Search results for: country's image components

Authors: Constanza A. Barriga, Rafael Bernardi, Amokrane Berdja, Christian D. Guzman

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Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariable in the image plane. However, this latter condition is not always satisfied with real optical systems. PSF angular variations cannot be evaluated directly from the observations, neither be corrected at a pixel resolution. We have developed a method for the restoration of images affected by static and anisotropic aberrations using deep neural networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T-80 telescope optical system, an 80 cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image, which has a constant and known PSF across its field-of-view. The method was tested with the T-80 telescope, achieving better results than with PSF deconvolution techniques. We present the method and results on this telescope.

Keywords: aberrations, deep neural networks, image restoration, variable point spread function, wide field images

Procedia PDF Downloads 134

Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

Procedia PDF Downloads 89

Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Swetasree Roy, Subaran Roy

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The contemporary literature documents globalization affects the stability of a country in three ways i) it increases peace ii) it decreases the likelihood of civil unrest, and iii) it creates employment. In this paper, we show that unemployment amongst youth plays a significant role in the effect of globalization and the internal stability of a country. Using recent data on globalization for 88 countries (2000-2014), we examine whether the presence of a large section of youth exacerbates the negative effects of globalization, thereby increasing chances of civil unrest. Using recent measures of globalization, we find globalization affect adversely on the stability of a country. Our results indicate that globalization in the presence of a high youth unemployment rate can create more instability in an economy. Results are robust in the presence of other socio-economic variables.

Keywords: globalization, youth bulge, civil unrest, unemployment

Procedia PDF Downloads 154

Authors: Vinay P., Srinivas K. S.

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Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

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Authors: Bharat Kumar, Deepak Kumar, Vijay Chaudhry

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Zirconium alloys are used as core components of nuclear reactors due to their high wear resistance, good corrosion properties, and good mechanical stability at high temperatures. Water flows inside the pressure tube through fuel claddings, which produces vibration of these core components and results in the wear of some components. Some components are subjected to the environment of coolant water containing LiOH which results in the corrosion of these components. The present work simulates some of these conditions to determine the failure mechanisms under these conditions and the effect of various parameters on them. Friction and wear experiments were performed varying the surrounding environment (room temperature, high temperature, and water submerged), duration, frequency, and displacement amplitude. Electrochemical corrosion experiments were performed by varying the concentration of LiOH in water. The worn and corroded surfaces were analyzed using scanning electron microscopy (SEM) to analyze the wear and corrosion mechanism and energy dispersive x-ray spectroscopy (EDS) and Raman spectroscopy to analyze the tribo-oxide layer formed during the wear and oxide layer formed during the corrosion. Wear increases with frequency and amplitude, and corrosion increases with LiOH concentration in water.

Keywords: zirconium alloys, wear, oxide layer, corrosion, EIS, linear polarization

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Authors: Angela Odero, Chrysoula Karathanasi, Michèle Baumann

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Integration of foreign communities has been a forefront issue in Luxembourg for some time now. The country’s continued progress depends largely on the successful integration of immigrants. The aim of our study was to analyze factors which intervene in the course of integration of Non-EU citizens through the discourse of Non-EU citizens residing in Luxembourg, who have signed the Welcome and Integration Contract (CAI). The two-year contract offers integration services to assist foreigners in getting settled in the country. Semi-structured focus group discussions with 50 volunteers were held in English, French, Spanish, Serbo-Croatian or Chinese. Participants were asked to talk about their integration experiences. Recorded then transcribed, the transcriptions were analyzed with the help of NVivo 10, a qualitative analysis software. A systematic and reiterative analysis of decomposing and reconstituting was realized through (1) the identification of predetermined categories (difficulties, challenges and integration needs) (2) initial coding – the grouping together of similar ideas (3) axial coding – the regrouping of items from the initial coding in new ways in order to create sub-categories and identify other core dimensions. Our results show that intervening factors include language acquisition, professional career and socio-cultural activities or events. Each of these factors constitutes different components whose weight shifts from person to person and from situation to situation. Connecting these three emergent factors are two elements essential to the success of the immigrant’s integration – the role of time and deliberate effort from the immigrants, the community, and the formal institutions charged with helping immigrants integrate. We propose a theoretical model where the factors described may be classified in terms of how they predispose, facilitate, and / or reinforce the process towards a successful integration. Measures currently in place propose one size fits all programs yet integrative measures which target the family unit and those customized to target groups based on their needs would work best.

