Search results for: image and signal processing

Authors: Latha Subbiah, Dhanalakshmi Samiappan

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In this paper, we propose denoising Common Carotid Artery (CCA) B mode ultrasound images by a decomposition approach to curvelet thresholding and automatic segmentation of the intima media thickness and adventitia boundary. By decomposition, the local geometry of the image, its direction of gradients are well preserved. The components are combined into a single vector valued function, thus removes noise patches. Double threshold is applied to inherently remove speckle noise in the image. The denoised image is segmented by active contour without specifying seed points. Combined with level set theory, they provide sub regions with continuous boundaries. The deformable contours match to the shapes and motion of objects in the images. A curve or a surface under constraints is developed from the image with the goal that it is pulled into the necessary features of the image. Region based and boundary based information are integrated to achieve the contour. The method treats the multiplicative speckle noise in objective and subjective quality measurements and thus leads to better-segmented results. The proposed denoising method gives better performance metrics compared with other state of art denoising algorithms.

Keywords: curvelet, decomposition, levelset, ultrasound

Procedia PDF Downloads 340

Authors: Marcos Bosques-Perez, Walter Izquierdo, Harold Martin, Liangdon Deng, Josue Rodriguez, Thony Yan, Mercedes Cabrerizo, Armando Barreto, Naphtali Rishe, Malek Adjouadi

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Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation.

Keywords: big data, image processing, multispectral, principal component analysis

Procedia PDF Downloads 177

Authors: Mahdi Bazarganigilani

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Aortic enlargement, also known as an aortic aneurysm, can occur when the walls of the aorta become weak. This disease can become deadly if overlooked and undiagnosed. In this paper, a computer-aided diagnosis (CAD) system was introduced to accurately diagnose aortic enlargement from chest x-ray images. An enhanced convolutional neural network (CNN) was employed and then trained by transfer learning by using three different main areas from the original images. The areas included the left lung, heart, and right lung. The accuracy of the system was then evaluated on 1001 samples by using 4-fold cross-validation. A promising accuracy of 90% was achieved in terms of the F-measure indicator. The results showed using different areas from the original image in the training phase of CNN could increase the accuracy of predictions. This encouraged the author to evaluate this method on a larger dataset and even on different CAD systems for further enhancement of this methodology.

Keywords: computer-aided diagnosis systems, aortic enlargement, chest X-ray, image processing, convolutional neural networks

Procedia PDF Downloads 162

Authors: K. P. Sandesh, M. H. Suman

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Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures.

Keywords: document classification, document clustering, entropy, accuracy, classifiers, clustering algorithms

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Authors: Amirreza Razzaghipour Sorkhab

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The exploration of the connection between cognition and Auditory Processing Disorder (APD) holds significant value. Individuals with APD experience challenges in processing auditory information through the central auditory nervous system's varied pathways. Understanding the importance of auditory attention in individuals with APD, as well as the primary diagnostic tools such as language and auditory attention tests, highlights the critical need for assessing their auditory attention abilities. While not all children with Auditory Processing Disorder (APD) show deficits in auditory attention, there are often deficiencies in cognitive and attentional performance. The link between various types of attention deficits and APD suggests impairments in sustained and divided auditory attention. Research into the origins of APD should also encompass higher-level processes, such as auditory attention. It is evident that investigating the interaction between APD and auditory and cognitive functions holds significant value. Furthermore, it was demonstrated that APD tests may be influenced by cognitive factors, but despite signs of auditory attention interaction with auditory processing skills and the influence of cognitive factors on tests for this disorder, auditory attention measures are not typically included in APD diagnostic protocols. Therefore, incorporating attention assessment tests into the battery of tests for individuals with auditory processing disorder will be beneficial for obtaining useful insights into their attentional abilities.

Keywords: auditory processing disorder, auditory attention, central auditory processing disorder, top-down pathway

