Search results for: content based image retrieval (CBIR)

Authors: Edward Fosu

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Green innovation has emerged as a significant environmental concern across the world. Green innovation refers to the utilization of technological developments that facilitate energy savings and waste material recycling. The stakeholder theory and resourced-based theory were used to examine how stakeholders' expectations affect corporate green innovation activities and how corporate innovation initiatives affect the corporate image and social performance. This study used structural equation modelling (SEM) and hierarchical regression to test the effects of environmental corporate social responsibility on social performance through mediators: green innovation and corporate image. A quantitative design was employed using data from Chinese companies in Ghana for this study. The study assessed. The results revealed that environmental practices promote corporate social performance (β = 0.070, t = 1.974, p = 0.049), positively affect green product innovation (β = 0.251, t = 7.478, p < 0.001), and has direct effect on green process innovation (β = 0.174, t = 6.192, p < 0.001). Green product innovation and green process innovation significantly promote corporate image respectively (β = 0.089, t = 2.581, p = 0.010), (β = 0.089, t = 2.367, p = 0.018). Corporate image has significant direct effects on corporate social performance (β = 0.146, t = 4.256, p < 0.001). Corporate environmental practices have an impact on the development of green products and processes which promote companies’ social performance. Additionally, evidence supports that corporate image influences companies’ social performance.

Keywords: environmental CSR, corporate image, green innovation, coprorate social performance

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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: Tabassum Husain, Shen Peng Li, Zhaolin Chen

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This paper evaluates the usability of 3D Guided Image Filtering to enhance the quality of short-time binned dynamic PET images by using MRI images. Guided image filtering is an edge-preserving filter proposed to enhance 2D images. The 3D filter is applied on 1 and 5-minute binned images. The results are compared with 15-minute binned images and the Gaussian filtering. The guided image filter enhances the quality of dynamic PET images while also preserving important information of the voxels.

Keywords: dynamic PET images, guided image filter, image enhancement, information preservation filtering

Procedia PDF Downloads 132

Authors: Zhiwei Yan, Jong-Hyouk Lee, Yong-Jin Park, Xiaodong Lee

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In this paper, we analyze NEtwork MObility (NEMO) supporting problems in Content-Centric Networking (CCN), and propose the CCN-NEMO which can well support the deployment of the content-centric paradigm in large-scale mobile Internet. The CCN-NEMO extends the signaling message of the basic CCN protocol, to support the mobility discovery and fast trigger of Interest re-issuing during the network mobility. Besides, the Mobile Router (MR) is extended to optimize the content searching and relaying in the local subnet. These features can be employed by the nested NEMO to maximize the advantages of content retrieving with CCN. Based on the analysis, we compare the performance on handover latency between the basic CCN and our proposed CCN-NEMO. The results show that our scheme can facilitate the content-retrieving in the NEMO scenario with improved performance.

Keywords: NEMO, CCN, mobility, handover latency

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Authors: Y. Bablu Singh, B. S. Purkayashtha, Chungkham Yashawanta Singh

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Morphological analysis forms the basic foundation in NLP applications including syntax parsing Machine Translation (MT), Information Retrieval (IR) and automatic indexing in all languages. It is the field of the linguistics; it can provide valuable information for computer based linguistics task such as lemmatization and studies of internal structure of the words. Computational Morphology is the application of morphological rules in the field of computational linguistics, and it is the emerging area in AI, which studies the structure of words, which are formed by combining smaller units of linguistics information, called morphemes: the building blocks of words. Morphological analysis provides about semantic and syntactic role in a sentence. It analyzes the Manipuri word forms and produces several grammatical information associated with the words. The Morphological Analyzer for Manipuri has been tested on 3500 Manipuri words in Shakti Standard format (SSF) using Meitei Mayek as source; thereby an accuracy of 80% has been obtained on a manual check.

Keywords: morphological analysis, machine translation, computational morphology, information retrieval, SSF

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Authors: Bum-Soo Kim, Jin-Uk Kim

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In this paper, we design and implement a partial denoising boundary image matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI (graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client, and sends the resulting images to the client. Experimental results show that our system provides much intuitive and accurate matching result.

