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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="realistic images"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2942</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: realistic images</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2942</span> Application of Deep Learning in Colorization of LiDAR-Derived Intensity Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edgardo%20V.%20Gubatanga%20Jr.">Edgardo V. Gubatanga Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Joshua%20Salvacion"> Mark Joshua Salvacion</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20LiDAR" title="aerial LiDAR">aerial LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=colorization" title=" colorization"> colorization</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=intensity%20images" title=" intensity images"> intensity images</a> </p> <a href="https://publications.waset.org/abstracts/94116/application-of-deep-learning-in-colorization-of-lidar-derived-intensity-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94116.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">166</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2941</span> Generative Adversarial Network Based Fingerprint Anti-Spoofing Limitations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yehjune%20Heo">Yehjune Heo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anti-spoofing" title="anti-spoofing">anti-spoofing</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint%20recognition" title=" fingerprint recognition"> fingerprint recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a> </p> <a href="https://publications.waset.org/abstracts/131965/generative-adversarial-network-based-fingerprint-anti-spoofing-limitations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131965.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2940</span> Artificial Intelligence-Generated Previews of Hyaluronic Acid-Based Treatments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ciro%20Cursio">Ciro Cursio</a>, <a href="https://publications.waset.org/abstracts/search?q=Giulia%20Cursio"> Giulia Cursio</a>, <a href="https://publications.waset.org/abstracts/search?q=Pio%20Luigi%20Cursio"> Pio Luigi Cursio</a>, <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Cursio"> Luigi Cursio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=hyaluronic%20acid" title=" hyaluronic acid"> hyaluronic acid</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment" title=" treatment"> treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/148414/artificial-intelligence-generated-previews-of-hyaluronic-acid-based-treatments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2939</span> Forensic Comparison of Facial Images for Human Identification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20P.%20Gangwar">D. P. Gangwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of human through facial images has got great importance in forensic science. The video recordings, CCTV footage, passports, driver licenses and other related documents are invariably sent to the laboratory for comparison of the questioned photographs as well as video recordings with suspected photographs/recordings to prove the identity of a person. More than 300 questioned and 300 control photographs received in actual crime cases, received from various investigation agencies, have been compared by me so far using various familiar analysis and comparison techniques such as Holistic comparison, Morphological analysis, Photo-anthropometry and superimposition. On the basis of findings obtained during the examination huge photo exhibits, a realistic and comprehensive technique has been proposed which could be very useful for forensic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CCTV%20Images" title="CCTV Images">CCTV Images</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20features" title=" facial features"> facial features</a>, <a href="https://publications.waset.org/abstracts/search?q=photo-anthropometry" title=" photo-anthropometry"> photo-anthropometry</a>, <a href="https://publications.waset.org/abstracts/search?q=superimposition" title=" superimposition"> superimposition</a> </p> <a href="https://publications.waset.org/abstracts/31353/forensic-comparison-of-facial-images-for-human-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31353.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2938</span> Data Augmentation for Early-Stage Lung Nodules Using Deep Image Prior and Pix2pix</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qasim%20Munye">Qasim Munye</a>, <a href="https://publications.waset.org/abstracts/search?q=Juned%20Islam"> Juned Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Haseeb%20Qureshi"> Haseeb Qureshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Jung"> Syed Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20technology" title="medical technology">medical technology</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=radiology" title=" radiology"> radiology</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer" title=" lung cancer"> lung cancer</a> </p> <a href="https://publications.waset.org/abstracts/150498/data-augmentation-for-early-stage-lung-nodules-using-deep-image-prior-and-pix2pix" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150498.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2937</span> Generating Synthetic Chest X-ray Images for Improved COVID-19 Detection Using Generative Adversarial Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muneeb%20Ullah">Muneeb Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Daishihan"> Daishihan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiadong%20Young"> Xiadong Young</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=CXR" title=" CXR"> CXR</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN." title=" GAN."