Search results for: image process

Authors: Usha Dinakaran, Richa Ganguly

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Gastronomic tourism has evolved into a prominent niche within the travel industry, with tourists increasingly seeking unique culinary experiences as a primary motivation for their journeys. This research explores the intricate relationship between gastronomic tourism and its profound influence on the overall image of travel destinations. It delves into the multifaceted aspects of culinary experiences, tourists' perceptions, and the preservation of cultural identity, all of which play pivotal roles in shaping a destination's image. The primary aim of this study is to comprehensively examine the interplay between gastronomy and tourism, specifically focusing on its impact on destination image. The research seeks to achieve the following objectives: (1) Investigate how tourists perceive and engage with gastronomic tourism experiences. (2) Understand the significance of food in shaping the tourism image. (3.) Explore the connection between gastronomy and the destination's cultural identity Quantify the relationship between tourists' engagement in co-creation activities related to gastronomic tourism and their overall satisfaction with the quality of their culinary experiences. To achieve these objectives, a mixed-method research approach will be employed, including surveys, interviews, and content analysis. Data will be collected from tourists visiting diverse destinations known for their culinary offerings. This research anticipates uncovering valuable insights into the nexus between gastronomic tourism and destination image. It is expected to shed light on how tourists' perceptions of culinary experiences impact their overall perception of a destination. Additionally, the study aims to identify factors influencing tourist satisfaction and how cultural identity is preserved and promoted through gastronomic tourism. The findings of this research hold practical implications for destination marketers and stakeholders. Understanding the symbiotic relationship between gastronomy and tourism can guide the development of more targeted marketing strategies. Furthermore, promoting co-creation activities can enhance tourists' culinary experiences and contribute to the positive image of destinations.This study contributes to the growing body of knowledge regarding gastronomic tourism by consolidating insights from various studies and offering a comprehensive perspective on its impact on destination image. It offers a platform for future research in this domain and underscores the importance of culinary experiences in contemporary travel. In conclusion, this research endeavors to illuminate the dynamic interplay between gastronomic tourism and destination image, providing valuable insights for both academia and industry stakeholders in the field of tourism and hospitality.

Keywords: gastronomy, tourism, destination image, culinary

Procedia PDF Downloads 74

Authors: Jie Huo, Jonathan Wu

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Segmentation of anatomical structures in medical images is essential for scientific inquiry into the complex relationships between biological structure and clinical diagnosis, treatment and assessment. As a method of incorporating the prior knowledge and the anatomical structure similarity between a target image and atlases, multi-atlas segmentation has been successfully applied in segmenting a variety of medical images, including the brain, cardiac, and abdominal images. The basic idea of multi-atlas segmentation is to transfer the labels in atlases to the coordinate of the target image by matching the target patch to the atlas patch in the neighborhood. However, this technique is limited by the pairwise registration between target image and atlases. In this paper, a novel multi-atlas segmentation approach is proposed by introducing a dynamic energy model. First, the target is mapped to each atlas image by minimizing the dynamic energy function, then the segmentation of target image is generated by weighted fusion based on the energy. The method is tested on MICCAI 2012 Multi-Atlas Labeling Challenge dataset which includes 20 target images and 15 atlases images. The paper also analyzes the influence of different parameters of the dynamic energy model on the segmentation accuracy and measures the dice coefficient by using different feature terms with the energy model. The highest mean dice coefficient obtained with the proposed method is 0.861, which is competitive compared with the recently published method.

Keywords: brain MRI segmentation, dynamic energy model, multi-atlas segmentation, energy minimization

Procedia PDF Downloads 336

Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

Procedia PDF Downloads 90

Authors: Anocha Rojanapanich

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This research investigates the effect of financial accounting information and non-financial accounting reports on corporate credibility via strength of board of directors and market environment volatility as moderating effect. Data in this research is collected by questionnaire form non-financial companies listed on the Stock Exchange of Thailand. Multiple regression statistic technique is used for analyzing the data. Results find that firms with greater financial accounting information reports and non-financial accounting information reports will gain greater corporate credibility. Therefore, the corporate reporting has the value for the firms. Moreover, the strength of board of directors will positively moderate the financial and non-financial accounting information reports and corporate credibility relationship. And market environment volatility will negatively moderate the financial and nonfinancial accounting information reports and corporate credibility relationship and the contribution of accounting information reports on corporate credibility is generated to the corporate image. That is the corporate image has affected by corporate credibility.