Keywords: boundary image matching, indexing, partial denoising, time-series matching

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Authors: Shangdong Zhu, Yunzhou Zhang, Jie Zhang, Hang Hu, Yazhou Zhang

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Image stitching is a very important branch in the field of computer vision, especially for panoramic map. In order to eliminate shape distortion, a novel stitching method is proposed based on gradually changing matrix when images are horizontal. For images captured horizontally, this paper assumes that there is only translational operation in image stitching. By analyzing each parameter of the homography matrix, the global homography matrix is gradually transferred to translation matrix so as to eliminate the effects of scaling, rotation, etc. in the image transformation. This paper adopts matrix approximation to get the minimum value of the energy function so that the shape distortion at those regions corresponding to the homography can be minimized. The proposed method can avoid multiple horizontal images stitching failure caused by accumulated shape distortion. At the same time, it can be combined with As-Projective-As-Possible algorithm to ensure precise alignment of overlapping area.

Keywords: image stitching, gradually changing matrix, horizontal direction, matrix approximation, homography matrix

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Authors: Boukhatem Mohammed Belkaid, Lahdir Mourad

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In this paper, we propose a new encryption system for security issues meteorological images from Meteosat Second Generation (MSG), which generates 12 images every 15 minutes. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every 15 minutes that will be used to encrypt each frame of the MSG meteorological basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, satellite MSG, encryption, decryption, key, correlation

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Authors: Harsh Vikram Singh, Ankur Rai, Anand Mohan

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In this paper, we propose secure and robust data hiding algorithm based on DCT by Arnold transform and chaotic sequence. The watermark image is scrambled by Arnold cat map to increases its security and then the chaotic map is used for watermark signal spread in middle band of DCT coefficients of the cover image The chaotic map can be used as pseudo-random generator for digital data hiding, to increase security and robustness .Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using bit error rate (BER), normalized correlation (NC), and peak signal to noise ratio (PSNR) value for different watermark and cover images such as Lena, Girl, Tank images and gain factor .We use a binary logo image and text image as watermark. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression as well as other attacks such as addition of noise, low pass filtering and cropping attacks compared to other existing algorithm using DCT coefficients. Moreover, to recover watermarks in proposed algorithm, there is no need to original cover image.

Keywords: data hiding, watermarking, DCT, chaotic sequence, arnold transforms

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Authors: Yi Shan Wu, Ting Hsuan Wu, Li Wei Cheng, Pei Yu Guo

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Given the fiercely competitive environment, human capital is one of the most valuable assets in a commercial enterprise. Therefore, developing strategies to acquire more talents is crucial. Talents are mainly attracted by both internal and external employer brands as well as by the messages conveyed from the employer image. This not only manifests the importance of a brand and an image of an organization but shows people might be affected by their personal values when assessing an organization as an employer. The goal of the present study is to examine the association between employer brand, employer image, and the likelihood of increasing organizational attractiveness. In addition, we draw from social identity theory to propose value congruence may affect the relationship between employer brand and employer image. Data was collected from those people who only worked less than a year in the industry via an online survey (N=209). The results show that employer image partly mediates the effect of employer brand on organizational attractiveness. In addition, the results also suggest that value congruence does not moderate the relationship between employer brand and employer image. These findings explain why building a good employer brand could enhance organization attractiveness and indicate there should be other factors that may affect employer image building, offering directions for future research.