> GAN.</a> </p> <a href="https://publications.waset.org/abstracts/176073/generating-synthetic-chest-x-ray-images-for-improved-covid-19-detection-using-generative-adversarial-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2936</span> Rhetoric and Renarrative Structure of Digital Images in Trans-Media</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Geng">Yang Geng</a>, <a href="https://publications.waset.org/abstracts/search?q=Anqi%20Zhao"> Anqi Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The misreading theory of Harold Bloom provides a new diachronic perspective as an approach to the consistency between rhetoric of digital technology, dynamic movement of digital images and uncertain meaning of text. Reinterpreting the diachroneity of 'intertextuality' in the context of misreading theory extended the range of the 'intermediality' of transmedia to the intense tension between digital images and symbolic images throughout history of images. With the analogy between six categories of revisionary ratios and six steps of digital transformation, digital rhetoric might be illustrated as a linear process reflecting dynamic, intensive relations between digital moving images and original static images. Finally, it was concluded that two-way framework of the rhetoric of transformation of digital images and reversed served as a renarrative structure to revive static images by reconnecting them with digital moving images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rhetoric" title="rhetoric">rhetoric</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20art" title=" digital art"> digital art</a>, <a href="https://publications.waset.org/abstracts/search?q=intermediality" title=" intermediality"> intermediality</a>, <a href="https://publications.waset.org/abstracts/search?q=misreading%20theory" title=" misreading theory"> misreading theory</a> </p> <a href="https://publications.waset.org/abstracts/100230/rhetoric-and-renarrative-structure-of-digital-images-in-trans-media" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100230.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2935</span> A Review on Light Shafts Rendering for Indoor Scenes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hatam%20H.%20Ali">Hatam H. Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Shahrizal%20Sunar"> Mohd Shahrizal Sunar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoshang%20Kolivand"> Hoshang Kolivand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Azhar%20Bin%20M.%20Arsad"> Mohd Azhar Bin M. Arsad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rendering light shafts is one of the important topics in computer gaming and interactive applications. The methods and models that are used to generate light shafts play crucial role to make a scene more realistic in computer graphics. This article discusses the image-based shadows and geometric-based shadows that contribute in generating volumetric shadows and light shafts, depending on ray tracing, radiosity, and ray marching technique. The main aim of this study is to provide researchers with background on a progress of light scattering methods so as to make it available for them to determine the technique best suited to their goals. It is also hoped that our classification helps researchers find solutions to the shortcomings of each method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=shaft%20of%20lights" title="shaft of lights">shaft of lights</a>, <a href="https://publications.waset.org/abstracts/search?q=realistic%20images" title=" realistic images"> realistic images</a>, <a href="https://publications.waset.org/abstracts/search?q=image-based" title=" image-based"> image-based</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20geometric-based" title=" and geometric-based"> and geometric-based</a> </p> <a href="https://publications.waset.org/abstracts/46822/a-review-on-light-shafts-rendering-for-indoor-scenes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2934</span> Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20A.%20Y.%20Mustafa">Adnan A. Y. Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20images" title="big images">big images</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20images" title=" binary images"> binary images</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20similarity" title=" image similarity"> image similarity</a> </p> <a href="https://publications.waset.org/abstracts/89963/quick-similarity-measurement-of-binary-images-via-probabilistic-pixel-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2933</span> 3D Guided Image Filtering to Improve Quality of Short-Time Binned Dynamic PET Images Using MRI Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tabassum%20Husain">Tabassum Husain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Peng%20Li"> Shen Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhaolin%20Chen"> Zhaolin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper evaluates the usability of 3D Guided Image Filtering to enhance the quality of short-time binned dynamic PET images by using MRI images. Guided image filtering is an edge-preserving filter proposed to enhance 2D images. The 3D filter is applied on 1 and 5-minute binned images. The results are compared with 15-minute binned images and the Gaussian filtering. The guided image filter enhances the quality of dynamic PET images while also preserving important information of the voxels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20PET%20images" title="dynamic PET images">dynamic PET