Keywords: corporate credibility, financial and non-financial reports, firms performance, corporate image

Procedia PDF Downloads 297

Authors: M. Dhana Lakshmi, S. Sakthivel Murugan

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As light passes from source to observer in the water medium, it is scattered by the suspended particulate matter. This scattering effect will plague the captured images with non-uniform illumination, blurring details, halo artefacts, weak edges, etc. To overcome this, pixel normalization with an Amended Unsharp Mask (AUM) filter is proposed to enhance the degraded image. To validate the robustness of the proposed technique irrespective of atmospheric light, the considered datasets are collected on dual locations. For those images, the maxima and minima pixel intensity value is computed and normalized; then the AUM filter is applied to strengthen the blurred edges. Finally, the enhanced image is obtained with good illumination and contrast. Thus, the proposed technique removes the effect of scattering called de-hazing and restores the perceptual information with enhanced edge detail. Both qualitative and quantitative analyses are done on considering the standard non-reference metric called underwater image sharpness measure (UISM), and underwater image quality measure (UIQM) is used to measure color, sharpness, and contrast for both of the location images. It is observed that the proposed technique has shown overwhelming performance compared to other deep-based enhancement networks and traditional techniques in an adaptive manner.

Keywords: underwater drone imagery, pixel normalization, thresholding, masking, unsharp mask filter

Procedia PDF Downloads 194

Authors: Mai Anh Pham Ho

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Ao Dai is the traditional dress of Vietnamese women that consists of a long tunic with slits on either side and wide trousers. This is the Vietnamese national costume which most common worn by women in daily life. The Vietnamese men may wear Ao Dai on special occasions like New Year Eve or Wedding Ceremony. Ao Dai is one of the few Vietnamese words that appear in English language dictionaries. Nowadays, there are variations in modern Ao Dai that consist of a short tunic on knee and slim trousers with the other materials like kaki or jeans. This paper aims to apply Pop art pattern on modern Ao Dai through the image of Vietnamese women by modifying the creation process of fashion design. It reflects on how modern culture is involved in Ao Dai and how it affects on fashion design. The research method of this paper is done through surveying the various examples of technological applications to fashion design, then the pop art pattern with the image of Vietnamese women is applied on modern Ao Dai. The results of this paper have shown through the collection of modern Ao Dai with three artworks applied the pop art pattern. In conclusion, the role of fashion technology supports and evolves the traditional value in order to establish the Vietnamese national personality as well as distinguish to other cultural values in the world.

Keywords: pop art pattern, Vietnamese national costume, modern ao dai, fashion design

Procedia PDF Downloads 282

Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Sohail Imran, Tariq Mahmood

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In complex healthcare diagnostic investigation process, medical practitioners have to focus on ways to standardize their processes to perform high quality care and optimize the time and costs. Process mining techniques can be applied to extract process related knowledge from data without considering causal and dynamic dependencies in business domain and processes. The application of process mining is effective in diagnostic investigation. It is very helpful where a treatment gives no dispositive evidence favoring it. In this paper, we applied process mining to discover important process flow of diagnostic investigation for hepatitis patients. This approach has some benefits which can enhance the quality and efficiency of diagnostic investigation processes.

Keywords: process mining, healthcare, diagnostic investigation process, process flow

Procedia PDF Downloads 523

Authors: Mariela Gonzalez, Zoraide Lugli, Eleonora Vivas, Rosana Guzmán

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The objective was to determine the predictive capacity of self-efficacy perceived for weight control, locus of weight control and skills of weight self-management in the dissatisfaction of the body image in obese people who attend treatment. Sectional study conducted in the city of Maracay, Venezuela, with 243 obese who attend to treatment, 173 of the feminine gender and 70 of the male, with ages ranging between 18 and 57 years old. The sample body mass index ranged between 29.39 and 44.14. The following instruments were used: The Body Shape Questionnaire (BSQ), the inventory of body weight self-regulation, The Inventory of self-efficacy in the regulation of body weight and the Inventory of the Locus of weight control. Calculating the descriptive statistics and of central tendency, coefficients of correlation and multiple regression; it was found that a low ‘perceived Self-efficacy in the weight control’ and a high ‘Locus of external control’, predict the dissatisfaction with body image in obese who attend treatment. The findings are a first approximation to give an account of the importance of the personal control variables in the study of the psychological grief on the overweight individual.

