Search results for: single image de-hazing

Authors: Ingrid Argote, John Archila, Marcelo Becker

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In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

Procedia PDF Downloads 229

Authors: Fatma Susilawati Mohamad, Azizah Abdul Manaf, Fadhillah Ahmad, Zarina Mohamad, Wan Suryani Wan Awang

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A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification.

Keywords: multi-class, linear classifier, nearest neighbor, linear discriminant analysis

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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Authors: Gaokai Liu

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Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets.

Keywords: deep learning, defect detection, image segmentation, nanomaterials

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Authors: Mejía M. Paula, Ramírez L. Leonardo, Puerta A. Gabriel

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In telemedicine, the image repository service is important to increase the accuracy of diagnostic support of medical personnel. This study makes comparison between two routing algorithms regarding the quality of service (QoS), to be able to analyze the optimal performance at the time of loading and/or downloading of medical images. This study focused on comparing the performance of Tabu Search with other heuristic and metaheuristic algorithms that improve QoS in telemedicine services in Colombia. For this, Tabu Search and Simulated Annealing heuristic algorithms are chosen for their high usability in this type of applications; the QoS is measured taking into account the following metrics: Delay, Throughput, Jitter and Latency. In addition, routing tests were carried out on ten images in digital image and communication in medicine (DICOM) format of 40 MB. These tests were carried out for ten minutes with different traffic conditions, reaching a total of 25 tests, from a server of Universidad Militar Nueva Granada (UMNG) in Bogotá-Colombia to a remote user in Universidad de Santiago de Chile (USACH) - Chile. The results show that Tabu search presents a better QoS performance compared to Simulated Annealing, managing to optimize the routing of medical images, a basic requirement to offer diagnostic images services in telemedicine.

Keywords: medical image, QoS, simulated annealing, Tabu search, telemedicine

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Authors: Mina Esmi Jahromi, Mehdi Khiadani

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Plunging water jets discharging into turbulent crossflows are capable of providing efficient air water interfacial area, which is desirable for the process of mass transfer. Although several studies have been dedicated to the air entrainment by water jets impinging into stagnant water, very few studies have focused on the water jets in crossflows. This study investigates development of the two-phase flow as a result of the jet impingements into crossflows by means of image processing technique and CFD simulations. Investigations are also conducted on the oxygen transfer and a correlation is established between the aeration properties and the oxygenation capacity of water jets in crossflows. This study helps the optimal design and the effective operation of the industrial and the environmental equipment incorporating water jets in crossflows.

Keywords: air entrainment, CFD simulation, image processing, jet in crossflow, oxygen transfer, two-phase flow

Procedia PDF Downloads 238

Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

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Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

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Authors: Aida Sheeta, M. Nashat Fors, Sherwet El Gholmy, Marwa Issa

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Globalization has raised the customer preferences not only towards the high-quality garments but also the right fitting, comfort and aesthetic apparels. This only can be accomplished by the good interaction between fabric mechanical and physical properties as well as the required style. Consequently, this paper provides an integrated review of the fabric drape terminology because it is considered as an essential feature in which the fabric can form folds with the help of the gravity. Moreover, an instrument has been fabricated in order to analyze the static and dynamic drape behaviors using different fabric types. In addition, the obtained results find out the parameters affecting the drape coefficient using digital image processing for various kind of commercial fabrics. This was found to be an essential first step in order to analyze the behavior of this fabric when it is fabricated in a certain 3-D garment design.

