Search results for: dynamic PET images

Authors: Deepa, Yashbir Singh, Shi Yi Wu, Michael Friebe, Joao Manuel R. S. Tavares, Hu Wei-Chih

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Cardiovascular disease can be detected by measuring the regional and global wall motion of the left ventricle (LV) of the heart; In this study, we designed a dynamic simulation tool using Computed Tomography (CT) images to assess the difference between actual and simulated left ventricular functions. Thirteen healthy subjects were involved in the study with actual and simulated left ventricular functions. In this research, we found the high correlation between actual left ventricular wall motion and simulated left ventricular wall motion. Our results confirm that our simulation tool is feasible for simulating left ventricular motion.

Keywords: cardiac imaging, left-ventricular remodeling, cardiac wall motion, myocardial functions

Procedia PDF Downloads 343

Authors: Lai-Fen Yang

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In this increasingly visual world, how can we best decipher and understand the many ways that our everyday lives are organized around looking practices and the many images we encounter each day? Indeed, how we interact with and interpret visual images is a basic component of human life. Today, however, we are living in one of the most artificial visual and image-saturated cultures in human history, which makes understanding the complex construction and multiple social functions of visual imagery more important than ever before. Themes regarding our experience of a visually pervasive mediated culture, here, termed visual spectacle.

Keywords: visual culture, contemporary, images, literacy

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Ahmed Hammed, Wady Naanaa

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In this paper, we propose a general approach to deal with the problem of a convolutive mixture of images. We use a direct blind source separation method by adding only one non-statistical justified constraint describing the relationships between different mixing matrix at the aim to make its resolution easy. This method can be applied, provided that this constraint is known, to degraded document affected by the overlapping of text-patterns and images. This is due to chemical and physical reactions of the materials (paper, inks,...) occurring during the documents aging, and other unpredictable causes such as humidity, microorganism infestation, human handling, etc. We will demonstrate that this problem corresponds to a convolutive mixture of images. Subsequently, we will show how the validation of our method through numerical examples. We can so obtain clear images from unreadable ones which can be caused by pages superposition, a phenomenon similar to that we find every often in archival documents.

Keywords: blind source separation, convoluted mixture, degraded documents, text-patterns overlapping

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Authors: Ayesha Murtza

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Controlling images develop misinformed behaviors about impoverished Muslim Pakistani women that add to the oppression these Pakistani women endure their whole lives. Meanwhile, patriarchal and stereotypical societies provide an ideological justification for gender, class, and racial oppression, especially for women. Cojoining the concepts of controlling images by Patricia Hill Collins (1990) and binary thinking by Barbara Christian (1987), this paper discusses the ways in which various controlling images of urban and rural women are being presented in Pakistani dramas. These images reinforce an interlocking system of oppression for women in Pakistan. This paper further explores how these controlling images of intersecting components like class, gender, religion, ethnicity, physical appearance, color, and caste normalize hegemonic gendered oppression in society and how men have the same attitude towards women of their family whether they belong to the rural or urban class since they are the product of the same society. It further sheds light on how these matrixes of domination are an inevitable part of Pakistani women’s everyday lives and how these women reinforce survival strategies for coping with all these forms of oppression. By employing the feminist interactional framework, this paper elucidates the role of masculinity, femininity, feminist activism, and traditional knowledge against a monolithic image of Pakistani women. By highlighting these, this paper complicates the role of descriptive and visual images, religion, women’s rights, and the stereotypical role of women in Pakistani dramas.

Keywords: controlling images, oppression, women, Pakistan

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

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Authors: Javed Khan, Aamir Saeed Malik, Nidal Kamel, Sarat Chandra Dass, Azura Mohd Affandi

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Low contrast images can result from the wrong setting of image acquisition or poor illumination conditions. Such images may not be visually appealing and can be difficult for feature extraction. Contrast enhancement of color images can be useful in medical area for visual inspection. In this paper, a new technique is proposed to improve the contrast of color images. The RGB (red, green, blue) color image is transformed into normalized RGB color space. Adaptive histogram equalization technique is applied to each of the three channels of normalized RGB color space. The corresponding channels in the original image (low contrast) and that of contrast enhanced image with adaptive histogram equalization (AHE) are morphed together in proper proportions. The proposed technique is tested on seventy color images of acne patients. The results of the proposed technique are analyzed using cumulative variance and contrast improvement factor measures. The results are also compared with decorrelation stretch. Both subjective and quantitative analysis demonstrates that the proposed techniques outperform the other techniques.

