Search results for: automatic image colorization

Authors: Haziyah Hussin

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Hazi Attire software is purposely designed to be used for pattern drafting of the Malay Traditional Women Apparel. It is software created using LISP Program that works under AutoCAD engine and able to draft various patterns for Malay women apparels from fitted, semi-fitted and loose silhouettes. It is fully automatic and the user can select styles from the menu on the screen and enter the measurements. Within five seconds patterns are ready to be printed and sewn. Hazi Attire is different from other programmes available in the market since it is fully automatic, user-friendly and able to print selected pattern chosen quickly and accurately. With this software (Hazi Attire), the selected styles can be generated the pattern according to made-to-measure or standard sizes. It would benefit the apparel industries by reducing manufacturing lead time and cycle time.

Keywords: basic pattern, pattern drafting, toile, Malay traditional women apparel, the measurement parameters, fitted, semi-fitted and loose silhouette

Procedia PDF Downloads 269

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

Procedia PDF Downloads 348

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

Procedia PDF Downloads 432

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

Procedia PDF Downloads 131

Authors: Zainab S. Abdullahi, Zakariyya H. Abdullahi, Sahnun Dahiru

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Student attendance in schools is a very important aspect in school management record. In recent years, security systems have become one of the most demanding systems in school. Every institute have its own method of taking attendance, many schools in Nigeria use the old fashion way of taking attendance. That is writing the students name and registration number in a paper and submitting it to the lecturer at the end of the lecture which is time-consuming and insecure, because some students can write for their friends without the lecturer’s knowledge. In this paper, we propose a system that takes attendance using face recognition. There are many automatic methods available for this purpose i.e. biometric attendance, but they all waste time, because the students have to follow a queue to put their thumbs on a scanner which is time-consuming. This attendance is recorded by using a camera attached in front of the class room and capturing the student images, detect the faces in the image and compare the detected faces with database and mark the attendance. The principle component analysis was used to recognize the faces detected with a high accuracy rate. The paper reviews the related work in the field of attendance system, then describe the system architecture, software algorithm and result.

Keywords: attendance system, face detection, face recognition, PCA

Procedia PDF Downloads 364

Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

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Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

Procedia PDF Downloads 274

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: Shuting Ji, Yueming Zhang, Jing Zhao

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The output error of the globoidal cam mechanism can be considered as a relevant indicator of mechanism performance, because it determines kinematic and dynamical behavior of mechanical transmission. Based on the differential geometry and the rigid body transformations, the mathematical model of surface geometry of the globoidal cam is established. Then we present the analytical expression of the output error (including the transmission error and the displacement error along the output axis) by considering different manufacture and assembly errors. The effects of the center distance error, the perpendicular error between input and output axes and the rotational angle error of the globoidal cam on the output error are systematically analyzed. A globoidal cam mechanism which is widely used in automatic tool changer of CNC machines is applied for illustration. Our results show that the perpendicular error and the rotational angle error have little effects on the transmission error but have great effects on the displacement error along the output axis. This study plays an important role in the design, manufacture and assembly of the globoidal cam mechanism.

Keywords: globoidal cam mechanism, manufacture error, transmission error, automatic tool changer

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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

Procedia PDF Downloads 136

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: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

Procedia PDF Downloads 79

Authors: Abdulkader Helwan

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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

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Authors: Karthiyayini Nagarajan, P. V. Ramakrishna

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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 497

Authors: Karthiyayini Nagarajan, P. V. RamaKrishna

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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

Authors: Hossein Shareh, Farhad Seifi

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The present study was to examine the role of attachment styles, gender schemas, sexual-self schemas, and body image during sexual activity in sexual function, marital satisfaction, and sexual self-esteem. The sampling method was among married women who were living in Mashhad; a snowball selected 765 people. Questionnaires and measures of adult attachment style (AAS), Bem Sex Role Inventory (BSRI), sexual self-schema (SSS), body exposure during sexual activity questionnaire (BESAQ), sexual function female inventory (FSFI), a short form of sexual self-esteem (SSEI-W-SF) and marital satisfaction (Enrich) were completed by participants. Data analysis using Pearson correlation and hierarchical regression and case analysis was performed by SPSS-19 software. The results showed that there is a significant correlation (P <0.05) between attachment and sexual function (r=0.342), marital satisfaction (r=0.351) and sexual self-esteem (r =0.292). A correlation (P <0.05) was observed between sexual schema (r=0.342) and sexual esteem (r=0.31). A meaningful correlation (P <0.05) exists between gender stereotypes and sexual function (r=0.352). There was a significant inverse correlation (P <0.05) between body image and their performance during sexual activity (r=0.41). There is no significant relationship between gender schemas, sexual schemas, body image, and marital satisfaction, and no relation was found between gender schemas, body image, and sexual self-esteem. Also, the result of the regression showed that attachment styles, gender schemas, sexual self- schemas, and body exposures during sexual activity are predictable in sexual function, and marital satisfaction can be predicted by attachment style and gender schema. Somewhat, sexual self-esteem can be expected by attachment style and gender schemas.

