Search results for: image processing of electrical impedance tomography

Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

Procedia PDF Downloads 376

Authors: Ketwadee Madden

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Product or service image is among the vital factors that predict individuals’ choice of buying a product or services, goes to a place or attached to a person. Similarly, in the context of tourism, the destination image is a very important factor to which tourist considers before making their tour destination decisions. In light of this, the objective of this study is to conceptually investigate among French tourists, the determinants of Thailand’s tourism destination image. For this objective to be achieved, prior studies were reviewed, leading to the development of conceptual framework highlighting the determinants of destination image. In addition, this study develops some hypotheses that are to be empirically investigated. Aside these, based on the conceptual findings, suggestions on how to motivate European tourists to chose Thailand as their preferred tourism destination were made.

Keywords: cognitive destination image, affective destination image, motivations, risk perception, word of mouth

Procedia PDF Downloads 139

Authors: Tahir Iqbal, Mumtaz Ali, Syed Wajahat Kareem, Muhammad Harris

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Digital information is expanding in exponential order in our life. Information that is residing online and offline are stored in huge repositories relating to every aspect of our lives. Getting the required information is a task of retrieval systems. Content based image retrieval (CBIR) is a retrieval system that retrieves the required information from repositories on the basis of the contents of the image. Time is a critical factor in retrieval system and using indexed views with CBIR system improves the time efficiency of retrieved results.

Keywords: content based image retrieval (CBIR), indexed view, color, image retrieval, cross correlation

Procedia PDF Downloads 470

Authors: Areej R. Banjar, Abraham G. Campbell

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The use of Mixed Reality (MR) in teaching and training is growing popular and can improve students’ ability to perform technical procedures. This short paper outlines the creation of an interactive educational MR 3D application that aims to improve the quality of instruction for dentistry students. This application is called MRImplantology and aims to teach the fundamentals and preoperative planning of dental implant placement. MRImplantology uses cone-beam computed tomography (CBCT) images as the source for 3D dental models that dentistry students will be able to freely manipulate within a 3D MR world to aid their learning process.

Keywords: augmented reality, education, dentistry, cone-beam computed tomography CBCT, head mounted display HMD, mixed reality

Procedia PDF Downloads 190

Authors: Youngseok Kim, Chul Park, Jonghwa Yi, Sangsik Choi

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The 2-MHz Side Scan SONAR (SSS) attached to the boat for inspection of underwater structures is affected by shaking. It is difficult to determine the exact scale of damage of structure. In this study, a motion sensor is attached to the inside of the 2-MHz SSS to get roll, pitch, and yaw direction data, and developed the image stabilization tool to correct the sonar image. We checked that reliable data can be obtained with an average error rate of 1.99% between the measured value and the actual distance through experiment. It is possible to get the accurate sonar data to inspect damage in underwater structure.

Keywords: image stabilization, motion sensor, safety inspection, sonar image, underwater structure

Procedia PDF Downloads 280

Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

Procedia PDF Downloads 125

Authors: Ashraf Abuelhaija, Klaus Solbach

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In Ultra high-field MRI scanners (3T and higher), parallel RF transmission techniques using multiple RF chains with multiple transmit elements are a promising approach to overcome the high-field MRI challenges in terms of inhomogeneity in the RF magnetic field and SAR. However, mutual coupling between the transmit array elements disturbs the desirable independent control of the RF waveforms for each element. This contribution demonstrates a 18 dB improvement of decoupling (isolation) performance due to the very low output impedance of our 1 kW power amplifier.

Keywords: EM coupling, inter-element isolation, magnetic resonance imaging (mri), parallel transmit

Procedia PDF Downloads 495

Authors: Lan Du, Yan Wang, Hui Dai

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Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method.

Keywords: change detection, Synthetic Aperture Radar (SAR), Scale-Invariant Feature Transformation (SIFT), segmentation

Procedia PDF Downloads 386

Authors: Raghvendra Singh Yadav, Ivo Kuřitka, Jarmila Vilcakova, Pavel Urbanek, Michal Machovsky, Milan Masař, Martin Holek

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ZnFe2O4 spinel ferrite nanoparticles were synthesized by sol-gel auto-combustion method. The synthesized spinel ferrite nanoparticles were annealed at different higher temperature to achieve different size nanoparticles. The as synthesized and annealed samples were characterized by powder X-ray Diffraction Spectroscopy, Raman Spectroscopy, Fourier Transform Infrared Spectroscopy, UV-Vis absorption Spectroscopy and Scanning Electron Microscopy. The magnetic properties were studied by vibrating sample magnetometer. The variation in magnetic parameters was noticed with variation in grain size. The dielectric constant and dielectric loss with variation of frequency shows normal behaviour of spinel ferrite. The variation in conductivity with variation in grain size is noticed. Modulus and Impedance Spectroscopy shows the role of grain and grain boundary on the electrical resistance and capacitance of different grain sized spinel ferrite nanoparticles. Acknowledgment: This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic – Program NPU I (LO1504).