Keywords: contrast enhacement, normalized RGB, adaptive histogram equalization, cumulative variance.

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Authors: Enver Kucukkulahli, Pakize Erdogmus, Kemal Polat

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The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software.

Keywords: image segmentation, clustering, GUI, 2D MRI

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Authors: Alina Trifan, António J. R. Neves

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Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods.

Keywords: bayer image, CFA, lossless compression, image coding standards

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, Rebecca K. Rossi, Yanmin Yuan, Xianpei Li

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The fiscal growth of any country hinges on the prudent administration of water resources. The river Yamuna and Ganga are measured as the life line of India as it affords the needs for life to endure. Earth observation over remote sensing images permits the precise description and identification of ingredients on the superficial from space and airborne platforms. Multiple and heterogeneous image sources are accessible for the same geographical section; multispectral, hyperspectral, radar, multitemporal, and multiangular images. In this paper, a taxonomical learning of the fluvial geomorphological features of river Yamuna and Ganga over doon valley using color fusion of multispectral remote sensing images was performed. Experimental results exhibited that the segmentation based colorization technique stranded on pattern recognition, and color mapping fashioned more colorful and truthful colorized images for geomorphological feature extraction.

Keywords: color fusion, geomorphology, fluvial processes, multispectral images, pattern recognition

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Authors: Gherbi Nabil

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Detection of ground targets in SAR radar images is an important area for radar information processing. In the literature, various algorithms have been discussed in this context. However, most of them are of low robustness and accuracy. To this end, we discuss target detection in SAR images based on compressive sensing. Firstly, traditional SAR image target detection algorithms are discussed, and their limitations are highlighted. Secondly, a compressive sensing method is proposed based on the sparsity of SAR images. Next, the detection problem is solved using Multiple Measurements Vector configuration. Furthermore, a robust Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem. Finally, the detection results obtained using raw complex data are presented. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

Keywords: compressive sensing, raw complex data, synthetic aperture radar, ADMM

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Authors: Dae-Hee Son, Sang-Hee Kang, Soon-Ryul Nam

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In this paper, we develop a dynamic modeling of wind farms in the Jeju power system. The dynamic model of wind farms is developed to study their dynamic effects on the Jeju power system. PSS/E is used to develop the dynamic model of a wind farm composed of 1.5-MW doubly fed induction generators. The output of a wind farm is regulated based on pitch angle control, in which the two controllable parameters are speed and power references. The simulation results confirm that the pitch angle is successfully controlled, regardless of the variation in wind speed and output regulation.

Keywords: dynamic model, Jeju power system, online limitation, pitch angle control, wind farm

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Authors: Mogens Saberi

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This paper presents different design approaches for the design of turbine foundations. In the design process, several unknown factors must be considered such as the soil stiffness at the site. The main static and dynamic loads are presented and the results of a dynamic simulation are presented for a turbine foundation that is currently being built. A turbine foundation is an important part of a power plant since a non-optimal behavior of the foundation can damage the turbine itself and thereby stop the power production with large consequences.

Keywords: dynamic turbine design, harmonic response analysis, practical turbine design experience, concrete foundation

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Authors: Amhimmid Mohammed Saffour, Abdelkader Salama

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Filtering is a part of image enhancement techniques, it is used to enhance certain details such as edges in the image that are relevant to the application. Additionally, filtering can even be used to eliminate unwanted components of noise. Medical images typically contain salt and pepper noise and Poisson noise. This noise appears to the presence of minute grey scale variations within the image. In this paper, different filters techniques namely (Median, Wiener, Rank order3, Rank order5, and Average) were applied on CT medical images (Brain and chest). We using all these filters to remove salt and pepper noise from these images. This type of noise consists of random pixels being set to black or white. Peak Signal to Noise Ratio (PSNR), Mean Square Error r(MSE) and Histogram were used to evaluated the quality of filtered images. The results, which we have achieved shows that, these filters, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patients with no difficulty.