Keywords: cloth fitting, fabric drape nodes, garment silhouette, image processing

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Authors: Adel Nikfarjam, Hamed Tayebi, Sadoullah Ebrahimnejad

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This paper considers a two-echelon inventory system with a number of warehouses and a single retailer. The retailer replenishes its required items from warehouses, and assembles them into a single final product. We assume that each warehouse supplies only one kind of the raw material for the retailer. The demand process of the final product is assumed to be Poissson, and unsatisfied demand of the final product will be lost. The retailer applies one-for-one-period ordering policy which is also known as (1, T) ordering policy. In this policy the retailer orders to each warehouse a fixed quantity of each item at fixed time intervals, which the fixed quantity is equal to the utilization of the item in the final product. Since, this policy eliminates all demand uncertainties at the upstream echelon, the standard lot sizing model can be applied at all warehouses. In this paper, we calculate the total cost function of the inventory system. Then, based on this function, we present a procedure to obtain the optimal time interval between two consecutive order placements from retailer to the warehouses, and the optimal order quantities of warehouses (assuming that there are positive ordering costs at warehouses). Finally, we present some numerical examples, and conduct numerical sensitivity analysis for cost parameters.

Keywords: two-echelon supply chain, multi-vendor-single-buyer inventory system, lost sales, Poisson demand, one-for-one-period policy, lot sizing model

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Authors: Hassan A. Alshahrani, Mehdi H. Hojjati

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In order to predict and model wrinkling which is caused by out of plane deformation due to compressive loading in the plane of the material during composite prepregs forming, it is necessary to quantitatively understand the relative magnitude of the bending stiffness. This study aims to examine the bending properties of out-of-autoclave (OOA) thermosetting prepreg under vertical cantilever test condition. A direct method for characterizing the bending behavior of composite prepregs was developed. The results from direct measurement were compared with results derived from an image-processing procedure that analyses the captured image during the vertical bending test. A numerical simulation was performed using ABAQUS to confirm the bending stiffness value.

Keywords: Bending stiffness, out-of-autoclave prepreg, forming process, numerical simulation.

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Authors: Sylvie Sossou, Grégoire Gansou, Ildevert Egue

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Introduction: The mission of the family and the school is to educate and train citizens of the city. But the family’s values , parental roles, respect for life collapse in their traditional African form. Indeed laxity with regard to divorce, liberal ideas about child rearing influence the emotional life of the latter. Several causes may contribute to the decline in academic performance. In order to seek a psychological solution to the issue, a study was conducted in 6 schools at the 9th district in Cotonou, cosmopolitan city of Benin. Objective: To evaluate the impact of single parenthood on the psycho child development. Materials and Methods: Questionnaires and interviews were used to gather verbal information. The questionnaires were administered to parents and children (schoolchildren 4, 5 and six form) from 7 to 12 years in lone parenthood. The interview was done with teachers and school leaders. We identified 209 cases of children living with a "single-parent" and 68 single parents. Results: Of the 209 children surveyed the results showed that 116 children are cut relational triangle in early childhood (before 3 years). The psychological effects showed that the separation has caused sadness for 52 children, anger 22, shame 17, crying at 31 children, fear for 14, the silence at 58 children. In front of complete family’s children, these children experience feelings of aggression in 11.48%; sadness in 30.64%; 5.26% the shame, the 6.69% tears; jealousy in 2.39% and 2.87% of indifference. The option to get married in 44.15% of children is a challenge to want to give a happy childhood for their offspring; 22.01% feel rejected, there is uncertainty for 11.48% of cases and 25.36% didn’t give answer. 49, 76% of children want to see their family together; 7.65% are against to avoid disputes and in many cases to save the mother of the father's physical abuse. 27.75% of the ex-partners decline responsibility in the care of the child. Furthermore family difficulties affecting the intellectual capacities of children: 37.32% of children see school difficulties related to family problems despite all the pressure single-parent to see his child succeed. Single parenthood affects inter-family relations: pressure 33.97%; nervousness 24.88%; overprotection 29.18%; backbiting 11.96%, are the lives of these families. Conclusion: At the end of the investigation, results showed that there is a causal relationship between psychological disorders, academic difficulties of children and quality of parental relationships. Other cases may exist, but the lack of resources meant that we have only limited at 6 schools. Early psychological treatment for these children is needed.