Keywords: spinel ferrite, nanoparticles, magnetic properties, dielectric properties

Procedia PDF Downloads 428

Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

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Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

Procedia PDF Downloads 177

Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

Procedia PDF Downloads 77

Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

Procedia PDF Downloads 129

Authors: Auwal Abubakar, Ahmed Ahidjo, Shazril Imran Shaukat, Noor Khairiah A. Karim, Gokula Kumar Appalanaido, Hafiz Mohd Zin

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Background: The use of optical surface imaging systems (OSISs) is increasingly becoming popular in radiotherapy practice, especially during breast cancer treatment. This study reviews the accuracy of the available commercial OSISs for breast radiotherapy. Method: A literature search was conducted and identified the available commercial OSISs from different manufacturers that are integrated into radiotherapy practice for setup verification during breast radiotherapy. Studies that evaluated the accuracy of the OSISs during breast radiotherapy using cone beam computed tomography (CBCT) as a reference were retrieved and analyzed. The physics and working principles of the systems from each manufacturer were discussed together with their respective strength and limitations. Results: A total of five (5) different commercially available OSISs from four (4) manufacturers were identified, each with a different working principle. Six (6) studies were found to evaluate the accuracy of the systems during breast radiotherapy in conjunction with CBCT as a goal standard. The studies revealed that the accuracy of the system in terms of mean difference ranges from 0.1 to 2.1 mm. The correlation between CBCT and OSIS ranges between 0.4 and 0.9. The limit of agreements obtained using bland Altman analysis in the studies was also within an acceptable range. Conclusion: The OSISs have an acceptable level of accuracy and could be used safely during breast radiotherapy. The systems are non-invasive, ionizing radiation-free, and provide real-time imaging of the target surface at no extra concomitant imaging dose. However, the system should only be used to complement rather than replace x-ray-based image guidance techniques such as CBCT.

Keywords: optical surface imaging system, Cone beam computed tomography (CBCT), surface guided radiotherapy, Breast radiotherapy

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Authors: L. Korpinen, P. Taskinen, P. Rautio

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This study aimed to describe procedures that we developed to use in the quantitative, bone-specific SPECT/CT at our hospital. Our procedures included the following questions for choosing imaging protocols, which were based on a clinical doctor's referral: (1) Is she/he a cancer patient or not? (2) Are there any indications of inflammatory rheumatoid arthritis? We performed about 1,106 skeletal scintigraphies over two years. About 394 patients were studied with quantitative bone-specific single-photon emission computed tomography/computerized tomography (SPECT/CT) (i.e., about 36% of all bone scintigraphies). Approximately 64% of the patients were studied using the conventional Anterior-Posterior/Posterior-Anterior imaging. Our procedure has improved efficiency and decreased cycle times.

Keywords: skeletal scintigraphy, SPECT/CT, imaging, procedure

Procedia PDF Downloads 152

Authors: Sandy Black, Calum McLaughlin, Alessandro Pranzitelli, Marc Laing

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Multiphase flow structures of two-phase multicomponent fluids were experimentally investigated in a large diameter high-pressure pipeline up to 130 bar at TÜV SÜD’s National Engineering Laboratory Advanced Multiphase Facility. One of the main objectives of the experimental test campaign was to evaluate the impact of pressure on multiphase flow patterns as much of the existing information is based on low-pressure measurements. The experiments were performed in a horizontal and vertical orientation in both 4-inch and 6-inch pipework using nitrogen, ExxsolTM D140 oil, and a 6% aqueous solution of NaCl at incremental pressures from 10 bar to 130 bar. To visualise the detailed structure of the flow of the entire cross-section of the pipe, a fast response X-ray tomography system was used. A wide range of superficial velocities from 0.6 m/s to 24.0 m/s for gas and 0.04 m/s and 6.48 m/s for liquid was examined to evaluate different flow regimes. The results illustrated the suppression of instabilities between the gas and the liquid at the measurement location and that intermittent or slug flow was observed less frequently as the pressure was increased. CFD modellings of low and high-pressure simulations were able to successfully predict the likelihood of intermittent flow; however, further tuning is necessary to predict the slugging frequency. The dataset generated is unique as limited datasets exist above 100 bar and is of considerable value to multiphase flow specialists and numerical modellers.