Keywords: dissatisfaction with body image, obese people, personal control, psychological variables

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Authors: Sotiris Papantonopoulos, Marianna Bortziou

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This study aimed at developing a new package of saffron using emotional design and specifically the Kansei Engineering method. Kansei Engineering is a proactive product development methodology, which aims to improve the product development process and to translate consumers' feelings and image of a product into design elements. A survey was conducted with two major purposes: (1) to determine the target group of saffron use and to collect information about the adequacy of the product’s promotion and the importance of its packaging, (2) to collect the most important properties of a package according to consumers and to evaluate the existing saffron packages according to these properties (benchmarking). The interaction with the general public conducted by the distribution of online questionnaires and personal interviews as well as the statistical analysis of the results were performed using the SPSS software. The results of the survey were used in all stages of Kansei Engineering. Based on the results, a new saffron package was designed by using various designing and image processing software. This improved package is expected to achieve a better promotion and increased sales of the product.

Keywords: design, emotional design, Kansei Engineering, packaging, saffron

Procedia PDF Downloads 162

Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: A. Vani, M. N. Mamatha

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Visual signal processing in human beings occurs in the occipital lobe of the brain. The signals that are generated in the brain are universal for all the human beings and they are called Visual Evoked Potential (VEP). Generally, the visually impaired people lose sight because of severe damage to only the eyes natural photo sensors, but the occipital lobe will still be functioning. In this paper, a technique of artificially generating VEP is proposed to enhance the visual ability of the subject. The system uses the electrical photoreceptors to capture image, process the image, to detect and recognize the subject or object. This voltage is further processed and can transmit wirelessly to a BIOMEMS implanted into occipital lobe of the patient’s brain. The proposed BIOMEMS consists of array of electrodes that generate the neuron potential which is similar to VEP of normal people. Thus, the neurons get the visual data from the BioMEMS which helps in generating partial vision or sight for the visually challenged patient. 

Keywords: BioMEMS, neuro-prosthetic, openvibe, visual evoked potential

Procedia PDF Downloads 315

Authors: Han-Wei Shih, Yao-Nan Wang, Ko-Tung Chang, Lung-Ming Fu

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A multi-channel cell growth real-time monitor and evaluation system using charge-coupled device (CCD) sensors with 40X lens integrating a NI LabVIEW image processing program is proposed and demonstrated. The LED light source control of monitor system is utilizing 8051 microprocessor integrated with NI LabVIEW software. In this study, the same concentration RAW264.7 cells growth rate and morphology in four different culture conditions (DMEM, LPS, G1, G2) were demonstrated. The real-time cells growth image was captured and analyzed by NI Vision Assistant every 10 minutes in the incubator. The image binarization technique was applied for calculating cell doubling time and cell division index. The cells doubling time and cells division index of four group with DMEM, LPS, LPS+G1, LPS+G2 are 12.3 hr,10.8 hr,14.0 hr,15.2 hr and 74.20%, 78.63%, 69.53%, 66.49%. The image magnification of multi-channel CCDs cell real-time monitoring system is about 100X~200X which compares with the traditional microscope.

Keywords: charge-coupled device (CCD), RAW264.7, doubling time, division index

Procedia PDF Downloads 358

Authors: Saman Ghaffarian, Ilgin Gökaşar

Abstract:

This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

Procedia PDF Downloads 379

Authors: El Asnaoui Khalid, Chawki Youness, Aksasse Brahim, Ouanan Mohammed

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In these recent decades, the important and fast growth in the development and demand of multimedia products is contributing to an insufficient in the bandwidth of device and network storage memory. Consequently, the theory of data compression becomes more significant for reducing the data redundancy in order to save more transfer and storage of data. In this context, this paper addresses the problem of the lossless and the near-lossless compression of images. This proposed method is based on Block SVD Power Method that overcomes the disadvantages of Matlab's SVD function. The experimental results show that the proposed algorithm has a better compression performance compared with the existing compression algorithms that use the Matlab's SVD function. In addition, the proposed approach is simple and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.

