Search results for: image and telemetric data

Authors: Ajish Sreedharan

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Visual cryptography encodes a secret image into shares of random binary patterns. If the shares are exerted onto transparencies, the secret image can be visually decoded by superimposing a qualified subset of transparencies, but no secret information can be obtained from the superposition of a forbidden subset. The binary patterns of the shares, however, have no visual meaning and hinder the objectives of visual cryptography. In the Secret Message Transmission through Meaningful Shares a secret message to be transmitted is converted to grey scale image. Then (2,2) visual cryptographic shares are generated from this converted gray scale image. The shares are encrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform. Two separate color images which are of the same size of the shares, taken as cover image of the respective shares to hide the shares into them. The encrypted shares which are covered by meaningful images so that a potential eavesdropper wont know there is a message to be read. The meaningful shares are transmitted through two different transmission medium. During decoding shares are fetched from received meaningful images and decrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform. The shares are combined to regenerate the grey scale image from where the secret message is obtained.

Keywords: visual cryptography, wavelet transform, meaningful shares, grey scale image

Procedia PDF Downloads 457

Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

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In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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

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

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Authors: Abigail Qian Zhou

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Since China has entered an era of mass tourism, there has been a fundamental change in the way Chinese people approach and perceive the image of other countries. With the advent of the new media era, social networking sites such as Sina Weibo have become a tool for many foreign governmental organizations to spread and promote their national image. Among them, the Japan National Tourism Organization (JNTO) was one of the first foreign official tourism agencies to register with Sina Weibo and actively implement communication activities. Due to historical and political reasons, cognition of Japan's national image by the Chinese has always been complicated and contradictory. However, since 2015, China has become the largest source of tourists visiting Japan. This clearly indicates that the broadening of Japan's national image in China has been effective and has value worthy of reference in promoting a positive Chinese perception of Japan and encouraging Japanese tourism. Within this context and using the method of content analysis in media studies through content mining software, this study analyzed how JNTO’s Sina Weibo accounts have constructed and spread Japan's national image. This study also summarized the characteristics of its content and form, and finally revealed the strategy of JNTO in building its international image. The findings of this study not only add a tourism-based perspective to traditional national image communications research, but also provide some reference for the effective international dissemination of national image in the future.

Keywords: national image, international communication, tourism, Japan, China

Procedia PDF Downloads 130

Authors: Nour-Elhouda Lecheheb

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Destination image is one of the main factors that influence potential visitors' decision choice. This study aims to explore the pre-visit perception of prior British tourists and compare them to the actual projected images of the Algerian tourism suppliers. Semi-structured interviews are conducted with both prior British tourists to Algeria and the Algerian tourism suppliers in 2019. The findings of this study suggest how the Algerian tourism suppliers might benefit from understanding the perceived image of prior tourists to match tourists' expectations and better plan their projected images.

Keywords: Algeria, destination choice, destination image, perceived image, projected image

Procedia PDF Downloads 172

Authors: Adnan A. Y. Mustafa

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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

Procedia PDF Downloads 155

Authors: Michael Zimba

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A novel set of features for copy-move image forgery, CMIF, detection method is proposed. The proposed set presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilised to extract robust features. The extracted features are shown to be invariant to additive noise, JPEG compression, and affine transformation. The proposed features can also be used in general object matching.

Keywords: virtual electrostatic field, features, affine transformation, copy-move image forgery

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Authors: Suangsuda Subjaroen, Chutikarn Sriviboon, Rosjana Chandhasa

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This research aims to examine the graduates' overall satisfaction and influential factors that affect the image of Suan Sunandha Rajabhat University, according to the graduates' viewpoints on the graduation rehearsal day. In accordance with the graduates' perceptions, the study is related to the levels of graduates' satisfaction, their perceived quality, perceived value, and the image of Suan Sunandha Rajabhat University. The sample group in this study involved 1,129 graduates of Suan Sunandha Rajabhat University who attended on 2019 graduation rehearsal day. A questionnaire was used as an instrument in order to collect data. By the use of computing software, the statistics used for data analysis were various, ranging from frequencies, percentage, mean, and standard deviation, One-Way ANOVA, and Multiple Regression Analysis. The majority of participants were graduates with a bachelor's degree, followed by masters graduates and PhD graduates, respectively. Among the participants, most of them graduated from the Faculty of Management Sciences, followed by the Faculty of Humanities and Social Sciences and Faculty of Education, respectively. Overall, the graduates were satisfied with the graduation rehearsal day, and each aspect was rated at a satisfactory level. Formality, steps, and procedures were the aspects that graduates were most satisfied with, followed by graduation rehearsal personnel and staff, venue, and facilities. Referring to graduates' perceptions, the perceived quality was rated at a very good level, the perceived value was at a good level, whereas the image of Suan Sunandha Rajabhat University was perceived at a good level, respectively. There were differences in satisfaction levels among graduates with a bachelor's degree, graduates with a master's degree and a doctoral degree with statistical significance at the level of 0.05. There was a statistical significance at the level of 0.05 in perceived quality and perceived value affecting the image of Suan Sunandha Rajabhat University. The image of Suan Sunandha Rajabhat University influenced graduates' satisfaction level with statistical significance at the level of 0.01.