Keywords: CT imaging, median filter, adaptive filter and average filter, MATLAB

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Authors: Mahesh K. Chudasama, Harit K. Raval

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3-roller conical bending process is widely used in the industries for manufacturing of conical sections and shells. It involves static as well dynamic bending stages. Analytical models for prediction of bending force during static as well as dynamic bending stage are available in the literature. In this paper, bending forces required for static bending stage and dynamic bending stages have been compared using the analytical models. It is concluded that force required for dynamic bending is very less as compared to the bending force required during the static bending stage.

Keywords: analytical modeling, cone frustum, dynamic bending, static bending

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Authors: Wei-Na Li, Mei-Lan Piao, Nam Kim

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We proposed a novel computer-generated hologram (CGH) achieved scheme, wherein the CGH is generated from a point cloud which is transformed by a mapping relationship of a series of elemental images captured from a real three-dimensional (3D) object by using a lens array. This scheme is composed of three procedures: mapping from elemental images to point cloud, hologram generation, and hologram display. A mapping method is figured out to achieve a virtual volume date (point cloud) from a series of elemental images. This mapping method consists of two steps. Firstly, the coordinate (x, y) pairs and its appearing number are calculated from the series of sub-images, which are generated from the elemental images. Secondly, a series of corresponding coordinates (x, y, z) are calculated from the elemental images. Then a hologram is generated from the volume data that is calculated by the previous two steps. Eventually, a spatial light modulator (SLM) and a green laser beam are utilized to display this hologram and reconstruct the original 3D object. In this paper, in order to show a more auto stereoscopic display of a real 3D object, we successfully obtained the actual depth data of every discrete point of the real 3D object, and overcame the inherent drawbacks of the depth camera by obtaining point cloud from the elemental images.

Keywords: elemental image, point cloud, computer-generated hologram (CGH), autostereoscopic display

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Authors: Ming-Yang Guo, Cheng-Xian Wu, Wei-Xiang Chen, Chun-Yuan Lin, Yen-Jen Lin, Ann-Shyn Chiang

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With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070.

Keywords: Drosophila driver image, Drosophila neuron images, intersection/union computation, parallel processing, OpenMP

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Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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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.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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Authors: Jawid Ahmad Baktash, Mursal Dawodi, Tomokazu Nagata

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With the rapid growth of Cloud Computing, Formal Methods became a good choice for the refinement of message specification and verification for Dynamic Routing in Cloud Computing. Cloud-based Dynamic Routing is becoming increasingly popular. We propose feedback in Formal Methods for Dynamic Routing and Cloud Computing; the model and topologies show how to send messages from index zero to all others formally. The responsibility of proper verification becomes crucial with Dynamic Routing in the cloud. Formal Methods can play an essential role in the routing and development of Networks, and the testing of distributed systems. Event-B is a formal technique that consists of describing the problem rigorously and introduces solutions or details in the refinement steps. Event-B is a variant of B, designed for developing distributed systems and message passing of the dynamic routing. In Event-B and formal methods, the events consist of guarded actions occurring spontaneously rather than being invoked.

Keywords: cloud, dynamic routing, formal method, Pro-B, event-B

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Authors: Devika Tanna

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'Biometrics' means 'life measurement' but the term is usually associated with the use of unique physiological characteristics to identify an individual. It is important to secure the privacy of digital face image that is stored in central database. To impart privacy to such biometric face images, first, the digital face image is split into two host face images such that, each of it gives no idea of existence of the original face image and, then each cover image is stored in two different databases geographically apart. When both the cover images are simultaneously available then only we can access that original image. This can be achieved by using the XM2VTS and IMM face database, an adaptive algorithm for spatial greyscale. The algorithm helps to select the appropriate host images which are most likely to be compatible with the secret image stored in the central database based on its geometry and appearance. The encryption is done using GEVCS which results in a reconstructed image identical to the original private image.