Keywords: integration, integration services, non-eu citizens, qualitative analysis, third country nationals

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Authors: Anatoliy Kharkhurin

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The study argues that variations in creative performance may be stipulated by cross-cultural differences in perception of the creativity construct. In Experiment 1, 50 Russian and 50 Emirati college students received structured imagination test that requires producing a drawing of an alien creature. In Experiment 2, 53 Russian and 53 Emirati college students (different from Experiment 1) on 5-point Likert-type scale evaluated the level of creativity of the drawings produced in the Experiment I. Repeated-measure ANOVA revealed an interaction between the country where the drawings were produced and the country where they were evaluated. Russians evaluated their country mates’ drawings as more creative than the Emiratis evaluated their country mates’ drawings. Regression analysis revealed that the creativity level of the drawings was positively predicted by the Russians’ evaluation and negatively predicted by the Emiratis’ evaluation. Finally, the evaluation of the drawings by the Russians predicted divergent thinking performance.

Keywords: creativity, culture, cross-cultural, perception, production

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Authors: M. M. Shahidul Hassan, Omiya Hassan

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Bangladesh has set its goal to reach upper middle-income country status by 2024. To attain this status, the country must satisfy the World Bank requirement of achieving minimum Gross National Income (GNI). Number of youth job seekers in the country is increasing. University graduates are looking for decent jobs. So, the vital issue of this country is to understand how the GNI and jobs can be increased. The objective of this paper is to address these issues and find ways to create more job opportunities for youths at home and abroad which will increase the country’s GNI. The paper studies proportion of different goods Bangladesh exported, and also the percentage of employment in different sectors. The data used here for the purpose of analysis have been collected from the available literature. These data are then plotted and analyzed. Through these studies, it is concluded that growth in sectors like agricultural, ready-made garments (RMG), jute industries and fisheries are declining and the business community is not interested in setting up capital-intensive industries. Under this situation, the country needs to explore other business opportunities for a higher economic growth rate. Knowledge can substitute the physical resource. Since the country consists of the large youth population, higher education will play a key role in economic development. It now needs graduates with higher-order skills with innovative quality. Such dispositions demand changes in a university’s curriculum, teaching and assessment method which will function young generations as active learners and creators. By bringing these changes in higher education, a knowledge-based society can be created. The application of such knowledge and creativity will then become the commodity of Bangladesh which will help to reach its goal as an upper middle-income country.

Keywords: Bangladesh, economic sectors, economic growth, higher education, knowledge-based economy, massifcation of higher education, teaching and learning, universities’ role in society

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Authors: Liyun Chang, Cheng-Hsiang Tsai

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To ensure the radiation precisely delivering to the target of cancer patients, the linear accelerator equipped with the pretreatment on-board imaging system is introduced and through it the patient setup is verified before the daily treatment. New generation radiotherapy using beam-intensity modulation, usually associated the treatment with steep dose gradients, claimed to have achieved both a higher degree of dose conformation in the targets and a further reduction of toxicity in normal tissues. However, this benefit is counterproductive if the beam is delivered imprecisely. To avoid shooting critical organs or normal tissues rather than the target, it is very important to carry out the quality assurance (QA) of this on-board imaging system. The QA of the On-Board Imager® (OBI) system of one Varian Clinac-iX linear accelerator was performed through our procedures modified from a relevant report and AAPM TG142. Two image modalities, 2D radiography and 3D cone-beam computed tomography (CBCT), of the OBI system were examined. The daily and monthly QA was executed for five months in the categories of safety, geometrical accuracy and image quality. A marker phantom and a blade calibration plate were used for the QA of geometrical accuracy, while the Leeds phantom and Catphan 504 phantom were used in the QA of radiographic and CBCT image quality, respectively. The reference images were generated through a GE LightSpeed CT simulator with an ADAC Pinnacle treatment planning system. Finally, the image quality was analyzed via an OsiriX medical imaging system. For the geometrical accuracy test, the average deviations of the OBI isocenter in each direction are less than 0.6 mm with uncertainties less than 0.2 mm, while all the other items have the displacements less than 1 mm. For radiographic image quality, the spatial resolution is 1.6 lp/cm with contrasts less than 2.2%. The spatial resolution, low contrast, and HU homogenous of CBCT are larger than 6 lp/cm, less than 1% and within 20 HU, respectively. All tests are within the criteria, except the HU value of Teflon measured with the full fan mode exceeding the suggested value that could be due to itself high HU value and needed to be rechecked. The OBI system in our facility was then demonstrated to be reliable with stable image quality. The QA of OBI system is really necessary to achieve the best treatment for a patient.