Keywords: single-parent, psycho child, school, Cotonou

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Authors: V. M. B. K. T. Malwanage, S. Samita

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Core stability is one of the major determinants that contribute to preventing injuries, enhance performance, and improve quality of life of the human. Endurance of the four major muscle groups of the central ‘core’ of the human body is identified as the most reliable determinant of core stability amongst the other numerous causes which contribute to readily make one’s core stability. This study aimed to develop a ‘Core Stability Index’ to confer a single value for an individual’s core stability based on the four endurance test scores. Since it is possible that at least some of the test scores are not independent, possibility of constructing a single index using the multivariate method exploratory factor analysis was investigated in the study. The study sample was consisted of 400 healthy young individuals with the mean age of 23.74 ± 1.51 years and mean BMI (Body Mass Index) of 21.1 ± 4.18. The correlation analysis revealed highly significant (P < 0.0001) correlations between test scores and thus construction an index using these highly inter related test scores using the technique factor analysis was justified. The mean values of all test scores were significantly different between males and females (P < 0.0001), and therefore two separate core stability indices were constructed for the two gender groups. Moreover, having eigen values 3.103 and 2.305 for males and females respectively, indicated one factor exists for all four test scores and thus a single factor based index was constructed. The 95% reference intervals constructed using the index scores were -1.64 to 2.00 and -1.56 to 2.29 for males and females respectively. These intervals can effectively be used to diagnose those who need improvement in core stability. The practitioners should find that with a single value measure, they could be more consistent among themselves.

Keywords: construction of indices, endurance test scores, muscle endurance, quality of life

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Authors: Didem Can

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Scheduling problems are one of the most fundamental issues of production systems. Many different approaches and models have been developed according to the production processes of the parts and the main purpose of the problem. In this study, one of the bottleneck stations of a company serving in the aerospace industry is analyzed and considered as a single machine scheduling problem with sequence-dependent setup times. The objective of the problem is assigning a large number of similar parts to the same shift -to reduce chemical waste- while minimizing the number of tardy jobs. The goal programming method will be used to achieve two different objectives simultaneously. The assignment of parts to the shift will be expressed using the integer programming method. Finally, the constraint programming method will be used as it provides a way to find a result in a short time by avoiding worse resulting feasible solutions with the defined variables set. The model to be established will be tested and evaluated with real data in the application part.

Keywords: constraint programming, goal programming, integer programming, sequence-dependent setup, single machine scheduling

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Authors: Ufuk Tosun, Reza Aghazadeh, Mehmet Bülent Özer

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This study puts forward a method to analyze the sloshing characteristics of liquid in a tuned sloshing absorber system by using image processing tools. Tuned sloshing vibration absorbers have recently attracted researchers’ attention as a seismic load damper in constructions due to its practical and logistical convenience. The absorber is liquid which sloshes and applies a force in opposite phase to the motion of structure. Experimentally characterization of the sloshing behavior can be utilized as means of verifying the results of numerical analysis. It can also be used to identify the accuracy of assumptions related to the motion of the liquid. There are extensive theoretical and experimental studies in the literature related to the dynamical and structural behavior of tuned sloshing dampers. In most of these works there are efforts to estimate the sloshing behavior of the liquid such as free surface motion and total force applied by liquid to the wall of container. For these purposes the use of sensors such as load cells and ultrasonic sensors are prevalent in experimental works. Load cells are only capable of measuring the force and requires conducting tests both with and without liquid to obtain pure sloshing force. Ultrasonic level sensors give point-wise measurements and hence they are not applicable to measure the whole free surface motion. Furthermore, in the case of liquid splashing it may give incorrect data. In this work a method for evaluating the sloshing wave height by using camera records and image processing techniques is presented. In this method the motion of the liquid and its container, made of a transparent material, is recorded by a high speed camera which is aligned to the free surface of the liquid. The video captured by the camera is processed frame by frame by using MATLAB Image Processing toolbox. The process starts with cropping the desired region. By recognizing the regions containing liquid and eliminating noise and liquid splashing, the final picture depicting the free surface of liquid is achieved. This picture then is used to obtain the height of the liquid through the length of container. This process is verified by ultrasonic sensors that measured fluid height on the surface of liquid.