images</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20image%20filter" title=" guided image filter"> guided image filter</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20preservation%20filtering" title=" information preservation filtering"> information preservation filtering</a> </p> <a href="https://publications.waset.org/abstracts/152864/3d-guided-image-filtering-to-improve-quality-of-short-time-binned-dynamic-pet-images-using-mri-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2932</span> Using Machine Learning to Classify Different Body Parts and Determine Healthiness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zachary%20Pan">Zachary Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=body%20part" title="body part">body part</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/160577/using-machine-learning-to-classify-different-body-parts-and-determine-healthiness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160577.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2931</span> Realistic Modeling of the Preclinical Small Animal Using Commercial Software</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Su%20Chul%20Han">Su Chul Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Seungwoo%20Park"> Seungwoo Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the increasing incidence of cancer, the technology and modality of radiotherapy have advanced and the importance of preclinical model is increasing in the cancer research. Furthermore, the small animal dosimetry is an essential part of the evaluation of the relationship between the absorbed dose in preclinical small animal and biological effect in preclinical study. In this study, we carried out realistic modeling of the preclinical small animal phantom possible to verify irradiated dose using commercial software. The small animal phantom was modeling from 4D Digital Mouse whole body phantom. To manipulate Moby phantom in commercial software (Mimics, Materialise, Leuven, Belgium), we converted Moby phantom to DICOM image file of CT by Matlab and two- dimensional of CT images were converted to the three-dimensional image and it is possible to segment and crop CT image in Sagittal, Coronal and axial view). The CT images of small animals were modeling following process. Based on the profile line value, the thresholding was carried out to make a mask that was connection of all the regions of the equal threshold range. Using thresholding method, we segmented into three part (bone, body (tissue). lung), to separate neighboring pixels between lung and body (tissue), we used region growing function of Mimics software. We acquired 3D object by 3D calculation in the segmented images. The generated 3D object was smoothing by remeshing operation and smoothing operation factor was 0.4, iteration value was 5. The edge mode was selected to perform triangle reduction. The parameters were that tolerance (0.1mm), edge angle (15 degrees) and the number of iteration (5). The image processing 3D object file was converted to an STL file to output with 3D printer. We modified 3D small animal file using 3- Matic research (Materialise, Leuven, Belgium) to make space for radiation dosimetry chips. We acquired 3D object of realistic small animal phantom. The width of small animal phantom was 2.631 cm, thickness was 2.361 cm, and length was 10.817. Mimics software supported efficiency about 3D object generation and usability of conversion to STL file for user. The development of small preclinical animal phantom would increase reliability of verification of absorbed dose in small animal for preclinical study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mimics" title="mimics">mimics</a>, <a href="https://publications.waset.org/abstracts/search?q=preclinical%20small%20animal" title=" preclinical small animal"> preclinical small animal</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20printer" title=" 3D printer"> 3D printer</a> </p> <a href="https://publications.waset.org/abstracts/47930/realistic-modeling-of-the-preclinical-small-animal-using-commercial-software" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2930</span> Low Light Image Enhancement with Multi-Stage Interconnected Autoencoders Integration in Pix to Pix GAN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Atif">Muhammad Atif</a>, <a href="https://publications.waset.org/abstracts/search?q=Cang%20Yan"> Cang Yan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20light%20image%20enhancement" title="low light image enhancement">low light image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/180048/low-light-image-enhancement-with-multi-stage-interconnected-autoencoders-integration-in-pix-to-pix-gan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/180048.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2929</span> Reduction of Speckle Noise in Echocardiographic Images: A Survey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fathi%20Kallel">Fathi Kallel</a>, <a href="https://publications.waset.org/abstracts/search?q=Saida%20Khachira"> Saida Khachira</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ben%20Slima"> Mohamed Ben Slima</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Ben%20Hamida"> Ahmed Ben Hamida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speckle noise is a main characteristic of cardiac ultrasound images, it corresponding to grainy appearance that degrades the image quality. For this reason, the ultrasound images are difficult to use automatically in clinical use, then treatments are required for this type of images. Then a filtering procedure of these images is necessary to eliminate the speckle noise and to improve the quality of ultrasound images which will be then segmented to extract the