Keywords: computational fluid dynamics, high pressure, multiphase, X-ray tomography

Procedia PDF Downloads 143

Authors: Vikas Kumar

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Portable batteries are reliable source of mobile energy to power smart wearable electronics, medical devices, communications, and others internet of thing (IoT) devices. There is a continuous increase in demand for thinner, more flexible battery with high energy density and reliability to meet the requirement. For a flexible battery, factors that affect these properties are the stability of current collectors, electrode materials and their interfaces with the corrosive electrolytes. State-of-the-art conventional and flexible batteries utilise carbon as an electrode and current collectors which cause high internal resistance (~100 ohms) and limit the peak current to ~1mA. This makes them unsuitable for a wide range of applications. Replacing the carbon parts with metallic components would reduce the internal resistance (and hence reduce parasitic loss), but significantly increases the risk of corrosion due to galvanic interactions within the battery. To overcome these challenges, low cost electroplated nickel (Ni) on copper (Cu) was studied as a potential anode current collector for a zinc-manganese oxide primary battery with different concentration of NH4Cl/ZnCl2 electrolyte. Using electrical impedance spectroscopy (EIS), we monitored the open circuit potential (OCP) of electroplated nickel (different thicknesses) in different concentration of electrolytes to optimise the thickness of Ni coating. Our results show that electroless Ni coating suffer excessive corrosion in these electrolytes. Corrosion rates of Ni coatings for different concentrations of electrolytes have been calculated with Tafel analysis. These results suggest that for electroplated Ni, channelling and/or open porosity is a major issue, which was confirmed by morphological analysis. These channels are an easy pathway for electrolyte to penetrate thorough Ni to corrode the Ni/Cu interface completely. We further investigated the incorporation of a special printed graphene layer on Ni to provide corrosion protection in this corrosive electrolyte medium. We find that the incorporation of printed graphene layer provides the corrosion protection to the Ni and enhances the chemical bonding between the active materials and current collector and also decreases the overall internal resistance of the battery system.

Keywords: corrosion, electrical impedance spectroscopy, flexible battery, graphene, metal current collector

Procedia PDF Downloads 129

Authors: Takanori Tanaka, Daisuke Kitao, Daisuke Ikeda

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The aurora is an attractive phenomenon but it is difficult to understand the whole mechanism of it. An approach of data-intensive science might be an effective approach to elucidate such a difficult phenomenon. To do that we need labeled data, which shows when and what types of auroras, have appeared. In this paper, we propose an image retrieval system for auroral all-sky images, some of which include discrete and diffuse aurora, and the other do not any aurora. The proposed system retrieves images which are similar to the query image by using a popular image recognition method. Using 300 all-sky images obtained at Tromso Norway, we evaluate two methods of image recognition methods with or without our original color filtering method. The best performance is achieved when SIFT with the color filtering is used and its accuracy is 81.7% for discrete auroras and 86.7% for diffuse auroras.

Keywords: data-intensive science, image classification, content-based image retrieval, aurora

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Authors: Veronica Sorgato, Jeremy Belhassen, Philippe Chartier, Roddy Sihanath, Nicolas Docquiere, Jean-Yves Giraud

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We analyzed the image-guided radiotherapy method used by the TomoTherapy® System (Accuray Corp.) for patient repositioning in clinical routine. The TomoTherapy® System computes X, Y, Z and roll displacements to match the reference CT, on which the dosimetry has been performed, with the pre-treatment MV CT. The accuracy of the repositioning method has been studied according to the treatment localization. For this, a database of 18774 treatment sessions, performed during 2 consecutive years (2016-2017 period) has been used. The database includes the X, Y, Z and roll displacements proposed by TomoTherapy® System as well as the manual correction of these proposals applied by the radiation therapist. This manual correction aims to further improve the repositioning based on the clinical situation and depends on the structures surrounding the target tumor tissue. The statistical analysis performed on the database aims to define repositioning limits to be used as security and guiding tool for the manual adjustment implemented by the radiation therapist. This tool will participate not only to notify potential repositioning errors but also to further improve patient positioning for optimal treatment.

Keywords: accuracy, IGRT MVCT, image-guided radiotherapy megavoltage computed tomography, statistical analysis, tomotherapy, localization

Procedia PDF Downloads 226

Authors: Jiawen Wang, Qijun Chen

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The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics.