Keywords: fluid structure interaction, image processing, sloshing, tuned liquid damper

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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

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Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: Wenfu Zheng, Guanghe Zheng

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The discussion on the social sustainability in existing literature is limited to two-dimension epistemology space with only two elements: the human and nature. Using the revelation of the Bible God, the paper adds a moral component to the two-dimension space. With the new variable being introduced, the authors formulate a to three-dimension epistemology space and discuss its implications. Based on the space, the authors explore the hierarchical structure of order principles for a sustainable society. The social order principle system hierarchically consists of three principles: moral, relational, and rational. The justification of every principle is analyzed briefly. The paper concluded that all these order principles are necessary assurance of building a sustainable society.

Keywords: common grace, saving grace, sustainable society, the image of God

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Authors: Sarah El Mokadem

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Egypt has been the focus of international media since 2011 revolution and its repercussions. By the end of 2017, President Abdel Fattah El Sisi will have finished his first term of presidency. With an upcoming presidential election, all eyes are returning back to Egypt as there are speculations about whether the current regime will uphold or change points in the constitution determining the years of presidency term and the allowed number or reelections. In this paper, the researcher examines the reports related to Egypt in three international news channels with different ideologies. The research aims to identify the frames used to portray major issues in Egypt like the economic struggle, democracy levels and stability and safety of the country. All available reports from these three channels in 2017 on YouTube were analyzed which is the year before the presidential elections.

Keywords: content analysis, Egypt, image building, news channel ideology

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Authors: Elyas Palantei, Dewiani, Farid Armin, Ardiansyah

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High precision of printed microwave sensor utilized for sensing and monitoring the potential breast cancer existed in women breast tissue was optimally computed. The single element of UWB printed sensor that successfully modeled through several numerical optimizations was multiple fabricated and incorporated with woman bra to form the spherical sensors array. One sample of UWB microwave sensor obtained through the numerical computation and optimization was chosen to be fabricated. In overall, the spherical sensors array consists of twelve stair patch structures, and each element was individually measured to characterize its electrical properties, especially the return loss parameter. The comparison of S11 profiles of all UWB sensor elements is discussed. The constructed UWB sensor is well verified using HFSS programming, CST programming, and experimental measurement. Numerically, both HFSS and CST confirmed the potential operation bandwidth of UWB sensor is more or less 4.5 GHz. However, the measured bandwidth provided is about 1.2 GHz due to the technical difficulties existed during the manufacturing step. The configuration of UWB microwave sensing and monitoring system implemented consists of 12 element UWB printed sensors, vector network analyzer (VNA) to perform as the transceiver and signal processing part, the PC Desktop/Laptop acting as the image processing and displaying unit. In practice, all the reflected power collected from whole surface of artificial breast model are grouped into several numbers of pixel color classes positioned on the corresponding row and column (pixel number). The total number of power pixels applied in 2D-imaging process was specified to 100 pixels (or the power distribution pixels dimension 10x10). This was determined by considering the total area of breast phantom of average Asian women breast size and synchronizing with the single UWB sensor physical dimension. The interesting microwave imaging results were plotted and together with some technical problems arisen on developing the breast sensing and monitoring system are examined in the paper.

Keywords: UWB sensor, UWB microwave imaging, spherical array, breast cancer monitoring, 2D-medical imaging

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: Bing Gu

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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.