necessary forms that exist. In this paper, we present the importance of the pre-treatment step for segmentation. This work is applied to cardiac ultrasound images. In a first step, a comparative study of speckle filtering method will be presented and then we use a segmentation algorithm to locate and extract cardiac structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20processing" title="medical image processing">medical image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20images" title=" ultrasound images"> ultrasound images</a>, <a href="https://publications.waset.org/abstracts/search?q=Speckle%20noise" title=" Speckle noise"> Speckle noise</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=speckle%20filtering" title=" speckle filtering"> speckle filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=snakes" title=" snakes"> snakes</a> </p> <a href="https://publications.waset.org/abstracts/19064/reduction-of-speckle-noise-in-echocardiographic-images-a-survey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19064.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">530</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2928</span> Subjective Evaluation of Mathematical Morphology Edge Detection on Computed Tomography (CT) Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emhimed%20Saffor">Emhimed Saffor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection "> edge detection </a> </p> <a href="https://publications.waset.org/abstracts/44926/subjective-evaluation-of-mathematical-morphology-edge-detection-on-computed-tomography-ct-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44926.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2927</span> Automatic Method for Classification of Informative and Noninformative Images in Colonoscopy Video</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nidhal%20K.%20Azawi">Nidhal K. Azawi</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20M.%20Gauch"> John M. Gauch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colonoscopy%20classification" title="colonoscopy classification">colonoscopy classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20alignment" title=" image alignment"> image alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/92461/automatic-method-for-classification-of-informative-and-noninformative-images-in-colonoscopy-video" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92461.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">253</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2926</span> Convolutional Neural Networks-Optimized Text Recognition with Binary Embeddings for Arabic Expiry Date Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Lotfy">Mohamed Lotfy</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Soliman"> Ghada Soliman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20character%20recognition" title=" optical character recognition"> optical character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/175474/convolutional-neural-networks-optimized-text-recognition-with-binary-embeddings-for-arabic-expiry-date-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175474.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2925</span> A Way of Converting Color Images to Gray Scale Ones for the Color-Blind: Applying to the part of the Tokyo Subway Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katsuhiro%20Narikiyo">Katsuhiro Narikiyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Shota%20Hashikawa"> Shota Hashikawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color-blind. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them. Therefore we try to convert color images to monochrome images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color-blind" title="color-blind">color-blind</a>, <a href="https://publications.waset.org/abstracts/search?q=JPEG" title=" JPEG"> JPEG</a>, <a href="https://publications.waset.org/abstracts/search?q=monochrome%20image" title=" monochrome image"> monochrome image</a>, <a href="https://publications.waset.org/abstracts/search?q=denoise" title=" denoise"> denoise</a> </p> <a href="https://publications.waset.org/abstracts/2968/a-way-of-converting-color-images-to-gray-scale-ones-for-the-color-blind-applying-to-the-part-of-the-tokyo-subway-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2968.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2924</span> Effective Texture Features for Segmented Mammogram Images Based on Multi-Region of Interest Segmentation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramayanam%20Suresh">Ramayanam Suresh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Nagaraja%20Rao"> A. Nagaraja Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Eswara%20Reddy"> B. Eswara Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20features" title="texture features">texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-ROI%20segmentation" title=" multi-ROI segmentation"> multi-ROI segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmarked%20images" title=" benchmarked images "> benchmarked images </a> </p> <a href="https://publications.waset.org/abstracts/88666/effective-texture-features-for-segmented-mammogram-images-based-on-multi-region-of-interest-segmentation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2923</span> Optimization Query Image Using Search Relevance Re-Ranking Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20G.