Keywords: attachment styles, gender and sexual schemas, body image, sexual function, marital satisfaction, sexual self-esteem

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Authors: Deniz Guven, Wil Ward, Jinming Duan, Li Bai

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Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods.

Keywords: optical coherence tomography, image segmentation, Kalman filter, object tracking

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Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

Procedia PDF Downloads 143

Authors: Bola Adeleke, Kayode Ogunsusi

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Heritage sites have evolved to preserve culture and heritage and also to educate and entertain tourists. Tourist travel decisions and behavior are influenced by destination image and value of the experience of tourists. Perceived value is one of the important tools for securing a competitive edge in tourism destinations. The model of Ritchie and Crouch distinguished 36 attributes of competitiveness which are classified into five factors which are quality of experience, touristic attractiveness, environment and infrastructure, entertainment/outdoor activities and cultural traditions. The study extended this model with a different grouping of the determinants of destination competitiveness. The theoretical framework used for this study assumes that apart from attractions already situated in the grove, satisfaction with destination common service, and entertainment and events, can all be used in creating a positive image for/and in attracting customers (destination selection) to visit Osun Sacred Osogbo Grove during and after annual celebrations. All these will impact positively on travel experience of customers as well as their spiritual fulfillment. Destination image has a direct impact on tourists’ satisfaction which consequently impacts on tourists’ likely future behavior on whether to revisit a cultural destination or not. The study investigated the variables responsible for destination image competitiveness of the Heritage Site; assessed the factors enhancing the destination image; and evaluated the perceived value realized by tourists from their cultural experience at the grove. A complete enumeration of tourists above 18 years of age who visited the Heritage Site within the month of March and April 2017 was taken. 240 respondents, therefore, were used for the study. The structured questionnaire with 5 Likert scales was administered. Five factors comprising 63 variables were used to determine the destination image competitiveness through principal component analysis, while multiple regressions were used to evaluate perceived value of tourists at the grove. Results revealed that 11 out of the 12 variables determining the destination image competitiveness were significant in attracting tourists to the grove. From the R-value, all factors predicted tourists’ value of experience strongly (R= 0.936). The percentage variance of customer value was explained by 87.70% of the variance of destination common service, entertainment and event satisfaction, travel environment satisfaction and spiritual satisfaction, with F-value being significant at 0.00. Factors with high alpha value contributed greatly to adding value to enhancing destination and tourists’ experience. 11 variables positively predicted tourist value with significance. Managers of Osun World Heritage Site should improve on variables critical to adding values to tourists’ experience.

Keywords: competitiveness, destination image, Osun Osogbo world heritage site, tourists

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Authors: Sabrine Ammar, Mohamed Tahar Bhiri

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Automatic planning has a de facto standard language called Planning Domain Definition Language (PDDL) for describing planning problems. It aims to formalize the planning problems described by the concept of state space. PDDL-related dynamic analysis tools, namely planners and validators, are insufficient for verifying and validating PDDL descriptions. Indeed, these tools made it possible to detect errors a posteriori by means of test activity. In this paper, we recommend a formal approach coupling the two languages Event-B and PDDL, for automatic planning. Event-B is used for formal modeling by stepwise refinement with mathematical proofs of planning problems. Thus, this paper proposes a refinement strategy allowing to obtain reliable PDDL descriptions from an ultimate Event-B model correct by construction. The ultimate Event-B model, correct by construction which is supposed to be translatable into PDDL, is automatically translated into PDDL using our MDE Event-B2PDDL tool.

Keywords: code generation, event-b, PDDL, refinement strategy, translation rules

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Authors: Eid Alharbi, Yaser Miaji, Saeed Alzahrani

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The importance of fruit quality and freshness is potential in today’s life. Most recent studies show and automatic online sorting system according to the internal disorder for fresh apple fruit has developed by using near infrared (NIR) spectroscopic technology. The automatic convener belts system along with sorting mechanism was constructed. To check the internal quality of the apple fruit, apple was exposed to the NIR radiations in the range 650-1300 nm and the data were collected in form of absorption spectra. The collected data were compared to the reference (data of known sample) analyzed and an electronic signal was pass to the sorting system. The sorting system was separate the apple fruit samples according to electronic signal passed to the system. It is found that absorption of NIR radiation in the range 930-950 nm was higher in the internally defected samples as compared to healthy samples. On the base of this high absorption of NIR radiation in 930-950 nm region the online sorting system was constructed.