Keywords: DBN, face recognition, light field, Lytro

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Authors: Yosef Ferede Bogale

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The purpose of this study was to evaluate the degree of customer satisfaction and the variables influencing it in the instance of the Bank of Abyssinia branches in the districts of Arada and Bole in Addis Ababa. The study was carried out utilizing a mixed research approach and a descriptive and explanatory research design in Addis Ababa, the capital city of Ethiopia. Both primary and secondary data were employed in this investigation. The study's target population consisted of 1000 of the bank's most prestigious clients. With a 93% response rate, 265 respondents from both genders in the active age group had higher levels of education and work experience and were in the active age group. Customers of the case bank under consideration comprised the study's target audience. The respondents, who belonged to both gender groups, were in the active age bracket with superior levels of education and work experience. As a result, this investigation discovered that the degree of client satisfaction was assigned a medium rating. Additionally given a middling rating were the company's image practices, employee competency, technology, and service quality. Further, the results also demonstrate that corporate image, employees’ competency, technology, and service quality all positively and significantly affect customer happiness. This study found that, to varying degrees, company image, technology, competence, and high-quality financial services will all improve consumer happiness. According to this report, banks should monitor customer satisfaction and service quality at least twice a year. This is because there is a growing movement among bank service providers for accountability, and measuring these factors is crucial. This study also recommends that banks make every effort to satisfy consumers' expectations to the highest level.

Keywords: customer satisfaction, corporate image, quality service risk, banks

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: Retno Supriyanti, Ays Rahmadian Subhi, Yogi Ramadhani, Haris B. Widodo

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Alzheimer is one type of disease in the elderly that may occur in the world. The severity of the Alzheimer can be measured using a scale called Clinical Dementia Rating (CDR) based on a doctor's diagnosis of the patient's condition. Currently, diagnosis of Alzheimer often uses MRI machine, to know the condition of part of the brain called Hippocampus and Ventricle. MRI image itself consists of 3 slices, namely Coronal, Sagittal and Axial. In this paper, we discussed the measurement of the area of the ventricle especially in the Coronal slice based on the severity level referring to the CDR value. We use Active Contour method to segment the ventricle’s region, therefore that ventricle’s area can be calculated automatically. The results show that this method can be used for further development in the automatic diagnosis of Alzheimer.

Keywords: Alzheimer, CDR, coronal, ventricle, active contour

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Yosef Ferede Bogale

Abstract:

The purpose of this study was to evaluate the degree of customer satisfaction and the variables influencing it in the instance of the Bank of Abyssinia branches in the districts of Arada and Bole in Addis Ababa. The study was carried out utilizing a mixed research approach and a descriptive and explanatory research design in Addis Ababa, the capital city of Ethiopia. Both primary and secondary data were employed in this investigation. The study's target population consisted of 1000 of the bank's most prestigious clients. With a 93% response rate, 265 respondents from both genders in the active age group had higher levels of education and work experience and were in the active age group. Customers of the case bank under consideration comprised the study's target audience. The respondents, who belonged to both gender groups, were in the active age bracket with superior levels of education and work experience. As a result, this investigation discovered that the degree of client satisfaction was assigned a medium rating. Additionally given a middling rating were the company's image practices, employee competency, technology, and service quality. Further, the results also demonstrate that corporate image, employees’ competency, technology, and service quality all positively and significantly affect customer happiness. This study found that, to varying degrees, company image, technology, competence, and high-quality financial services will all improve consumer happiness. According to this report, banks should monitor customer satisfaction and service quality at least twice a year. This is because there is a growing movement among bank service providers for accountability, and measuring these factors is crucial. This study also recommends that banks make every effort to satisfy consumers' expectations to the highest level.