Keywords: adaptive algorithm, database, host images, privacy, visual cryptography

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Authors: Chantana Insra

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The research “Buddha Images in Mudras Representing Days of a Week: Tactile Texture Design for the Blind” aims to provide original tactile format to institutions for the blind, as supplementary textbooks, to accumulate Buddhist knowledge, so that it could be extracurricular learning. The research studied on 33 students with both total and partial blindness, the latter with the ability to read Braille’s signs, of elementary 4 – 6, who are pursuing their studies on the second semester of the academic year 2013 at Bangkok School for the Blind. The researcher opted samples specifically, studied data acquired from both documents and fieldworks. Those methods must be related to the blind, tactile format production, and Buddha images in mudras representing days of a week. Afterwards, the formats will be analyzed and designed so that there would be 8 format pictures of Buddha images in mudras representing days of the week. Experts will next evaluate the media and try out.

Keywords: blind, tactile texture, Thai Buddha images, Mudras, texture design

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Authors: A. Mudau, T. R. Stacey, R. A. Govender

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This paper reports for the first time the findings on the dynamic compressive strength data of Bushveld Complex brittle rock materials. These rocks were subjected to both quasi-static and impact loading tests to help understand their behaviour both under quasi-static and dynamic loading conditions. Unlike quasi-static tests, characterization of dynamic behaviour of materials is challenging, in particularly brittle rock materials. The split Hopkinson pressure bar (SHPB) results reported for anorthosite and norite showed relatively low values for dynamic compressive strength compared to the quasi-static uniaxial compressive strength data. It was noticed that the dynamic stress conditions were not fully attained during testing, as well as constant strain rate.

Keywords: Bushveld Complex, dynamic comperession, rock brittleness, stress equilibrium

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Authors: Jaqueline Maria Ribeiro Vieira

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Different strategies and tools are available at the oil and gas industry for detecting and analyzing tension and possible fractures in borehole walls. Most of these techniques are based on manual observation of the captured borehole images. While this strategy may be possible and convenient with small images and few data, it may become difficult and suitable to errors when big databases of images must be treated. While the patterns may differ among the image area, depending on many characteristics (drilling strategy, rock components, rock strength, etc.). Previously we developed and proposed a novel strategy capable of detecting patterns at borehole images that may point to regions that have tension and breakout characteristics, based on segmented images. In this work we propose the inclusion of data-mining classification strategies in order to create a knowledge database of the segmented curves. These classifiers allow that, after some time using and manually pointing parts of borehole images that correspond to tension regions and breakout areas, the system will indicate and suggest automatically new candidate regions, with higher accuracy. We suggest the use of different classifiers methods, in order to achieve different knowledge data set configurations.

Keywords: image segmentation, oil well visualization, classifiers, data-mining, visual computer

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Authors: Nancy Elizabeth Naranjo Garcia

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Canoa (1976) by Felipe Cazals is a film that exposes the consequences of power that the Mexican State exercised over the 1968 student movement. The film, in this particular way, approaches the Tlatelolco Massacre from a point of view that takes into consideration the events that led up to it. Nonetheless, the reference to the political tension in Canoa remains ambiguous. Thus, the cinematographic representation refers to an event that leaves space for reflection, and as a consequence leaves evidence of an image that signals the notion of survival as Georges Didi-Huberman points out. In addition to denouncing the oppressive force by the Mexican State, the images in Canoa also emphasize what did not happen in Tlatelolco and its condensation with the student activists. To observe the images that Canoa offers in a new light, this work proposes further exploration with the following questions; How do the images in Canoa narrate? How are the images inserted in the film? In this fashion, a more profound comprehension of the objective and the essence of the images becomes feasible. As a result, it is possible to analyze the images of Canoa with the real killing at San Miguel Canoa in literature. The film visualizes a testimony of the event that once seemed unimaginable, an image that anticipates and structures the proceeding event. Therefore, this study takes a second look at how Canoa considers not only the killing at San Miguel Canoa and the Tlatlelolco Massacre, but goes further on contextualize an unimaginable image.

Keywords: cinematographic representation, student movement, Tlatelolco Massacre, unimaginable image

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Authors: Siti Salbiah Samsudin, Hamzah Arof, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris

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Standard X-ray spine images produced by conventional screen-film technique have a limited field of view. This limitation may obstruct a complete inspection of the spine unless images of different parts of the spine are placed next to each other contiguously to form a complete structure. Another solution to producing a whole spine image is by assembling the digitized x-ray images of its parts automatically using image stitching. This paper presents a new Medical Image Stitching (MIS) method that utilizes Minimum Average Correlation Energy (MACE) filters to identify and merge pairs of x-ray medical images. The effectiveness of the proposed method is demonstrated in two sets of experiments involving two databases which contain a total of 40 pairs of overlapping and non-overlapping spine images. The experimental results are compared to those produced by the Normalized Cross Correlation (NCC) and Phase Only Correlation (POC) methods for comparison. It is found that the proposed method outperforms those of the NCC and POC methods in identifying both the overlapping and non-overlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about two to five times shorter than those of the POC and NCC methods.