Keywords: CBCT, image quality, quality assurance, OBI

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

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Authors: Ali Albadri

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Step band in an escalator moves in a cyclic periodic pattern. Similarly, most if not all of the components and sub-assemblies in the escalator operate in the same way. If you mark up one step in the step band of an escalator and stand next to the escalator, on the incline, to watch the marked-up step when it passes by, you ask yourself, does the marked up step behaves exactly the same way during each revolution when it passes you by again and again? We can say that; there is some similarity in this example and the example when an astronomer watches planets in the sky, and he or she asks himself or herself, does each planet intersects the plan of observation in the same position for every pantry rotation? For a fact, we know for the answer to the second example is no, because scientist, astronomers, and mathematicians have proven that planets deviate from their paths to take new paths during their planetary moves, albeit with minimal change. But what about the answer to the question in the first example? considering that there is increase in the wear and tear of components with time in the step, in the step band, in the tracks and in many other places in the escalator. There is also the accumulation of fatigue in the components and sub-assemblies. This research is part of many studies which we are conducting to address the answer for the question in the first example. We have been using the fractal dimension as a quantities tool and the Poincare map as a qualitative tool. This study has shown that the fractal dimension value and the shape and distribution of the orbits in the Poincare map has significant correlation with the quality of the mechanical components and sub-assemblies in the escalator.

Keywords: fractal dimension, Poincare map, rugby ball orbit, worm orbit

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Authors: Ruchi Agarwal

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The Indian diaspora in Thailand is as ethnically diverse as any other country. Although a relatively small community, the Indian diaspora has long established its roots, some with their fifth generation now living in Thailand. The community has a solid social and economic standing recognized by the host country but lacks connections with its ethnic roots in the home country. The biggest dilemma faced by the younger generation of the Indian diasporic community is the identity crisis. Regardless of being born and brought up in Thailand and possessing Thai citizenship, they do not get recognition as Thais by their Thai counterparts. However, with the Act Asia Policy of the Indian government, there has been an increase in social and political activities organized by old and new Indian associations, bringing new hopes of recognizing the Thai-Indian diasporic community.

Keywords: Indian, Thailand, diaspora, Act East Policy, Thai

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Farida Prihatini

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As a largest Muslim country in the world with around 215 Muslim inhabitants, Indonesia interestingly is not an Islamic country. Yet, Indonesia is not a secular country as well. The country has committed to be a unity in diversity country where people from various socio-political background may be coexistent live in this archipelago country. However, many provinces and Muslim groups are disposed of special regulation for Muslim people, namely local ordinances with sharia nuances, applied specifically in provinces, cities or regions where Muslim inhabitants are the majority. For the last two decades, particularly since Indonesia reform movement of 1998, a lot of local ordinances (Peraturan Daerah) with Sharia nuance have been enacted and applied in several provinces, cities and regions in Indonesia. The local ordinances are mostly deal with restriction of alcohol, prohibition of prostitution, Al Qur'an literacy, obligation to wear Muslim attire and zakat or alms management. Some of local ordinances have been warmly welcomed by society, while other ordinances have created tension. Those who oppose the ordinances believe that such things regulated by the ordinances are in violation of human rights and democracy, part of privacy rights of the people and must not be regulated by the State or local government. This paper describes the dynamic of local Ordinances with sharia nuances in Indonesia, in this research is limited to three ordinances: on the restriction of alcohol, prohibition of prostitution and obligation to wear Muslim attire. The researcher employs a normative method by studying secondary data and local ordinances in selected areas in Indonesia. The findings of the paper are that local ordinances with sharia nuances are indeed part of the needs of society, yet, in their implementation must take the pluralism of Indonesia and the state basic foundation, which is Pancasila (five pillars) into account.

Keywords: local, ordinances, sharia, rights

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Authors: Hasan Vural

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Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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

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

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Authors: Jae-Neung Lee, Keun-Chang Kwak

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This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding.