Procedia PDF Downloads 66

Authors: Yasuyuki Takahashi, Hayato Ishimura, Masao Miyagawa, Teruhito Mochizuki

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Quantitative measurement of myocardium perfusion is possible with single photon emission computed tomography (SPECT) using a semiconductor detector. However, accumulation of ^99mTc-tetrofosmin in the liver may make it difficult to assess that accurately in the inferior myocardium. Our idea is to reduce the high accumulation in the liver by using dynamic SPECT imaging and a technique called time subtraction. We evaluated the performance of a new SPECT system with a cadmium-zinc-telluride solid-state semi- conductor detector (Discovery NM 530c; GE Healthcare). Our system acquired list-mode raw data over 10 minutes for a typical patient. From the data, ten SPECT images were reconstructed, one for every minute of acquired data. Reconstruction with the semiconductor detector was based on an implementation of a 3-D iterative Bayesian reconstruction algorithm. We studied 20 patients with coronary artery disease (mean age 75.4 ± 12.1 years; range 42-86; 16 males and 4 females). In each subject, 259 MBq of ^99mTc-tetrofosmin was injected intravenously. We performed both a phantom and a clinical study using dynamic SPECT. An approximation to a liver-only image is obtained by reconstructing an image from the early projections during which time the liver accumulation dominates (0.5~2.5 minutes SPECT image-5~10 minutes SPECT image). The extracted liver-only image is then subtracted from a later SPECT image that shows both the liver and the myocardial uptake (5~10 minutes SPECT image-liver-only image). The time subtraction of liver was possible in both a phantom and the clinical study. The visualization of the inferior myocardium was improved. In past reports, higher accumulation in the myocardium due to the overlap of the liver is un-diagnosable. Using our time subtraction method, the image quality of the ^99mTc-tetorofosmin myocardial SPECT image is considerably improved.

Keywords: 99mTc-tetrofosmin, dynamic SPECT, time subtraction, semiconductor detector

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Authors: Yu Zhang, Kohei Inoue, Kiichi Urahama

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We propose a simplified bilateral filter with binarized coefficients for accelerating it. Its computational cost is further decreased by sampling pixels. This computationally low cost filter is useful for smoothing or denoising images by using mobile devices with limited computational power.

Keywords: bilateral filter, binarized-weight bilateral filter, image smoothing, image denoising, pixel sampling

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Authors: Gorazd Karer

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When performing a diagnostic procedure or surgery in general anesthesia (GA), a proper introduction and dosing of anesthetic agents are one of the main tasks of the anesthesiologist. However, depth of anesthesia (DoA) also seems to be a suitable process for closed-loop control implementation. To implement such a system, one must be able to acquire the relevant signals online and in real-time, as well as stream the calculated control signal to the infusion pump. However, during a procedure, patient monitors and infusion pumps are purposely unable to connect to an external (possibly medically unapproved) device for safety reasons, thus preventing closed-loop control. The paper proposes a conceptual solution to the aforementioned problem. First, it presents some important aspects of contemporary clinical practice. Next, it introduces the closed-loop-control-system structure and the relevant information flow. Focusing on transferring the data from the patient to the computer, it presents a non-invasive image-based system for signal acquisition from a patient monitor for online depth-of-anesthesia assessment. Furthermore, it introduces a UDP-based communication method that can be used for transmitting the calculated anesthetic inflow to the infusion pump. The proposed system is independent of a medical device manufacturer and is implemented in Matlab-Simulink, which can be conveniently used for DoA control implementation. The proposed scheme has been tested in a simulated GA setting and is ready to be evaluated in an operating theatre. However, the proposed system is only a step towards a proper closed-loop control system for DoA, which could routinely be used in clinical practice.

Keywords: closed-loop control, depth of anesthesia (DoA), modeling, optical signal acquisition, patient state index (PSi), UDP communication protocol

Procedia PDF Downloads 217

Authors: Oliveira M., Gonçalves L., Francisco I., Caramelo F., Vale F., Sanz D., Domingues M., Lopes M., Moreia D., Lopes T., Santos T., Cardoso H.

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The ARTHUR project consists of a platform that allows the virtual performance of maxillofacial surgeries, offering, in a photorealistic concept, the possibility for the patient to have an idea of the surgical changes before they are performed on their face. For this, the system brings together several image formats, dicoms and objs that, after loading, will generate the bone volume, soft tissues and hard tissues. The system also incorporates the patient's stereophotogrammetry, in addition to their data and clinical history. After loading and inserting data, the clinician can virtually perform the surgical operation and present the final result to the patient, generating a new facial surface that contemplates the changes made in the bone and tissues of the maxillary area. This tool acts in different situations that require facial reconstruction, however this project focuses specifically on two types of use cases: bone congenital disfigurement and acquired disfiguration such as oral cancer with bone attainment. Being developed a cloud based solution, with mobile support, the tool aims to reduce the decision time window of patient. Because the current simulations are not realistic or, if realistic, need time due to the need of building plaster models, patient rates on decision, rely on a long time window (1,2 months), because they don’t identify themselves with the presented surgical outcome. On the other hand, this planning was performed time based on average estimated values of the position of the maxilla and mandible. The team was based on averages of the facial measurements of the population, without specifying racial variability, so the proposed solution was not adjusted to the real individual physiognomic needs.