Keywords: organizational attractiveness, employer brand, employer image, value congruence

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Authors: S. Sarumathi, C. Deepadharani, Garimella Archana, S. Dakshayani, D. Logeshwaran, D. Jayakumar, Vijayarangan Natarajan

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The need of the hour for the customers who use a dial-up or a low broadband connection for their internet services is to access HD video data. This can be achieved by developing a new video format using H. 265. This is the latest video codec standard developed by ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) on April 2013. This new standard for video compression has the potential to deliver higher performance than the earlier standards such as H. 264/AVC. In comparison with H. 264, HEVC offers a clearer, higher quality image at half the original bitrate. At this lower bitrate, it is possible to transmit high definition videos using low bandwidth. It doubles the data compression ratio supporting 8K Ultra HD and resolutions up to 8192×4320. In the proposed model, we design a new video format which supports this H. 265 standard. The major areas of applications in the coming future would lead to enhancements in the performance level of digital television like Tata Sky and Sun Direct, BluRay Discs, Mobile Video, Video Conferencing and Internet and Live Video streaming.

Keywords: access HD video, H. 265 video standard, high performance, high quality image, low bandwidth, new video format, video streaming applications

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Authors: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

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Authors: Elhussaien Elshiekh

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A study was performed to management and compare radiation doses and image quality during Lumbar spine PA and Lumbar spine LAT, x- ray radiography using Computed Radiography (CR) and Digital Radiography (DR). Standard exposure factors such as kV, mAs and FFD used for imaging the Lumbar spine anthropomorphic phantom obtained from average exposure factors that were used with CR in five radiology centres. Lumbar spine phantom was imaged using CR and DR systems. Entrance surface air kerma (ESAK) was calculated X-ray tube output and patient exposure factor. Images were evaluated using visual grading system based on the European Guidelines on Quality Criteria for diagnostic radiographic images. The ESAK corresponding to each image was measured at the surface of the phantom. Six experienced specialists evaluated hard copies of all the images, the image score (IS) was calculated for each image by finding the average score of the Six evaluators. The IS value also was used to determine whether an image was diagnostically acceptable. The optimum recommended exposure factors founded here for Lumbar spine PA and Lumbar spine LAT, with respectively (80 kVp,25 mAs at 100 cm FFD) and (75 kVp,15 mAs at 100 cm FFD) for CR system, and (80 kVp,15 mAs at100 cm FFD) and (75 kVp,10 mAs at 100 cm FFD) for DR system. For Lumbar spine PA, the lowest ESAK value required to obtain a diagnostically acceptable image were 0.80 mGy for DR and 1.20 mGy for CR systems. Similarly for Lumbar spine LAT projection, the lowest ESAK values to obtain a diagnostically acceptable image were 0.62 mGy for DR and 0.76 mGy for CR systems. At standard kVp and mAs values, the image quality did not vary significantly between the CR and the DR system, but at higher kVp and mAs values, the DR images were found to be of better quality than CR images. In addition, the lower limit of entrance skin dose consistent with diagnostically acceptable DR images was 40% lower than that for CR images.

Keywords: image quality, dosimetry, radiation protection, optimization, digital radiography, computed radiography

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Authors: Abigail Qian Zhou

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In recent years, the prosperity of mass tourism in China has accelerated the breadth and depth of direct communication between countries, and the national image has been placed in a new communication context. Outbound tourists are not only directly involved in the formation of the national image, but are also the most direct medium and the most active symbol representing the national image. This study uses Chinese middle-class tourists visiting Japan as a case study, and analyzes, through participant observation and semi-structured interviews, the communication function of the national image transmitted by 'human medium' in tourism activities. It also explores the 'human medium' in the era of mass tourism. This study hopes to build a bridge for tourism research and national image and media studies. It will provide a theoretical basis and practical guidance for promoting the national image, strengthening exchanges between tourists and local populations, and expanding the tourism market in the future.