%20Asmitha%20Chandini">T. G. Asmitha Chandini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Query" title="Query">Query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=re-ranking" title=" re-ranking"> re-ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic" title=" semantic"> semantic</a>, <a href="https://publications.waset.org/abstracts/search?q=signature" title=" signature"> signature</a> </p> <a href="https://publications.waset.org/abstracts/28398/optimization-query-image-using-search-relevance-re-ranking-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">552</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2922</span> ARABEX: Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder and Custom Convolutional Recurrent Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hozaifa%20Zaki">Hozaifa Zaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Soliman"> Ghada Soliman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title=" character recognition"> character recognition</a> </p> <a href="https://publications.waset.org/abstracts/175472/arabex-automated-dotted-arabic-expiration-date-extraction-using-optimized-convolutional-autoencoder-and-custom-convolutional-recurrent-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175472.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2921</span> Automatic Near-Infrared Image Colorization Using Synthetic Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoganathan%20Karthik">Yoganathan Karthik</a>, <a href="https://publications.waset.org/abstracts/search?q=Guhanathan%20Poravi"> Guhanathan Poravi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=near-infrared%20images" title=" near-infrared images"> near-infrared images</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20image%20colorization" title=" automatic image colorization"> automatic image colorization</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data" title=" synthetic data"> synthetic data</a> </p> <a href="https://publications.waset.org/abstracts/185909/automatic-near-infrared-image-colorization-using-synthetic-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185909.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">43</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2920</span> Highly Realistic Facial Expressions of Anthropomorphic Social Agent as a Factor in Solving the 'Uncanny Valley' Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniia%20Nigmatullina">Daniia Nigmatullina</a>, <a href="https://publications.waset.org/abstracts/search?q=Vlada%20Kugurakova"> Vlada Kugurakova</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxim%20Talanov"> Maxim Talanov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a methodology and our plans of anthropomorphic social agent visualization. That includes creation of three-dimensional model of the virtual companion's head and its facial expressions. Talking Head is a cross-disciplinary project of developing of the human-machine interface with cognitive functions. During the creation of a realistic humanoid robot or a character, there might be the ‘uncanny valley’ problem. We think about this phenomenon and its possible causes. We are going to overcome the ‘uncanny valley’ by increasing of realism. This article discusses issues that should be considered when creating highly realistic characters (particularly the head), their facial expressions and speech visualization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anthropomorphic%20social%20agent" title="anthropomorphic social agent">anthropomorphic social agent</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20animation" title=" facial animation"> facial animation</a>, <a href="https://publications.waset.org/abstracts/search?q=uncanny%20valley" title=" uncanny valley"> uncanny valley</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20modeling" title=" 3D modeling"> 3D modeling</a> </p> <a href="https://publications.waset.org/abstracts/41558/highly-realistic-facial-expressions-of-anthropomorphic-social-agent-as-a-factor-in-solving-the-uncanny-valley-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41558.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">290</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2919</span> Comparison of Vessel Detection in Standard vs Ultra-WideField Retinal Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maher%20un%20Nisa">Maher un Nisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahsan%20Khawaja"> Ahsan Khawaja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retinal imaging with Ultra-WideField (UWF) view technology has opened up new avenues in the field of retinal pathology detection. Recent developments in retinal imaging such as Optos California Imaging Device helps in acquiring high resolution images of the retina to help the Ophthalmologists in diagnosing and analyzing eye related pathologies more accurately. This paper investigates the acquired retinal details by comparing vessel detection in standard 450 color fundus images with the state of the art 2000 UWF retinal images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20fundus" title="color fundus">color fundus</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20images" title=" retinal images"> retinal images</a>, <a href="https://publications.waset.org/abstracts/search?q=ultra-widefield" title=" ultra-widefield"> ultra-widefield</a>, <a href="https://publications.waset.org/abstracts/search?q=vessel%20detection" title=" vessel detection"> vessel detection</a> </p> <a href="https://publications.waset.org/abstracts/33520/comparison-of-vessel-detection-in-standard-vs-ultra-widefield-retinal-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2918</span> Fuzzy Decision Support System for Human-Realistic Overtaking in Railway Traffic Simulations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tom%C3%A1%C5%A1%20Vy%C4%8D%C3%ADtal">Tomáš Vyčítal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a simulation model of a railway system it is important, besides other crucial algorithms, to have correct behaviour of train overtaking in stochastic conditions. This problem is being addressed in many simulation tools focused on railway traffic, however these are not very human-realistic. The goal of this paper is to create a more human-realistic overtaking decision support system for the use in railway traffic simulations. A fuzzy system has been chosen for this task as fuzzy systems are well-suited for human-like decision making. The fuzzy system designed takes into account timetables, train positions, delays and buffer times as inputs and provides an instruction to overtake or not overtake. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision-making%20support" title="decision-making support">decision-making support</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20systems" title=" fuzzy systems"> fuzzy systems</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=railway" title=" railway"> railway</a>, <a href="https://publications.waset.org/abstracts/search?q=transport" title=" transport"> transport</a> </p> <a href="https://publications.waset.org/abstracts/156626/fuzzy-decision-support-system-for-human-realistic-overtaking-in-railway-traffic-simulations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156626.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2917</span> Energy Consumption Models for Electric Vehicles: Survey and Proposal of a More Realistic Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Sagaama">I. Sagaama</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kechiche"> A. Kechiche</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Trojet"> W. Trojet</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Kamoun"> F. Kamoun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Replacing combustion engine vehicles by electric vehicles (EVs) is a major step in recent years due to their potential benefits. Battery autonomy and charging processes are still a big issue for that kind of vehicles. Therefore, reducing the energy consumption of electric vehicles becomes a necessity. Many researches target introducing recent information and communication technologies in EVs in order to propose reducing energy consumption services. Evaluation of realistic scenarios is a big challenge nowadays. In this paper, we will elaborate a state of the art of different proposed energy consumption models in the literature, then we will present a comparative study of these models, finally, we will extend previous works in order to propose an accurate and realistic energy model for calculating instantaneous power consumption of electric vehicles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title="electric vehicle">electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicular%20networks" title=" vehicular networks"> vehicular networks</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20models" title=" energy models"> energy models</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20simulation" title=" traffic simulation"> traffic simulation</a> </p> <a href="https://publications.waset.org/abstracts/69264/energy-consumption-models-for-electric-vehicles-survey-and-proposal-of-a-more-realistic-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69264.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2916</span> Enhancement of X-Rays Images Intensity Using Pixel Values Adjustments Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousif%20Mohamed%20Y.%20Abdallah">Yousif Mohamed Y. Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Razan%20Manofely"> Razan Manofely</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajab%20M.%20Ben%20Yousef"> Rajab M. Ben Yousef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enhancement" title="enhancement">enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=x-rays" title=" x-rays"> x-rays</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel%20intensity%20values" title=" pixel intensity values"> pixel intensity values</a>, <a href="https://publications.waset.org/abstracts/search?q=MatLab" title=" MatLab"> MatLab</a> </p> <a href="https://publications.waset.org/abstracts/31031/enhancement-of-x-rays-images-intensity-using-pixel-values-adjustments-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2915</span> Filtering and Reconstruction System for Grey-Level Forensic Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahd%20Aljarf">Ahd Aljarf</a>, <a href="https://publications.waset.org/abstracts/search?q=Saad%20Amin"> Saad Amin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Images are important source of information used as evidence during any investigation process. Their clarity and accuracy is essential and of the utmost importance for any investigation. Images are vulnerable to losing blocks and having noise added to them either after alteration or when the image was taken initially, therefore, having a high performance image processing system and it is implementation is very important in a forensic point of view. This paper focuses on improving the quality of the forensic images. For different reasons packets that store data can be affected, harmed or even lost because of noise. For example, sending the image through a wireless channel can cause loss of bits. These types of errors might give difficulties generally for the visual display quality of the forensic images. Two of the images problems: noise and losing blocks are covered. However, information which gets transmitted through any way of communication may suffer alteration from its original state or even lose important data due to the channel noise. Therefore, a developed system is introduced to improve the quality and clarity of the forensic images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20filtering" title="image