Keywords: image compression, SVD, block SVD power method, lossless compression, near lossless

Procedia PDF Downloads 387

Authors: Yongseok Kwon, Woowon Jeong, Eunjin Cho, Sangkug Chung, Kyehan Rhee

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Steady streaming flow fields induced by a 500 um bubble oscillating at 12 kHz were measured using microscopic particle image velocimetry (PIV). The accuracy of velocity measurement using a micro PIV system was checked by comparing the measured velocity fields with the theoretical velocity profiles in fully developed laminar flow. The steady streaming flow velocities were measured in the saggital plane of the bubble attached on the wall. Measured velocity fields showed upward jet flow with two symmetric counter-rotating vortices, and the maximum streaming velocity was about 12 mm/s, which was within the velocity ranges measured by other researchers. The measured streamlines were compared with the analytic solution, and they also showed a reasonable agreement.

Keywords: oscillating bubble, particle image velocimetry, microstreaming, vortices,

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Authors: Vahid Bayrami Rad

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Ever since humans developed a collective way of life to the development of urbanization, the concern of security has always been considered one of the most important challenges of life. To protect property, locks have always been a practical tool. With the advancement of technology, the form of locks has changed from mechanical to electric. One of the most widely used fields of using artificial intelligence is its application in the technology of surveillance security systems. Currently, the technologies used in smart anti-theft door handles are one of the most potential fields for using artificial intelligence. Artificial intelligence has the possibility to learn, calculate, interpret and process by analyzing data with the help of algorithms and mathematical models and make smart decisions. We will use Arduino board to process data.

Keywords: arduino board, artificial intelligence, image processing, solenoid lock

Procedia PDF Downloads 69

Authors: Spanova Yerkezhan, Amantay Ayan, Alimzhanova Laura

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This article discusses the concept of rebranding and its relationship to cybersecurity. Rebranding is the process of changing the appearance and image of a company or organization in order to appeal to new customers or change the perception of a company. It can be a powerful tool for businesses looking to renew their reputation or expand into new markets. In today's digital age, companies increasingly rely on technology and the internet to conduct business; rebranding can also present significant cybersecurity risks. This is because a rebranding effort can create new vulnerabilities for companies, particularly in terms of their online presence. This article explores the potential hazards associated with rebranding and provides recommendations for mitigating those risks. It also highlights the importance of considering cybersecurity in the rebranding process and how it can be integrated into the overall strategy for a successful and secure rebranding.

Keywords: rebranding, cybersecurity, cyberattack, logo, vulnerability

Procedia PDF Downloads 166

Authors: Nadia Ben Youssef, Aicha Bouzid

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Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach.

Keywords: gradient, edge detection, color image, quaternion

Procedia PDF Downloads 234

Authors: Okhee Woo

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Purpose: With increasing concern of individual radiation exposure doses, studies analyzing radiation dosage in breast imaging modalities are required. Aim of this study is to compare radiation dosage and image quality between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM). Methods and Materials: 303 patients (mean age 52.1 years) who studied DBT and FFDM were retrospectively reviewed. Radiation dosage data were obtained by radiation dosage scoring and monitoring program: Radimetrics (Bayer HealthCare, Whippany, NJ). Entrance dose and mean glandular doses in each breast were obtained in both imaging modalities. To compare the image quality of DBT with two-dimensional synthesized mammogram (2DSM) and FFDM, 5-point scoring of lesion clarity was assessed and the better modality between the two was selected. Interobserver performance was compared with kappa values and diagnostic accuracy was compared using McNemar test. The parameters of radiation dosages (entrance dose, mean glandular dose) and image quality were compared between two modalities by using paired t-test and Wilcoxon rank sum test. Results: For entrance dose and mean glandular doses for each breasts, DBT had lower values compared with FFDM (p-value < 0.0001). Diagnostic accuracy did not have statistical difference, but lesion clarity score was higher in DBT with 2DSM and DBT was chosen as a better modality compared with FFDM. Conclusion: DBT showed lower radiation entrance dose and also lower mean glandular doses to both breasts compared with FFDM. Also, DBT with 2DSM had better image quality than FFDM with similar diagnostic accuracy, suggesting that DBT may have a potential to be performed as an alternative to FFDM.