Keywords: human medium, national image, communication, Chinese middle class, outbound tourists

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Authors: Murray Olowa

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This article examines the studies investigating the Mathematical Knowledge for Teaching (MKT) of secondary preservice teachers in Papua New Guinea based on probabilities. This research was conducted due to the continuous issues faced in the country in both primary and secondary education, like changes in curriculum, emphasis on mathematics and science education, and a decline in mathematics performance. Moreover, the mathematics curriculum doesn’t capture Pedagogical Content Knowledge (PCK) or Subject Matter Knowledge (SMK). The two main domains that have been identified are SMK and PCK, which have been further sub-divided into Common Content Knowledge (CCK), Specialised Content Knowledge (SCK) and Horizon Content Knowledge (HCK), and Knowledge of Content and Students (KCS), Knowledge of Content and Teaching (KCT) and Knowledge of Content and Curriculum (KCC), respectively. The data collected from 15-_year-_ ones and 15-_year-_fours conducted at St Peter Chanel Secondary Teachers College revealed that there is no significant difference in subject matter knowledge between year one and year four since the P-value of 0.22>0.05. However, it was revealed that year fours have higher pedagogical content knowledge than year one since P-value was 0.007<0.05. Finally, the research has proven that year fours have higher MKT than year one. This difference occurred due to final year preservice teachers’ hard work and engagement in mathematics curriculum and teaching practice.

Keywords: mathematical knowledge for teaching, subject matter knowledge, pedagogical content knowledge, Papua New Guinea, preservice teachers, probability

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Authors: Razan Farah, Siham Ballah

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Facebook, Instagram, TikTok and other social media applications have become an integral component of everyone’s social life, particularly among younger generations and adolescences. These social apps have been changing a lot of conceptions and believes in the population by representing public figures and celebrities as role models. The social comparison theory, which says that people self-evaluate based on comparisons with similar others, is commonly used to explore the impact of social media on body image. There is a need to study the influence of those social platforms on the body image as there have been an increase in body dissatisfaction in the recent years. This cross sectional study used a self administered questionnaire on a simple random sample of 133 female medical students of the first year. Finding shows that the response rate was 75%. There was an association between social media usage and noticing how the person look(p value = .022), but no significant association between social media use and body image influence or dissatisfaction was found. This study implies more research under this topic in Sudan as the literature are scarce.

Keywords: body image, body dissatisfaction, social media, adolescences

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Authors: S. Vignesh, K. S. Rangasamy

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The paper deals with the device capable of detecting and disabling digital cameras. The system locates the camera and then neutralizes it. Every digital camera has an image sensor known as a CCD, which is retro-reflective and sends light back directly to its original source at the same angle. The device shines infrared LED light, which is invisible to the human eye, at a distance of about 20 feet. It then collects video of these reflections with a camcorder. Then the video of the reflections is transferred to a computer connected to the device, where it is sent through image processing algorithms that pick out infrared light bouncing back. Once the camera is detected, the device would project an invisible infrared laser into the camera's lens, thereby overexposing the photo and rendering it useless. Low levels of infrared laser neutralize digital cameras but are neither a health danger to humans nor a physical damage to cameras. We also discuss the simplified design of the above device that can used in theatres to prevent piracy. The domains being covered here are optics and image processing.

Keywords: CCD, optics, image processing, D3CIP

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Authors: Efthymios Chondrogiannis, Vassiliki Andronikou, Efstathios Karanastasis, Theodora Varvarigou

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A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries.

Keywords: ontologies, relational databases, SPARQL, web interface

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Shubham V. Kamdi

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This paper aims to serve as an introduction to image resource caching techniques for iOS mobile applications. It will explain how developers can break down multiple image-downloading tasks concurrently using state-of-the-art iOS frameworks, namely Swift Concurrency and Combine. The paper will explain how developers can leverage SwiftUI to develop reactive view components and use declarative coding patterns. Developers will learn to bypass built-in image caching systems by curating the procedure to implement a swift-based LRU cache system. The paper will provide a full architectural overview of a system, helping readers understand how mobile applications are designed professionally. It will cover technical discussion, helping readers understand the low-level details of threads and how they can switch between them, as well as the significance of the main and background threads for requesting HTTP services via mobile applications.