Keywords: university image, perceived quality, perceived value, intention to study higher education, intention to recommend the university to others

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Authors: Andrew R. Willis, Kevin Brink, Kathleen Dipple

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This article describes a software package called ROS-georegistration intended for use with the robot operating system (ROS) and the Gazebo 3D simulation environment. ROSgeoregistration provides tools for the simulation, test, and deployment of aerial georegistration algorithms and is available at github.com/uncc-visionlab/rosgeoregistration. A model creation package is provided which downloads multi-spectral images from the Google Earth Engine database and, if necessary, incorporates these images into a single, possibly very large, reference image. Additionally a Gazebo plugin which uses the real-time sensor pose and image formation model to generate simulated imagery using the specified reference image is provided along with related plugins for UAV relevant data. The novelty of this work is threefold: (1) this is the first system to link the massive multi-spectral imaging database of Google’s Earth Engine to the Gazebo simulator, (2) this is the first example of a system that can simulate geospatially and radiometrically accurate imagery from multiple sensor views of the same terrain region, and (3) integration with other UAS tools creates a new holistic UAS simulation environment to support UAS system and subsystem development where real-world testing would generally be prohibitive. Sensed imagery and ground truth registration information is published to client applications which can receive imagery synchronously with telemetry from other payload sensors, e.g., IMU, GPS/GNSS, barometer, and windspeed sensor data. To highlight functionality, we demonstrate ROSgeoregistration for simulating Electro-Optical (EO) and Synthetic Aperture Radar (SAR) image sensors and an example use case for developing and evaluating image-based UAS position feedback, i.e., pose for image-based Guidance Navigation and Control (GNC) applications.

Keywords: EO-to-EO, EO-to-SAR, flight simulation, georegistration, image generation, robot operating system, vision-based navigation

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Authors: Jun Seung Woo, Shin Dong Ho

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The Image Captioning models with Attention technology have developed significantly compared to previous models, but it is still unsatisfactory in recognizing images. We perform an extensive search over seven interesting Convolutional Neural Networks(CNN) architectures to analyze the behavior of different models for image captioning. We compared seven different CNN Architectures, according to batch size, using on public benchmarks: MS-COCO datasets. In our experimental results, DenseNet and InceptionV3 got about 14% loss and about 160sec training time per epoch. It was the most satisfactory result among the seven CNN architectures after training 50 epochs on GPU.

Keywords: deep learning, image captioning, CNN architectures, densenet, inceptionV3

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Authors: Eman A. Gani, Loay E. George, Faisel G. Mohammed, Kamal H. Sager

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Digital retinal images are more appropriate for automatic screening of diabetic retinopathy systems. Unfortunately, a significant percentage of these images are poor quality that hinders further analysis due to many factors (such as patient movement, inadequate or non-uniform illumination, acquisition angle and retinal pigmentation). The retinal images of poor quality need to be enhanced before the extraction of features and abnormalities. So, the segmentation of retinal image is essential for this purpose, the segmentation is employed to smooth and strengthen image by separating the background and damaged areas from the overall image thus resulting in retinal image enhancement and less processing time. In this paper, methods for segmenting colored retinal image are proposed to improve the quality of retinal image diagnosis. The methods generate two segmentation masks; i.e., background segmentation mask for extracting the background area and poor quality mask for removing the noisy areas from the retinal image. The standard retinal image databases DIARETDB0, DIARETDB1, STARE, DRIVE and some images obtained from ophthalmologists have been used to test the validation of the proposed segmentation technique. Experimental results indicate the introduced methods are effective and can lead to high segmentation accuracy.