Keywords: yacht simulator, simulator, trends analysis, SIFT

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Authors: Tai Nguyen, Minh Bui, Huong Ninh, Tu Nguyen, Hai Tran

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GraphCut algorithm has been widely utilized to solve various types of computer vision problems. Its expensive computational cost encouraged many researchers to improve the speed of the algorithm. Recent works proposed schemes that work on parallel computing platforms such as CUDA. However, the problem of low convergence speed prevents the usage of GraphCut for real time applications. In this paper, we propose global suppression heuristic to boost the conver-gence process of the algorithm. A parallel implementation of GraphCut algorithm on CUDA designed for the image stitching problem is introduced. Our method achieves up to 3× time boost on the graph of size 80 × 480 compared to the best sequential GraphCut algorithm while achieving satisfactory stitched images, suitable for panorama applications. Our source code will be soon available for further research.

Keywords: CUDA, graph cut, image stitching, texture synthesis, maxflow/mincut algorithm

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Authors: Waraphorn Sribuakaew, Chutikarn Sriviboon, Rosjana Chandhasa

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The purpose of this research is to study the satisfaction level of graduates and factors that affect the image of Suan Sunandha Rajabhat University based on the perceptions of graduates on the graduation ceremony day. By studying the satisfaction of graduates, the image of Suan Sunandha Rajabhat University according to the graduates' perceptions and the loyalty to the university (in the aspects of intention to continue studying at a higher level, intention to recommend the university to a friend), the sample group used in this study was 1,000 graduates of Suan Sunandha Rajabhat University who participated on the 2019 graduation ceremony day. A questionnaire was utilized as a tool for data collection. By the use of computing software, the statistics used for data analysis were frequencies, percentage, mean, and standard deviation, One-Way ANOVA, and multiple regression analysis. Most of the respondents were graduates with a bachelor's degree, followed by graduates with a master's degree and PhD graduates, respectively. Major participants graduated from the Faculty of Management Sciences, followed by the Faculty of Humanities and Social Sciences and Faculty of Education, respectively. The graduates were satisfied on the ceremony day as a whole and rated each aspect at a satisfactory level. Formality, steps, and procedures were the aspects that graduates were most satisfied with, followed by graduation ceremony personnel and staff, venue, and facilities. On the perception of the graduates, the image of Suan Sunandha Rajabhat University was at a good level, while loyalty to the university was at a very high level. The intention of recommendation to others was at the highest level, followed by the intention to pursue further education at a very high level. The graduates graduating from different faculties have different levels of satisfaction on the graduation day with statistical significance at the level of 0.05. The image of Suan Sunandha Rajabhat University affected the satisfaction of graduates with statistical significance at the level of 0.01. The satisfactory level of graduates on the graduation ceremony day influenced the level of loyalty to the university with statistical significance at the level of 0.05.

Keywords: university image, loyalty to the university, intention to study higher education, intention to recommend the university to others, graduates' satisfaction

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

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Authors: N. H. Harun, A. S. Abdul Nasir, M. Y. Mashor, R. Hassan

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Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year. There are two main categories for leukaemia, which are acute and chronic leukaemia. The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image. In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively. Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving k-means clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia.

Keywords: acute leukaemia images, clustering algorithms, image segmentation, moving k-means

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Authors: Abdulkader Helwan

Abstract:

Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

Procedia PDF Downloads 503

Authors: Karthiyayini Nagarajan, P. V. Ramakrishna

Abstract:

Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar (SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System (Lab Bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.

Keywords: synthetic aperture radar, radio reflection model, lab bench, imaging engineering

Procedia PDF Downloads 498

Authors: M. Cangi, T. Dolar, C. Ersoy, Y. E. Aydogdu, A. I. Aydeniz, A. Mugan

Abstract:

The drum is one of the critical parts in a washing machine in which the clothes are washed and spin by the rotational movement. It is activated by the drum shaft which is attached to an electric motor and subjected to dynamic loading. Being one of the critical components, failures of the drum require costly repairs of dynamic components. In this study, tolerance bands between the drum shaft and its two bearings were examined to develop a relationship between the fatigue life of the shaft and the interaction tolerances. Optimization of tolerance bands was completed in consideration of the fatigue life of the shaft as the cost function. The following methodology is followed: multibody dynamic model of a washing machine was constructed and used to calculate dynamic loading on the components. Then, these forces were used in finite element analyses to calculate the stress field in critical components which was used for fatigue life predictions. The factors affecting the fatigue life were examined to find optimum tolerance grade for a given test condition. Numerical results were verified by experimental observations.

Keywords: fatigue life, finite element analysis, tolerance analysis, optimization

Procedia PDF Downloads 157

Authors: Karthiyayini Nagarajan, P. V. RamaKrishna

Abstract:

Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.

Keywords: synthetic aperture radar, radio reflection model, lab bench

Procedia PDF Downloads 468