Keywords: 3D computing, image processing, image registry, image reconstruction

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Authors: Allan F. F. Alves, Fernando A. Bacchim Neto, Guilherme Giacomini, Marcela de Oliveira, Ana L. M. Pavan, Maria E. D. Rosa, Diana R. Pina

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Stroke is a worldwide concern, only in Brazil it accounts for 10% of all registered deaths. There are 2 stroke types, ischemic (87%) and hemorrhagic (13%). Early diagnosis is essential to avoid irreversible cerebral damage. Non-enhanced computed tomography (NECT) is one of the main diagnostic techniques used due to its wide availability and rapid diagnosis. Detection depends on the size and severity of lesions and the time spent between the first symptoms and examination. The Alberta Stroke Program Early CT Score (ASPECTS) is a subjective method that increases the detection rate. The aim of this work was to implement an image segmentation system to enhance ischemic stroke and to quantify the area of ischemic and hemorrhagic stroke lesions in CT scans. We evaluated 10 patients with NECT examinations diagnosed with ischemic stroke. Analyzes were performed in two axial slices, one at the level of the thalamus and basal ganglion and one adjacent to the top edge of the ganglionic structures with window width between 80 and 100 Hounsfield Units. We used different image processing techniques such as morphological filters, discrete wavelet transform and Fuzzy C-means clustering. Subjective analyzes were performed by a neuroradiologist according to the ASPECTS scale to quantify ischemic areas in the middle cerebral artery region. These subjective analysis results were compared with objective analyzes performed by the computational algorithm. Preliminary results indicate that the morphological filters actually improve the ischemic areas for subjective evaluations. The comparison in area of the ischemic region contoured by the neuroradiologist and the defined area by computational algorithm showed no deviations greater than 12% in any of the 10 examination tests. Although there is a tendency that the areas contoured by the neuroradiologist are smaller than those obtained by the algorithm. These results show the importance of a computer aided diagnosis software to assist neuroradiology decisions, especially in critical situations as the choice of treatment for ischemic stroke.

Keywords: ischemic stroke, image processing, CT scans, Fuzzy C-means

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Authors: Patrik Scajev, Pavels Onufrijevs, Algirdas Mekys, Tadas Malinauskas, Dominykas Augulis, Liudvikas Subacius, Kuo-Chih Lee, Jevgenijs Kaupuzs, Arturs Medvids, Hung Hsiang Cheng

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In this study, we focused on the optoelectronic properties of the photodiodes prepared by using 200 nm thick Ge₀.₉₅Sn₀.₀₅ epitaxial layers on Ge/n-Si substrate with aluminum contacts. Photodiodes were formed on non-irradiated and Nd: YAG laser irradiated Ge₀.₉₅Sn₀.₀₅ layers. The samples were irradiated by pulsed Nd: YAG laser with 136.7-462.6 MW/cm² intensity. The photodiodes were characterized by using short laser pulses with the wavelength in the 2.0-2.6 μm range. The laser-irradiated diode was found more sensitive in the long-wavelength range due to laser-induced Sn atoms redistribution providing formation of graded bandgap structure. Sub-millisecond photocurrent relaxation in the diodes revealed their suitability for image sensors. Our findings open the perspective for improving the photo-sensitivity of GeSn alloys in the mid-infrared by pulsed laser processing.

Keywords: GeSn, laser processing, photodetector, infrared

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Chih-Wen Wu

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This study examines the antecedents of heritage tourism loyalty and its relation to destination image, consumer travel experience, and destination satisfaction in the tourism context. In this respect, a number of important questions concerning how destination image, consumer travel experience, and destination satisfaction impact destination loyalty are raised. This study attempts to identify three key antecedents of loyalty in the heritage context. The author empirically tests predicted relationships by using personal interview data from 475 foreign tourists. The conceptual model investigated the relevant relationships among the constructs by using confirmatory factor analysis(CFA) and structural equation modeling (SEM) approach. Findings from the research sample support the argument that destination image, consumer travel experience, destination satisfaction are the key determinants of destination loyalty. Destination image and consumer travel experience influence destination satisfaction. The author also discusses theoretical and managerial implications of research findings for marketing the heritage globally.

Keywords: heritage, destination loyalty, destination image, consumer travel experience, destination satisfaction, tourism

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Authors: R. Latif, W. Jenkal, A. Toumanari, A. Hatim

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This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques.