filtering">image filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20reconstruction" title=" image reconstruction"> image reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=forensic%20images" title=" forensic images"> forensic images</a> </p> <a href="https://publications.waset.org/abstracts/15654/filtering-and-reconstruction-system-for-grey-level-forensic-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2914</span> Mage Fusion Based Eye Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Ashit">Ahmed Ashit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image fusion is a significant and efficient image processing method used for detecting different types of tumors. This method has been used as an effective combination technique for obtaining high quality images that combine anatomy and physiology of an organ. It is the main key in the huge biomedical machines for diagnosing cancer such as PET-CT machine. This thesis aims to develop an image analysis system for the detection of the eye tumor. Different image processing methods are used to extract the tumor and then mark it on the original image. The images are first smoothed using median filtering. The background of the image is subtracted, to be then added to the original, results in a brighter area of interest or tumor area. The images are adjusted in order to increase the intensity of their pixels which lead to clearer and brighter images. once the images are enhanced, the edges of the images are detected using canny operators results in a segmented image comprises only of the pupil and the tumor for the abnormal images, and the pupil only for the normal images that have no tumor. The images of normal and abnormal images are collected from two sources: “Miles Research” and “Eye Cancer”. The computerized experimental results show that the developed image fusion based eye tumor detection system is capable of detecting the eye tumor and segment it to be superimposed on the original image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=eye%20tumor" title=" eye tumor"> eye tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=canny%20operators" title=" canny operators"> canny operators</a>, <a href="https://publications.waset.org/abstracts/search?q=superimposed" title=" superimposed"> superimposed</a> </p> <a href="https://publications.waset.org/abstracts/30750/mage-fusion-based-eye-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2913</span> Multiscale Simulation of Absolute Permeability in Carbonate Samples Using 3D X-Ray Micro Computed Tomography Images Textures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Jouini">M. S. Jouini</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Al-Sumaiti"> A. Al-Sumaiti</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tembely"> M. Tembely</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Rahimov"> K. Rahimov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Characterizing rock properties of carbonate reservoirs is highly challenging because of rock heterogeneities revealed at several length scales. In the last two decades, the Digital Rock Physics (DRP) approach was implemented successfully in sandstone rocks reservoirs in order to understand rock properties behaviour at the pore scale. This approach uses 3D X-ray Microtomography images to characterize pore network and also simulate rock properties from these images. Even though, DRP is able to predict realistic rock properties results in sandstone reservoirs it is still suffering from a lack of clear workflow in carbonate rocks. The main challenge is the integration of properties simulated at different scales in order to obtain the effective rock property of core plugs. In this paper, we propose several approaches to characterize absolute permeability in some carbonate core plugs samples using multi-scale numerical simulation workflow. In this study, we propose a procedure to simulate porosity and absolute permeability of a carbonate rock sample using textures of Micro-Computed Tomography images. First, we discretize X-Ray Micro-CT image into a regular grid. Then, we use a textural parametric model to classify each cell of the grid using supervised classification. The main parameters are first and second order statistics such as mean, variance, range and autocorrelations computed from sub-bands obtained after wavelet decomposition. Furthermore, we fill permeability property in each cell using two strategies based on numerical simulation values obtained locally on subsets. Finally, we simulate numerically the effective permeability using Darcy’s law simulator. Results obtained for studied carbonate sample shows good agreement with the experimental property. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiscale%20modeling" title="multiscale modeling">multiscale modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=permeability" title=" permeability"> permeability</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a>, <a href="https://publications.waset.org/abstracts/search?q=micro-tomography%20images" title=" micro-tomography images"> micro-tomography images</a> </p> <a href="https://publications.waset.org/abstracts/76794/multiscale-simulation-of-absolute-permeability-in-carbonate-samples-using-3d-x-ray-micro-computed-tomography-images-textures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=realistic%20images&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=realistic%20images&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=realistic%20images&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=realistic%20images&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=realistic%20images&page=6">6</a></li> <li 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