Keywords: main thread, background thread, reactive view components, declarative coding

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Authors: P. Strakos, M. Jaros, T. Karasek, T. Kozubek, P. Vavra, T. Jonszta

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In medical imaging, segmentation of different areas of human body like bones, organs, tissues, etc. is an important issue. Image segmentation allows isolating the object of interest for further processing that can lead for example to 3D model reconstruction of whole organs. Difficulty of this procedure varies from trivial for bones to quite difficult for organs like liver. The liver is being considered as one of the most difficult human body organ to segment. It is mainly for its complexity, shape versatility and proximity of other organs and tissues. Due to this facts usually substantial user effort has to be applied to obtain satisfactory results of the image segmentation. Process of image segmentation then deteriorates from automatic or semi-automatic to fairly manual one. In this paper, overview of selected available software applications that can handle semi-automatic image segmentation with further 3D volume reconstruction of human liver is presented. The applications are being evaluated based on the segmentation results of several consecutive DICOM images covering the abdominal area of the human body.

Keywords: image segmentation, semi-automatic, software, 3D volumetric reconstruction

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Authors: Adeela Abu Bakar, Minder Kaur, Parthaman Singh

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In the Malaysian education system, a formal setting of English language learning takes place in a content-based classroom (CBC). Until recently, there is less study in Malaysia, which researched the effects of code-switching (CS) behaviour towards the students’ knowledge sharing (KS) with their peers. The aim of this study is to investigate the frequency, reasons, and effect that CS, from the English language to Bahasa Melayu, has among local STEM and N-STEM undergraduates towards KS in a content-based classroom. The study implies a mixed-method research design with questionnaire and interviews as the instruments. The data is collected through distribution of questionnaires and interviews with the undergraduates. The quantitative data is analysed using SPSS in simple frequencies and percentages, whereas qualitative data involves organizing the data into themes, followed by analysis. Findings found that N-STEM undergraduates code-switch more as compared to STEM undergraduates. In addition to that, both the STEM and N-STEM undergraduates agree that CS acts as a catalyst towards KS in a content-based classroom. However, they also acknowledge that excess use of CS can be a hindrance towards KS. The findings of the study can benefit STEM and N-STEM undergraduates, education policymakers, language teachers, university educators, and students with significant insights into the role of CS towards KS in a content-based classroom. Some of the recommendations that can be applied for future studies are that the number of participants can be increased, an observation to be included for the data collection.

Keywords: switching, content-based classroom, content and language integrated learning, knowledge sharing, STEM and N-STEM undergraduates

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Authors: Ramandeep Kaur M. Malhi, Prashant K. Srivastava, G.Sandhya Kiran

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Quantitative estimates of foliar biochemicals, namely chlorophyll content (CC), serve as key information for the assessment of plant productivity, stress, and the availability of nutrients. This also plays a critical role in predicting the dynamic response of any vegetation to altering climate conditions. The advent of hyperspectral data with an enhanced number of available wavelengths has increased the possibility of acquiring improved information on CC. Retrieval of CC is extensively carried through well known spectral indices derived from hyperspectral data. In the present study, an attempt is made to develop hyperspectral indices by identifying optimum bands for CC estimation in Butea monosperma (Lam.) Taub growing in forests of Shoolpaneshwar Wildlife Sanctuary, Narmada district, Gujarat State, India. 196 narrow bands of EO-1 Hyperion images were screened, and the best optimum wavelength from blue, green, red, and near infrared (NIR) regions were identified based on the coefficient of determination (R²) between band reflectance and laboratory estimated CC. The identified optimum wavelengths were then employed for developing 12 hyperspectral indices. These spectral index values and CC values were then correlated to investigate the relation between laboratory measured CC and spectral indices. Band 15 of blue range and Band 22 of green range, Band 40 of the red region, and Band 79 of NIR region were found to be optimum bands for estimating CC. The optimum band based combinations on hyperspectral data proved to be the most effective indices for quantifying Butea CC with NDVI and TVI identified as the best (R² > 0.7, p < 0.01). The study demonstrated the significance of band characterization in the development of the best hyperspectral indices for the chlorophyll estimation, which can aid in monitoring the vitality of forests.