Keywords: customer satisfaction, corporate image, quality services risk, bank

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Authors: Eli Avraham

Abstract:

The Arab Spring was widely covered in the global media and the number of Western tourists traveling to the area began to fall. The goal of this study was to analyze which media strategies marketers in Middle Eastern countries chose to employ in their attempts to repair the negative image of the area in the wake of the Arab Spring. Several studies were published concerning image-restoration strategies of destinations during crises around the globe; however, these strategies were not part of an overarching theory, conceptual framework or model from the fields of crisis communication and image repair. The conceptual framework used in the current study was the ‘multi-step model for altering place image’, which offers three types of strategies: source, message and audience. Three research questions were used: 1.What public relations crisis techniques and advertising campaign components were used? 2. What media policies and relationships with the international media were adopted by Arab officials? 3. Which marketing initiatives (such as cultural and sports events) were promoted? This study is based on qualitative content analysis of four types of data: 1) advertising components (slogans, visuals and text); (2) press interviews with Middle Eastern officials and marketers; (3) official media policy adopted by government decision-maker (e.g. boycotting or arresting newspeople); and (4) marketing initiatives (e.g. organizing heritage festivals and cultural events). The data was located in three channels from December 2010, when the events started, to September 31, 2013: (1) Internet and video-sharing websites: YouTube and Middle Eastern countries' national tourism board websites; (2) News reports from two international media outlets, The New York Times and Ha’aretz; these are considered quality newspapers that focus on foreign news and tend to criticize institutions; (3) Global tourism news websites: eTurbo news and ‘Cities and countries branding’. Using the ‘multi-step model for altering place image,’ the analysis reveals that Middle Eastern marketers and officials used three kinds of strategies to repair their countries' negative image: 1. Source (cooperation and media relations; complying, threatening and blocking the media; and finding alternatives to the traditional media) 2. Message (ignoring, limiting, narrowing or reducing the scale of the crisis; acknowledging the negative effect of an event’s coverage and assuring a better future; promotion of multiple facets, exhibitions and softening the ‘hard’ image; hosting spotlight sporting and cultural events; spinning liabilities into assets; geographic dissociation from the Middle East region; ridicule the existing stereotype) and 3. Audience (changing the target audience by addressing others; emphasizing similarities and relevance to specific target audience). It appears that dealing with their image problems will continue to be a challenge for officials and marketers of Middle Eastern countries until the region stabilizes and its regional conflicts are resolved.

Keywords: Arab spring, cultural events, image repair, Middle East, tourism marketing

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Authors: Israa Al-Neami, Ali J. Al-Askery, Martin Johnston, Charalampos Tsimenidis

Abstract:

In this paper, we focus on the design of a multi-line copper wire (MLCW) communication system. First, we construct our proposed MLCW channel and verify its characteristics based on the Kolmogorov-Smirnov test. In addition, we apply Middleton class A impulsive noise (IN) to the copper channel for further investigation. Second, the MIMO G.fast system is adopted utilizing the proposed MLCW channel model and is compared to a single line G-fast system. Second, the performance of the coded system is obtained utilizing concatenated interleaved Reed-Solomon (RS) code with four-dimensional trellis-coded modulation (4D TCM), and compared to the single line G-fast system. Simulations are obtained for high quadrature amplitude modulation (QAM) constellations that are commonly used with G-fast communications, the results demonstrate that the bit error rate (BER) performance of the coded MLCW system shows an improvement compared to the single line G-fast systems.

Keywords: G.fast, Middleton Class A impulsive noise, mitigation techniques, Copper channel model

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Authors: Fernando Duarte

Abstract:

The work aims to evaluate the use of classical image processing methodologies towards skin tone classification in dermatological images. The skin tone is an important attribute when considering several factor for skin cancer diagnosis. Currently, there is a lack of clear methodologies to classify the skin tone based only on the dermatological image. In this work, a recent released dataset with the label for skin tone was used as reference for the evaluation of classical methodologies for segmentation and adjustment of color space for classification of skin tone in dermatological images. It was noticed that even though the classical methodologies can work fine for segmentation and color adjustment, classifying the skin tone without proper control of the aquisition of the sample images ended being very unreliable.

Keywords: segmentation, classification, color space, skin tone, Fitzpatrick

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Authors: Belaynesh Chekol, Numan Çelebi

Abstract:

The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

Procedia PDF Downloads 281