Keywords: mechatronics design, NIR, fruit quality, spectroscopic technology

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Authors: P. Subbuthai, S. Muruganand

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Diabetic Retinopathy is one of the complicated diseases and it is caused by the changes in the blood vessels of the retina. Extraction of retina image through Fundus camera sometimes produced poor contrast and noises. Because of this noise, detection of blood vessels in the retina is very complicated. So preprocessing is needed, in this paper, a novel algorithm is implemented to remove the noisy pixel in the retina image. The proposed algorithm is Extended Median Filter and it is applied to the green channel of the retina because green channel vessels are brighter than the background. Proposed extended median filter is compared with the existing standard median filter by performance metrics such as PSNR, MSE and RMSE. Experimental results show that the proposed Extended Median Filter algorithm gives a better result than the existing standard median filter in terms of noise suppression and detail preservation.

Keywords: fundus retina image, diabetic retinopathy, median filter, microaneurysms, exudates

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

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

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Authors: M. F. Haroun, T. A. Gulliver

Abstract:

In this paper, an encryption algorithm is proposed for real-time image encryption. The scheme employs a dual chaotic generator based on a three dimensional (3D) discrete Lorenz attractor. Encryption is achieved using non-autonomous modulation where the data is injected into the dynamics of the master chaotic generator. The second generator is used to permute the dynamics of the master generator using the same approach. Since the data stream can be regarded as a random source, the resulting permutations of the generator dynamics greatly increase the security of the transmitted signal. In addition, a technique is proposed to mitigate the error propagation due to the finite precision arithmetic of digital hardware. In particular, truncation and rounding errors are eliminated by employing an integer representation of the data which can easily be implemented. The simple hardware architecture of the algorithm makes it suitable for secure real-time applications.

Keywords: chaotic systems, image encryption, non-autonomous modulation, FPGA

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Authors: Evan W. Krieger, Vijayan K. Asari, Saibabu Arigela

Abstract:

Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image.

Keywords: dynamic range compression, multi-level Fourier features, nonlinear enhancement, super resolution

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Authors: Dhruv Saini, Megha Sharma, Sharad Gupta

Abstract:

This research is showing the use of celebrity endorsement and social media influencers and how they help in enhancing the brand loyalty of the consumers. The study aims at keeping brand image of the brand as the link between the two. However, choosing the right celebrity or social media influencer is not an easy task and it is very essential for a brand to select the right ambassador for advertising their products and for selling the product to the ultimate consumer. The purpose of the study is to create a relationship of Celebrity endorsement with brand image and with brand loyalty and creating a relationship of Social media influencers with brand image and with brand loyalty and then making a comparison between the two by measuring the effects of both simultaneously. And then by analyzing which among the two has a greater impact on brand loyalty of the consumers. The study mainly focuses on four major variables namely Celebrity endorsement, Social media influencers, Brand image and Brand loyalty. The study also focuses on interdependence and relationships that these variables have with each other and how they are linked with each other. The study also aims at looking which among Celebrity endorsement and Social media influencer has a greater impact on increasing or enhancing the loyalty for a brand. Earlier celebrity endorsers had a major impact on brand loyalty of the consumers but with time social media influencers are also playing a very vital role in impacting the brand loyalty of the consumers and are giving a fight to the celebrity endorsers as well. Also, Brand image also has a very vital role to play in enhancing the brand loyalty of a brand in the minds of the consumers as a well-known and a better perception of a brand leads to retention of more and more consumers. Also, both Celebrity endorsement and Social media influencers are two-way swords as both have a number of positives and a number of negatives as well, so these are to be compared keeping in mind their adverse effects. Examination of the current market situation has shown that the recommendations of celebrities when properly integrated by comparing product strengths. Advertisers agree that celebrity authorization does not guarantee sales but it can create buzz and make the consumer feel better by-product, which is also what customers should expect as a real star by delivering the promise. On the other hand, depending on the results of the studies, there should be a variety of conclusions planned. Some of the influential people on social media had a positive impact on the product portrait. One of the conclusions is that the product image had a positive impact on consumers. Moreover, the results of the following study states that the most influential influencers consumers in their intended purpose of the purchase, but instead produced a positive result indirectly with Brand image which would further lead to brand loyalty .

Keywords: brand image, brand loyalty, celebrity endorsement, social media influencer

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Authors: Eid Alharbi, Yaser Miaji

Abstract:

The importance of fruit quality and freshness is potential in today’s life. Most recent studies show and automatic online sorting system according to the internal disorder for fresh apple fruit has developed by using near infrared (NIR) spectroscopic technology. The automatic conveyer belts system along with sorting mechanism was constructed. To check the internal quality of the apple fruit, apple was exposed to the NIR radiations in the range 650-1300nm and the data were collected in form of absorption spectra. The collected data were compared to the reference (data of known sample) analyzed and an electronic signal was pass to the sorting system. The sorting system was separate the apple fruit samples according to electronic signal passed to the system. It is found that absorption of NIR radiation in the range 930-950nm was higher in the internally defected samples as compared to healthy samples. On the base of this high absorption of NIR radiation in 930-950nm region the online sorting system was constructed.

Keywords: mechatronics design, NIR, fruit quality, spectroscopic technology

Procedia PDF Downloads 397