Keywords: image inpainting, generative adversary nets, genetic algorithm, small-sample size

Procedia PDF Downloads 130

Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

Procedia PDF Downloads 348

Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Abdi Djamila, Haffar Hichem

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The poly methylene bleu (PMB) has been successfully electro deposited on fluorine doped tin oxide (FTO) conducting glass as substrate. Its optical, electrical and photoelectrochemical characterizations have been carried out in order to show the performances of such polymer. The deposited film shows a good electric conductivity which is well confirmed by the low gap value determinated optically by UV–vis spectroscopy. Like all polymers the PMB presents an absorption difference in the visible range function of the polarization potential, it is expressed by the strong conjugation at oxidized state but is weakened with leucoform formation at reduced state. The electrochemical analysis of the films permit to show the cyclic voltamperogram with the anodic oxidation and cathodic reduction states of the polymer and to locate the corresponding energy levels HOMO and LUMO of this later. The electrochemical impedance spectroscopy permit to see the conductive character of such film and to calculate important parameters as Rtc and CPE. The study of the photoelectro activity of our polymer shows that under exposure to intermittent light source this later exhibit important photocurrents which enables it to be used in photo organic ells.

Keywords: polymethylene blue, electropolymerization, homo-lumo, photocurrents

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Authors: Sudeepti Dayal, Neelesh Gupta

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Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently.

Keywords: Compression ratio, DWT, SPIHT, DCT

Procedia PDF Downloads 349

Authors: D. Srinivasacharya, G. Madhava Rao

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In this article, the pulsatile flow of blood flow in bifurcated artery with mild stenosis is investigated. Blood is treated to be a micropolar fluid with constant density. The arteries forming bifurcation are assumed to be symmetric about its axes and straight cylinders of restricted length. As the geometry of the stenosed bifurcated artery is irregular, it is changed to regular geometry utilizing the appropriate transformations. The numerical solutions, using the finite difference method, are computed for the flow rate, the shear stress, and the impedance. The influence of time, coupling number, half of the bifurcated angle and Womersley number on shear stress, flow rate and impedance (resistance to the flow) on both sides of the flow divider is shown graphically. It has been observed that the shear stress and flow rate are increasing with increase in the values of Womersley number and bifurcation angle on both sides of the apex. The shear stress is increasing along the inner wall and decreasing along the outer wall of the daughter artery with an increase in the value of coupling number. Further, it has been noticed that the shear stress, flow rate, and impedance are perturbed largely near to the apex in the parent artery due to the presence of backflow near the apex.

Keywords: micropolar fluid, bifurcated artery, stenosis, back flow, secondary flow, pulsatile flow, Womersley number

Procedia PDF Downloads 192

Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

Procedia PDF Downloads 112

Authors: İsmail İnce

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The aim of this study is to determine index and mechanical properties of pyroclastic rock in a practical way by means of electrical resistivity method. For this purpose, electrical resistivity, uniaxial compressive strength, point load strength, P-wave velocity, density and porosity values of 10 different pyroclastic rocks were measured in the laboratory. A simple regression analysis was made among the index-mechanical properties of the samples compatible with electrical resistivity values. A strong exponentially relation was found between index-mechanical properties and electrical resistivity values. The electrical resistivity method can be used to assess the engineering properties of the rock from which it is difficult to obtain regular shaped samples as a non-destructive method.

Keywords: electrical resistivity, index-mechanical properties, pyroclastic rocks, regression analysis

Procedia PDF Downloads 473

Authors: Siraa Ben Ftima, Mourad Talbi, Tahar Ezzedine

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In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations.

Keywords: lifting wavelet transform (LWT), sub-space vectorial decomposition, secure, image watermarking, watermark

Procedia PDF Downloads 276

Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

Abstract:

A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Ahmad Al-Zoabi

Abstract:

Development of research approaches for identifying images of the past in the built environment is at a beginning stage, and a review of the current literature reveals a limited body of research in this area. This study seeks to make a contribution to fill this void. It investigates the theoretical and empirical studies that examine the built environment as a medium for communicating the past in order to understand how images of the past are operationalized in these studies. Findings revealed that image could be operationalized in several ways depending on the focus of the study. Three concerns were addressed in this study when defining the image of the past: (a) to investigate an 'everyday' popular image of the past; (b) to look at the building's image as an integrated part of a larger image for the city; and (c) to find patterns within residents' images of the past. This study concludes that a future study is needed to address the effects of different scales (size and depth of history) of cities and of different cultural backgrounds of images of the past.

Keywords: architecture, built environment, image of the past, research approaches

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

Abstract:

Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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