as a preprocessing for other methods in image processing. We present an image binarization method with the primary purpose to find markers such as those used in mobile 3D scanning systems. Handling a mobile 3D scanning system often includes bad conditions such as light reflection and non-uniform illumination. As the basic part of the scanning process, the proposed binarization method successfully overcomes the above problems and does it successfully. Due to the trend of increasing image size and real-time image processing we were able to achieve the required small algorithmic complexity. The paper outlines a comparison with several other methods with a focus on objects with markers including the calibration system plane of the 3D scanning system. Although it is obvious that no binarization algorithm is best for all types of images, we also give the results of the proposed method applied to historical documents.

Recent developments for acquiring and distributing remotely-sensed data have greatly increased data availability to the user community. The past two decades have witnessed an explosion in data acquisition by a variety of ground, airborne and orbital sensors. The popularization of Unmanned Aerial Systems (UAS) and the development of reduced cost orbital platforms should guarantee that even higher data volumes will be available to future analysts. The past decades also saw the opening of image data archives (e.g., Landsat, CBERS, Sentinel), making access to a rich database of moderate resolution satellite images a reality across the globe. This increased volume and variety of remotely-sensed data increases the demand for methods and procedures for data handling and information extraction. This chapter describes recent efforts to expand the analyst’s data processing toolset and includes the theory and strategies used in manipulating remotely-sensed data by digital systems. The text focuses on presenting algorithms and techniques for image processing and analysis and emphasizes recent developments not covered by previous editions of the ASPRS Manual of Remote Sensing. Although the main topics covered by the chapter involve the direct processing of images, the text also covers concepts involved in processing remote sensing data that may not have been collected or stored as images, such as spectral curves acquired by spectroradiometers. Several sections of this chapter match this description, including Spectral Vegetation Indices and Spectral Mixture Analysis. Image processing includes not only the analysis of images, but also the necessary steps involved in preparing images for analysis, such as geometric correction, atmospheric correction and several techniques associated with image enhancement. Spectral indices resulting from the combination of multiple spectral bands are presented, with emphasis on the description of vegetated targets. A detailed treatment is given to the mixture problem resulting from the contribution of multiple materials within the instantaneous field of view (IFOV) of a given sensor. Because multiple applications can benefit from the increased explanation power provided by a large number of spectral bands, hyperspectral data processing is also presented and discussed. Further, the chapter addresses the benefits and challenges involved in combining datasets acquired by different systems (Data Fusion). Image classification addresses multiple strategies involved in assigning classes to images (e.g., Support Vector Machine, and Decision Trees); and includes advances in Object-Based Image Analysis (OBIA), particularly those related to image segmentation in preparation for classification. Given the increasing length of remotely-sensed data time series, particular attention is given to preparing sequences of images and data, including multiple techniques for smoothing, spike removal and the retrieval of metrics associated with temporal variations of targets. The chapter also brings multiple examples of use of products derived from processing remotely-sensed data as input to a variety of workflows, including modeling and analysis efforts. Finally, very current topics involving recent advances in image acquisition and availability, are presented for generating 3D surfaces from multiple images using Structure from Motion (SfM); processing of very large datasets (Big Data); and processing of images in the cloud are presented.

The geometry of vascular system is an important determinant of blood flow in health and disease. There is a strong geometric component to atherosclerosis in coronary heart disease since lesions are preferentially located at bifurcation points and regions of high curvature. The influence of these local structures on recirculation and deleterious shear stresses and their role in plaque development is widely accepted. Over time, researchers have turned to MR, CT, or biplane images of vascular trees to faithfully capture these features in the flow simulations. Historically, this has taken the form of labor-intensive manual reconstructions from morphometric measurements based on the centerline, whereby small idealized subsets of vascular trees are developed into computational grids. With improved imaging, image processing, and geometric reconstruction algorithms, researchers have begun to develop geometrically accurate computational models directly from the medical images. This chapter provides an overview of contemporary methods for image processing, centerline detection, boundary condition definition, and grid generation of both clinical and research images of cardiovascular structures.

17 - Arterial Tree Morphometry

We introduced the image processing application to identify fracture and calculate fracture porosity based on the image logging for the gas-bearing shale. Shale gas is an unconventional gas resource and has been paid more attention in recent years. The pore structure of shale is very complicated, where fracture is the main flowing channel. The conventional logging and the imaging logging offer the abundant information for formation evaluation in downhole. The dual laterolog logging is used to identify fracture, but difficult to get accurate fracture porosity. The imaging logging could directly characterize fracture in the borehole and easily estimate fracture porosity with images processing method. The image logging processing with the mathematical morphology to calculate the fracture porosity was discussed in the paper. A typical shale gas well Jiaoye1 from Jiaoshiba area of Chongqing City in Sichuan Basin was presented to compare the calculation result of the two methods. The results shows that both the dual laterolog logging and the image logging could identify borehole fracture and calculate fracture porosity, where the dual laterolog logging has relatively large uncertainty. The advantage of the imaging logging clearly indicates borehole fracture, but the image processing method should be improved.

In this study, dimensions, mass and shape characteristics of five commercial loquat cultivars (‘Akko XIII’, ‘Champagne de Grasse’, ‘Guzelyurt 6’, ‘Hafifcukurgobek’ and ‘Sayda’) and two genotypes (‘KKTC3’ and ‘KKTC4’) were determined. Physical properties such as dimension and shape were determined both by digital caliper and image processing to compare each other. Among the cultivars and genotypes tested, ‘KKTC4’ (58.88 mm) had the highest length while ‘KKTC3’ had the highest major diameter, minor diameter, mass, geometric mean diameter, surface area and volume, with a mean of 46.31 mm, 43.61 mm, 60.12 g, 48.69 mm, 74.82 cm2, and 61.25 cm3, respectively. The greatest value of elongation and the smallest value of sphericity were obtained from ‘Akko XII’ with 1.33, and ‘Akko XII’ was more oblong than the other cultivars and genotypes. The smallest value of elongation and the greatest value of sphericity were obtained from ‘Sayda’ with 1.12 and 91.16%, respectively. The results of comparison between the digital caliper and image processing showed that using the image processing method could potentially be rapid and alternative to the conventional measurement method. Key words: Eriobotrya japonica, image processing, loquat, physical properties.