Keywords: retinal images, fundus images, diabetic retinopathy, background segmentation, damaged areas segmentation

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Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

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To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: Hamed Qazanfari, Hamid Hassanpour, Kazem Qazanfari

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In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods.

Keywords: content-based image retrieval, color difference histogram, efficient features selection, entropy, correlation

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Authors: M. C. Paliwal, A. K. Jain, S. K. Katiyar

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Classification of satellite imagery is very important for the assessment of its accuracy. In order to determine the accuracy of the classified image, usually the assumed-true data are derived from ground truth data using Global Positioning System. The data collected from satellite imagery and ground truth data is then compared to find out the accuracy of data and error matrices are prepared. Overall and individual accuracies are calculated using different methods. The study illustrates advanced classification and accuracy assessment of land use/land cover mapping using satellite imagery. IRS-1C-LISS IV data were used for classification of satellite imagery. The satellite image was classified using the software in fourteen classes namely water bodies, agricultural fields, forest land, urban settlement, barren land and unclassified area etc. Classification of satellite imagery and calculation of accuracy was done by using ERDAS-Imagine software to find out the best method. This study is based on the data collected for Bhopal city boundaries of Madhya Pradesh State of India.

Keywords: resolution, accuracy assessment, land use mapping, satellite imagery, ground truth data, error matrices

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

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Authors: Terungwa Simon Yange, Agana Moses A.

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This paper studied steganography and designed a simplistic approach to a steganographic tool for hiding information in image files with the view of addressing the security challenges with data by hiding data from unauthorized users to improve its security. The Structured Systems Analysis and Design Method (SSADM) was used in this work. The system was developed using Java Development Kit (JDK) 1.7.0_10 and MySQL Server as its backend. The system was tested with some hypothetical health records which proved the possibility of protecting data from unauthorized users by making it secret so that its existence cannot be easily recognized by fraudulent users. It further strengthens the confidentiality of patient records kept by medical practitioners in the health setting. In conclusion, this work was able to produce a user friendly steganography software that is very fast to install and easy to operate to ensure privacy and secrecy of sensitive data. It also produced an exact copy of the original image and the one carrying the secret message when compared with each.

Keywords: steganography, cryptography, encryption, decryption, secrecy

Procedia PDF Downloads 267

Authors: Yanwen Li, Shuguo Xie

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In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

Keywords: gradient image, segmentation and extract, mean-shift algorithm, dictionary iearning

Procedia PDF Downloads 267

Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Yousif Mohamed Y. Abdallah, Mommen A. Alkhir, Amel S. Algaddal

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This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI.

Keywords: improvement, brain, matlab, markers, boundaries

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Authors: Jiraporn Prommaha

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The purpose of this research is to study the development of Thailand Brand Identity and the perception of its image in order to find any guidelines for the identity development and the image perception of Thailand Brand in Health and Wellness Tourism. The paper is conducted through mixed methods research, both the qualitative and quantitative researches. The qualitative focuses on the in-depth interview of executive administrations from public and private sectors involved scholars and experts in identity and image issue, main 11 people. The quantitative research was done by the questionnaires to collect data from foreign tourists 800; Chinese tourists 400 and UK tourists 400. The technique used for this was the Exploratory Factor Analysis (EFA), this was to determine the relation between the structures of the variables by categorizing the variables into group by applying the Varimax rotation technique. This technique showed recognition the Thailand brand image related to the 2 countries, China and UK. The results found that guidelines for brand identity development and image perception of health and wellness tourism in Thailand; as following (1) Develop communication in order to understanding of the meaning of the word 'Health and beauty tourism' throughout the country, (2) Develop human resources as a national agenda, (3) Develop awareness rising in the conservation and preservation of natural resources of the country, (4) Develop the cooperation of all stakeholders in Health and Wellness Businesses, (5) Develop digital communication throughout the country and (6) Develop safety in Tourism.