Keywords: radiation dose, DBT, digital mammography, image quality

Procedia PDF Downloads 349

Authors: Parimalah Velo, Ahmad Zakaria

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Introduction: Monte Carlo simulations of preclinical imaging systems allow opportunity to enable new research that could range from designing hardware up to discovery of new imaging application. The simulation system which could accurately model an imaging modality provides a platform for imaging developments that might be inconvenient in physical experiment systems due to the expense, unnecessary radiation exposures and technological difficulties. The aim of present study is to validate the Monte Carlo simulation of thyroid phantom imaging using Geant4-GATE for Siemen’s e-cam single head gamma camera. Upon the validation of the gamma camera simulation model by comparing physical characteristic such as energy resolution, spatial resolution, sensitivity, and dead time, the GATE simulation of thyroid phantom imaging is carried out. Methods: A thyroid phantom is defined geometrically which comprises of 2 lobes with 80mm in diameter, 1 hot spot, and 3 cold spots. This geometry accurately resembling the actual dimensions of thyroid phantom. A planar image of 500k counts with 128x128 matrix size was acquired using simulation model and in actual experimental setup. Upon image acquisition, quantitative image analysis was performed by investigating the total number of counts in image, the contrast of the image, radioactivity distributions on image and the dimension of hot spot. Algorithm for each quantification is described in detail. The difference in estimated and actual values for both simulation and experimental setup is analyzed for radioactivity distribution and dimension of hot spot. Results: The results show that the difference between contrast level of simulation image and experimental image is within 2%. The difference in the total count between simulation and actual study is 0.4%. The results of activity estimation show that the relative difference between estimated and actual activity for experimental and simulation is 4.62% and 3.03% respectively. The deviation in estimated diameter of hot spot for both simulation and experimental study are similar which is 0.5 pixel. In conclusion, the comparisons show good agreement between the simulation and experimental data.

Keywords: gamma camera, Geant4 application of tomographic emission (GATE), Monte Carlo, thyroid imaging

Procedia PDF Downloads 271

Authors: Yindi Zhao, Yun Zhang, Silu Xia, Lixin Wu

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The contours and areas of water surfaces, especially lakes, often change due to natural disasters and construction activities. It is an effective way to extract and update water contours from satellite images using image processing algorithms. However, to produce optimal water surface contours that are close to true boundaries is still a challenging task. This paper compares the performances of three different level set models, including the Chan-Vese (CV) model, the signed pressure force (SPF) model, and the region-scalable fitting (RSF) energy model for extracting lake contours. After experiment testing, it is indicated that the RSF model, in which a region-scalable fitting (RSF) energy functional is defined and incorporated into a variational level set formulation, is superior to CV and SPF, and it can get desirable contour lines when there are “holes” in the regions of waters, such as the islands in the lake. Therefore, the RSF model is applied to extracting lake contours from Landsat satellite images. Four temporal Landsat satellite images of the years of 2000, 2005, 2010, and 2014 are used in our study. All of them were acquired in May, with the same path/row (121/036) covering Xuzhou City, Jiangsu Province, China. Firstly, the near infrared (NIR) band is selected for water extraction. Image registration is conducted on NIR bands of different temporal images for information update, and linear stretching is also done in order to distinguish water from other land cover types. Then for the first temporal image acquired in 2000, lake contours are extracted via the RSF model with initialization of user-defined rectangles. Afterwards, using the lake contours extracted the previous temporal image as the initialized values, lake contours are updated for the current temporal image by means of the RSF model. Meanwhile, the changed and unchanged lakes are also detected. The results show that great changes have taken place in two lakes, i.e. Dalong Lake and Panan Lake, and RSF can actually extract and effectively update lake contours using multi-temporal satellite image.

Keywords: level set model, multi-temporal image, lake contour extraction, contour update

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Authors: M. Y. Lee, S. H. Park, S. M. Yu, H. S. Jo, C. G. Song

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Photoacoustic imaging is the imaging technology that combines the optical imaging with ultrasound. It also provides the high contrast and resolution due to optical and ultrasound imaging, respectively. For these reasons, many studies take experiment in order to apply this method for many diagnoses. We developed the real-time photoacoustic tomography (PAT) system using linear-ultrasound transducer. In this study, we conduct the experiment using swine and detect the blood of carotid artery and femoral artery. We measured the blood of femoral and carotid artery of swine and reconstructed the image using 950nm due to the HbO₂ absorption coefficient. The photoacoustic image is overlaid with ultrasound image in order to match the position. In blood of artery, major composition of blood is HbO₂. In this result, we can measure the blood of artery.