Keywords: image stitching, MACE filter, panorama image, scoliosis

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Authors: S. Benchelha, H. Chennaoui, M. Hakdaoui, L. Baidder, H. Mansouri, H. Ejjaaouani, T. Benchelha

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Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys.

Keywords: landslides, False Color Composite (FCC), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Normalized Difference Index (NDI), Normalized Difference Mid Red Index (NDMIDR)

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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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.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Stefania Arguelles Reyes, Octavio José Salcedo Parra, Alberto Acosta López

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This paper presents a web application for the improvement of images through recognition. The web application is based on the analysis of picture-based recognition methods that allow an improvement on the physical appearance of people posting in social networks. The basis relies on the study of tools that can correct or improve some features of the face, with the help of a wide collection of user images taken as reference to build a facial profile. Automatic facial profiling can be achieved with a deeper study of the Object Detection Library. It was possible to improve the initial images with the help of MATLAB and its filtering functions. The user can have a direct interaction with the program and manually adjust his preferences.

Keywords: Matlab, make up, recognition methods, web application

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Authors: Mamadou I. Wade, Henry C. Ogworonjo, Madiha Gul, Mandoye Ndoye, Mohamed Chouikha, Wayne Patterson

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In this paper, we present a novel application of the Paillier cryptographic system to the encryption of RGB (Red Green Blue) images. In this method, an RGB image is first separated into its constituent channel images, and the Paillier encryption function is applied to each of the channels pixel intensity values. Next, the encrypted image is combined and compressed if necessary before being transmitted through an unsecured communication channel. The transmitted image is subsequently recovered by a decryption process. We performed a series of security and performance analyses to the recovered images in order to verify their robustness to security attack. The results show that the proposed image encryption scheme produces highly secured encrypted images.

Keywords: image encryption, Paillier cryptographic system, RBG image encryption, Paillier

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Authors: Victor N. Anisimov, Igor E. Serov, Ksenia M. Kolkova, Natalia V. Galkina

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The problem of memorability of the advertising content is closely connected with the key issues of neuromarketing. The memorability of the advertising content contributes to the marketing effectiveness of the promoted product. Significant directions of studying the phenomenon of memorability are the memorability of the brand (detected through the memorability of the logo) and the memorability of the product offer (detected through the memorization of dynamic audiovisual advertising content - commercial). The aim of this work is to reveal the predictors of memorization of static and dynamic audiovisual stimuli (logos and commercials). An important direction of the research was revealing differences in psychophysiological correlates of memorability between static and dynamic audiovisual stimuli. We assumed that static and dynamic images are perceived in different ways and may have a difference in the memorization process. Objective methods of recording psychophysiological parameters while watching static and dynamic audiovisual materials are well suited to achieve the aim. The electroencephalography (EEG) method was performed with the aim of identifying correlates of the memorability of various stimuli in the electrical activity of the cerebral cortex. All stimuli (in the groups of statics and dynamics separately) were divided into 2 groups – remembered and not remembered based on the results of the questioning method. The questionnaires were filled out by survey participants after viewing the stimuli not immediately, but after a time interval (for detecting stimuli recorded through long-term memorization). Using statistical method, we developed the classifier (statistical model) that predicts which group (remembered or not remembered) stimuli gets, based on psychophysiological perception. The result of the statistical model was compared with the results of the questionnaire. Conclusions: Predictors of the memorability of static and dynamic stimuli have been identified, which allows prediction of which stimuli will have a higher probability of remembering. Further developments of this study will be the creation of stimulus memory model with the possibility of recognizing the stimulus as previously seen or new. Thus, in the process of remembering the stimulus, it is planned to take into account the stimulus recognition factor, which is one of the most important tasks for neuromarketing.

Keywords: memory, commercials, neuromarketing, EEG, branding

Procedia PDF Downloads 251