Keywords: brand identity, image perception, nation branding, health and wellness tourism, mixed methods research

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Authors: Yao-Hong Tsai

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This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Shola Haruna Adeosun, Ajoke F. Adebiyi

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Business organizations in Nigeria have failed to realize the role of a good corporate image policy in business dealings. This is probably because they do not understand the concept of corporate image and the necessary tools for promoting it. Corporate image goes beyond attractive products or rendering quality services, advertising and paying good salary. It pervades every aspect of business concern, from the least worker’s personality to the dealings within the organization and with the large society. In the face of the societal dynamics, especially in the business world, brought by technology, companies are faced with stiff competition that maintaining a competitive edge requires aggressive strategies. One of such strategies in effective corporate image management is promotion. This study investigates the strategies that could be deployed in order to build and promote the effective corporate image, as well as enhance profit margins of an organization, using Phinomar Nigeria Limited, Ngwo as case study. The study reveals that Phinomar Nigeria Limited has a laid down corporate image policy but not effectively managed; and that, strategies deployed to promote corporate image are limited; while responses to Phinomar products are fairly high. It, therefore, suggests profitable products but requires periodical improvement in the employee's welfare and work environment; as well as, the need to increase the scope of Phinomar’s social responsibility.

Keywords: corporate image, effective, enhancing, management, profitability, strategy

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Authors: M. V. Chidananda Murthy, M. Z. Kurian, H. S. Guruprasad

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Image restoration in the process of communication is one of the emerging fields in the image processing. The motion analysis processing is the simplest case to detect motion in an image. Applications of motion analysis widely spread in many areas such as surveillance, remote sensing, film industry, navigation of autonomous vehicles, etc. The scene may contain multiple moving objects, by using motion analysis techniques the blur caused by the movement of the objects can be enhanced by filling-in occluded regions and reconstruction of transparent objects, and it also removes the motion blurring. This paper presents the design and comparison of various motion detection and enhancement filters. Median filter, Linear image deconvolution, Inverse filter, Pseudoinverse filter, Wiener filter, Lucy Richardson filter and Blind deconvolution filters are used to remove the blur. In this work, we have considered different types and different amount of blur for the analysis. Mean Square Error (MSE) and Peak Signal to Noise Ration (PSNR) are used to evaluate the performance of the filters. The designed system has been implemented in Matlab software and tested for synthetic and real-time images.

Keywords: image enhancement, motion analysis, motion detection, motion estimation

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Authors: Salah Abdul Hameed Saleh, Ghada Hasan

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The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city- IRAQ. The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations. The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS). The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and root-mean-square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration with an acceptable level of accuracy.

Keywords: air pollution, PM10 concentration, Lansat8 OLI image, reflectance, multispectral algorithms, Kirkuk area

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Authors: Misha Alexander, S. B. Waykar

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Visual Cryptography is a special unbreakable encryption technique that transforms the secret image into random noisy pixels. These shares are transmitted over the network and because of its noisy texture it attracts the hackers. To address this issue a Natural Visual Secret Sharing Scheme (NVSS) was introduced that uses natural shares either in digital or printed form to generate the noisy secret share. This scheme greatly reduces the transmission risk but causes distortion in the retrieved secret image through variation in settings and properties of digital devices used to capture the natural image during encryption / decryption phase. This paper proposes a new NVSS scheme that extracts the secret key from randomly selected unaltered multiple natural images. To further improve the security of the shares data hiding techniques such as Steganography and Alpha channel watermarking are proposed.

Keywords: decryption, encryption, natural visual secret sharing, natural images, noisy share, pixel swapping

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Authors: Kevin Wongleedee

Abstract:

The purposes of this research were to study the image of a flight attendant career from the perspective of high school students in Bangkok and to study the level of interest to pursue a flight attendant career. A probability random sampling of 400 students was utilized. Half the sample group came from private high schools and the other half came from public high schools. A questionnaire was used to collect the data and small in-depth interviews were also used to get their opinions about the image and their level of interest in the flight attendant career. The findings revealed that the majority of respondents had a medium level of interest in the flight attendant career. High school students who majored in Math-English were more interested in a flight attendant career than high school students who majored in Science-Math with a 0.05 level of significance. The image of flight attendant career was rated as a good career with a chance to travel to many countries. The image of flight attendance career can be ranked as follows: a career with a chance to travel, a career with ability to speak English, a career that requires punctuality, a career with a good service mind, and a career with an understanding of details. The findings from the in-depth interviews revealed that the major obstacles that prevented high school students from choosing a flight attendant as a career were their ability to speak English, their body proportions, and lack of information.

Keywords: flight attendant, high school students, image, media engineering

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Authors: T. G. Asmitha Chandini

Abstract:

Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking.

Keywords: Query, keyword, image, re-ranking, semantic, signature

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