Keywords: photoacoustic tomography, swine artery, carotid artery, femoral artery

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Authors: Nour-Elhouda Lecheheb

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Despite substantial tourism potentials, the Algerian tourism industry has faced a number of challenges, including the government heavy dependence on the energy sector, negative perception in the West, and a lack of effective resource management and marketing. This paper attempts to discuss the challenges hindering the development of the Algerian tourism industry from the perspective of the official tourism suppliers in Algeria both in the public and private sectors. A total of 10 semi-structured interviews were conducted during a field-trip to Algiers, Algeria, in September 2019. From the analysis of the interviews, it is evident that the Algerian tourism suppliers face a number of challenges mainly the country’s negative image in the West and a significant lack of political and financial support to contest this negative image effectively and sufficiently.

Keywords: Algerian tourism, destination development, destination image, tourism suppliers

Procedia PDF Downloads 258

Authors: Erfan Niazi, Marianne Fenech

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Red Blood Cells (RBCs) or erythrocytes tend to form chain-like aggregates under low shear rate called rouleaux. This is a reversible process and rouleaux disaggregate in high shear rates. Therefore, RBCs aggregation occurs in the microcirculation where low shear rates are present but does not occur under normal physiological conditions in large arteries. Numerical modeling of RBCs interactions is fundamental in analytical models of a blood flow in microcirculation. Population Balance Modeling (PBM) is particularly useful for studying problems where particles agglomerate and break in a two phase flow systems to find flow characteristics. In this method, the elementary particles lose their individual identity due to continuous destructions and recreations by break-up and agglomeration. The aim of this study is to find RBCs aggregation in a dynamic situation. Simplified PBM was used previously to find the aggregation rate on a static observation of the RBCs aggregation in a drop of blood under the microscope. To find aggregation rate in a dynamic situation we propose an experimental set up testing RBCs sedimentation. In this test, RBCs interact and aggregate to form rouleaux. In this configuration, disaggregation can be neglected due to low shear stress. A high-speed camera is used to acquire video-microscopic pictures of the process. The sizes of the aggregates and velocity of sedimentation are extracted using an image processing techniques. Based on the data collection from 5 healthy human blood samples, the aggregation rate was estimated as 2.7x10³(±0.3 x10³) 1/s.

Keywords: red blood cell, rouleaux, microfluidics, image processing, population balance modeling

Procedia PDF Downloads 355

Authors: Yaolin Tian, Shanxiong Chen, Fujia Zhao, Xiaoyu Lin, Hailing Xiong

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Ancient books are significant culture inheritors and their background textures convey the potential history information. However, multi-style texture recovery of ancient books has received little attention. Restricted by insufficient ancient textures and complex handling process, the generation of ancient textures confronts with new challenges. For instance, training without sufficient data usually brings about overfitting or mode collapse, so some of the outputs are prone to be fake. Recently, image generation and style transfer based on deep learning are widely applied in computer vision. Breakthroughs within the field make it possible to conduct research upon multi-style texture recovery of ancient books. Under the circumstances, we proposed a network of layout analysis and image fusion system. Firstly, we trained models by using Deep Convolution Generative against Networks (DCGAN) to synthesize multi-style ancient textures; then, we analyzed layouts based on the Position Rearrangement (PR) algorithm that we proposed to adjust the layout structure of foreground content; at last, we realized our goal by fusing rearranged foreground texts and generated background. In experiments, diversified samples such as ancient Yi, Jurchen, Seal were selected as our training sets. Then, the performances of different fine-turning models were gradually improved by adjusting DCGAN model in parameters as well as structures. In order to evaluate the results scientifically, cross entropy loss function and Fréchet Inception Distance (FID) are selected to be our assessment criteria. Eventually, we got model M8 with lowest FID score. Compared with DCGAN model proposed by Radford at el., the FID score of M8 improved by 19.26%, enhancing the quality of the synthetic images profoundly.