Keywords: band, characterization, chlorophyll, hyperspectral, indices

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Authors: Jaiden X. Schraut

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Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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Authors: Natalia Polkanova, Sergey Kazakov

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In the research, we proposed the set of factors of tourist destinations attractiveness in Spain and determined the factors that have the greatest impact on the positive perception of the tourist destination by Russian tourists; also, we examined what factors create the willingness for Russians to recommend this tourist destination to their friends and relatives. The tourists' comments on the Russian travel sites have been analyzed in order to determine the frequency of attractiveness characteristics references. Additionally, the study will reflect the relationship of variables.

Keywords: tourism destination, destination attractiveness, destination competitiveness, content analysis, unstructured image

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Authors: Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Mads Dyrmann, Robert Poulsen

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For the past three years, the Danish project, RoboWeedSupport, has sought to bridge the gap between the potential herbicide savings using a decision support system and the required weed inspections. In order to automate the weed inspections it is desired to generate a map of the weed species present within the field, to generate the map images must be captured with samples covering the field. This paper investigates the economical cost of performing this data collection based on a camera system mounted on a all-terain vehicle (ATV) able to drive and collect data at up to 50 km/h while still maintaining a image quality sufficient for identifying newly emerged grass weeds. The economical estimates are based on approximately 100 hectares recorded at three different locations in Denmark. With an average image density of 99 images per hectare the ATV had an capacity of 28 ha per hour, which is estimated to cost 6.6 EUR/ha. Alternatively relying on a boom solution for an existing tracktor it was estimated that a cost of 2.4 EUR/ha is obtainable under equal conditions.

Keywords: weed mapping, integrated weed management, weed recognition, image acquisition

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Authors: Israa Sh. Tawfic, Sema Koc Kayhan

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In this paper, first, we propose least support orthogonal matching pursuit (LS-OMP) algorithm to improve the performance, of the OMP (orthogonal matching pursuit) algorithm. LS-OMP algorithm adaptively chooses optimum L (least part of support), at each iteration. This modification helps to reduce the computational complexity significantly and performs better than OMP algorithm. Second, we give the procedure for the invisible image watermarking in the presence of compressive sampling. The image reconstruction based on a set of watermarked measurements is performed using LS-OMP.

Keywords: compressed sensing, orthogonal matching pursuit, restricted isometry property, signal reconstruction, least support orthogonal matching pursuit, watermark

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping.

Keywords: GF-2 images, feature extraction-rectification, nearest neighbour object based classification, segmentation algorithms, neural network classification, multilayer perceptron

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Authors: Krzysztof Malczewski

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PET is widely applied scanning procedure in medical imaging based research. It delivers measurements of functioning in distinct areas of the human brain while the patient is comfortable, conscious and alert. This article presents the new compression sensing based super-resolution algorithm for improving the image resolution in clinical Positron Emission Tomography (PET) scanners. The issue of motion artifacts is well known in Positron Emission Tomography (PET) studies as its side effect. The PET images are being acquired over a limited period of time. As the patients cannot hold breath during the PET data gathering, spatial blurring and motion artefacts are the usual result. These may lead to wrong diagnosis. It is shown that the presented approach improves PET spatial resolution in cases when Compressed Sensing (CS) sequences are used. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. The application of CS to PET has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the goal is to combine super-resolution image enhancement algorithm with CS framework to achieve high resolution PET output. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity.

Keywords: PET, super-resolution, image reconstruction, pattern recognition

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Authors: Youngseok Kim, Chul Park, Jonghwa Yi, Sangsik Choi

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The 2-MHz Side Scan SONAR (SSS) attached to the boat for inspection of underwater structures is affected by shaking. It is difficult to determine the exact scale of damage of structure. In this study, a motion sensor is attached to the inside of the 2-MHz SSS to get roll, pitch, and yaw direction data, and developed the image stabilization tool to correct the sonar image. We checked that reliable data can be obtained with an average error rate of 1.99% between the measured value and the actual distance through experiment. It is possible to get the accurate sonar data to inspect damage in underwater structure.

Keywords: image stabilization, motion sensor, safety inspection, sonar image, underwater structure

Procedia PDF Downloads 280