Keywords: deep learning, image fusion, image generation, layout analysis

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Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

Abstract:

Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: color overlapping windows, image stitching, leukocyte detection, white blood cell detection

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Authors: Chia Jui Hsieh, Jyh Cheng Chen, Chih Wei Kuo, Ruei Teng Wang, Woei Chyn Chu

Abstract:

Compressed sensing (CS) based computed tomographic (CT) reconstruction algorithm utilizes total variation (TV) to transform CT image into sparse domain and minimizes L1-norm of sparse image for reconstruction. Different from the traditional CS based reconstruction which only calculates x-coordinate and y-coordinate TV to transform CT images into sparse domain, we propose a multi-directional TV to transform tomographic image into sparse domain for low-dose reconstruction. Our method considers all possible directions of TV calculations around a pixel, so the sparse transform for CS based reconstruction is more accurate. In 2D CT reconstruction, we use eight-directional TV to transform CT image into sparse domain. Furthermore, we also use 26-directional TV for 3D reconstruction. This multi-directional sparse transform method makes CS based reconstruction algorithm more powerful to reduce noise and increase image quality. To validate and evaluate the performance of this multi-directional sparse transform method, we use both Shepp-Logan phantom and a head phantom as the targets for reconstruction with the corresponding simulated sparse projection data (angular sampling interval is 5 deg and 6 deg, respectively). From the results, the multi-directional TV method can reconstruct images with relatively less artifacts compared with traditional CS based reconstruction algorithm which only calculates x-coordinate and y-coordinate TV. We also choose RMSE, PSNR, UQI to be the parameters for quantitative analysis. From the results of quantitative analysis, no matter which parameter is calculated, the multi-directional TV method, which we proposed, is better.

Keywords: compressed sensing (CS), low-dose CT reconstruction, total variation (TV), multi-directional gradient operator

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Authors: John Toussaint

Abstract:

Objective: Our society is suffering from an epidemic of body image dissatisfaction, and related disorders appear to be increasing globally for children. There is much to indicate that children's body image and eating attitudes are being affected negatively by socio-cultural factors such as parents, peers and media. Most studies and theories, however, have focused extensively on the daughter-mother relationship. Very few studies have investigated the role of attachment to the father as an important factor in the development of girls’ and women’s attitudes towards themselves and their bodies. Recently, data have shown that the father’s parenting style, as well as the quality of the relationship with him is crucial for the understanding of the development and persistence of body image disorders. This presentation is based on samples of participants with self-defined body image dissatisfaction, and the self-reported measures of their fathers’ parental behaviours, emotional warmth, support, or protection. Attachment theory does offer support in exploring these relationships and it is used in this presentation to assist in understanding the relationship between the father and his daughter in relation to body image and mental health. Clinical implications are also offered in respect to work with body image, eating disorders and relational therapy. Methods: As awareness of the increasing frequency of body image concerns in children grows, so too does the need for a simple, valid and reliable measure of body image. The Children's Body Image Scale (CBIS) designed in Australia, depicts seven male and females figures from which children are to choose their perceived body type and ideal body type. This was compared with a range of international body mass index (BMI) reference standards. These measures together with individual one-on-one interviews were completed by 158 children aged 7-12 years. Results: A high frequency of body image dissatisfaction was indicated in the children's responses. 55% of girls and 41% of boys said they would like to be thinner, and wished for an ideal BMI figure below the 10th percentile. This is an unhealthy and unattainable level of body fatness for the majority of children when considered in relation to the reported secular trend of their increasing average body size. Thin children were generally ranked as best and perceived as kind, happy, academically skilled, and socially successful. Fat children were perceived as unintelligent, lazy, greedy, unpopular, and unable to play physical games. Conclusions: Body image ideals and fat stereotypes are well entrenched among children. There is much to indicate that children's body image and eating attitudes are being affected negatively by sociocultural factors such as parents, peers and media. Teachers and health professionals could promote intervention programs for children involving knowledge and acceptance of genetic influences on body type; the dangerous effects of weight loss dieting; the importance of physical activity and eating healthy; and scepticism and critical analysis of mass media messages.

Keywords: body image, father attachment, mental health, eating disorders

Procedia PDF Downloads 260