Keywords: hybrid technique, ADTF, DWT, thresholding, ECG signal

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Authors: Li Zhou, Yanmei Fang

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With current technology, images and videos can be obtained more easily than ever. It is so easy to manipulate these digital multimedia information when obtained, and that the content or source of the image and video could be easily tampered. In this paper, we propose to identify the image and video frame by the texture-based approach, e.g. Markov Transition Probability (MTP), which is in space domain, DCT domain and DWT domain, respectively. In the experiment, image and video frame database is constructed, and is used to train and test the classifier Support Vector Machine (SVM). Experiment results show that the texture-based approach has good performance. In order to verify the experiment result, and testify the universality and robustness of algorithm, we build a random testing dataset, the random testing result is in keeping with above experiment.

Keywords: multimedia forensics, video frame, LBP, MTP, SVM

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Authors: Hyeon-Seung Kim, Bit-Na Cho, Tae-Woon Jeong, Soo-Young Yoon, Leen-Seok Kang

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Recently, a construction project has been large in the size and complicated in the site work. The web-cameras are used to manage the construction site of such a large construction project. They can be used for monitoring the construction schedule as compared to the actual work image of the planned work schedule. Specially, because the 4D CAD system that the construction appearance is continually simulated in a 3D CAD object by work schedule is widely applied to the construction project, the comparison system between the real image of actual work appearance by web-camera and the simulated image of planned work appearance by 3D CAD object can be an intelligent construction schedule management system (ICON). The delayed activities comparing with the planned schedule can be simulated by red color in the ICON as a virtual reality object. This study developed the ICON and it was verified in a real bridge construction project in Korea. To verify the developed system, a web-camera was installed and operated in a case project for a month. Because the angle and zooming of the web-camera can be operated by Internet, a project manager can easily monitor and assume the corrective action.

Keywords: 4D CAD, web-camera, ICON (intelligent construction schedule management system), 3D object image

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Authors: Jung-In Baik, Seung-Jun Yu, Young-Min Ko, Hyoung-Kyu Song

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This paper proposes an efficient relaying algorithm to obtain diversity for improving the reliability of a signal. The algorithm achieves time or space diversity gain by multiple versions of the same signal through two routes. Relays are separated between a source and destination. The routes between the source and destination are set adaptive in order to deal with different channels and noises. The routes consist of one or more relays and the source transmits its signal to the destination through the routes. The signals from the relays are combined and detected at the destination. The proposed algorithm provides a better performance than the conventional algorithms in bit error rate (BER).

Keywords: multi-hop, OFDM, relay, relaying selection

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Authors: Agastya Pratap Singh

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Generative Adversarial Networks (GANs) have emerged as powerful tools in the fields of image recognition and anomaly detection due to their ability to model complex data distributions and generate realistic images. This systematic review explores recent advancements and applications of GANs in both image recognition and anomaly detection tasks. We discuss various GAN architectures, such as DCGAN, CycleGAN, and StyleGAN, which have been tailored to improve accuracy, robustness, and efficiency in visual data analysis. In image recognition, GANs have been used to enhance data augmentation, improve classification models, and generate high-quality synthetic images. In anomaly detection, GANs have proven effective in identifying rare and subtle abnormalities across various domains, including medical imaging, cybersecurity, and industrial inspection. The review also highlights the challenges and limitations associated with GAN-based methods, such as instability during training and mode collapse, and suggests future research directions to overcome these issues. Through this review, we aim to provide researchers with a comprehensive understanding of the capabilities and potential of GANs in transforming image recognition and anomaly detection practices.

Keywords: generative adversarial networks, image recognition, anomaly detection, DCGAN, CycleGAN, StyleGAN, data augmentation

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Authors: Balgaisha G. Mukanova, Yelbek B. Utepov, Aida G. Nazarova, Alisher Z. Imanov

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The problem of identifying local noise sources on a construction site using a sensor system is considered. Mathematical modeling of detected signals on sensors was carried out, considering signal decay and signal delay time between the source and detector. Recordings of noises produced by construction tools were used as a dependence of noise on time. Synthetic sensor data was constructed based on these data, and a model of the propagation of acoustic waves from a point source in the three-dimensional space was applied. All sensors and sources are assumed to be located in the same plane. A source localization method is checked based on the signal time delay between two adjacent detectors and plotting the direction of the source. Based on the two direct lines' crossline, the noise source's position is determined. Cases of one dominant source and the case of two sources in the presence of several other sources of lower intensity are considered. The number of detectors varies from three to eight detectors. The intensity of the noise field in the assessed area is plotted. The signal of a two-second duration is considered. The source is located for subsequent parts of the signal with a duration above 0.04 sec; the final result is obtained by computing the average value.

Keywords: acoustic model, direction of arrival, inverse source problem, sound localization, urban noises

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Authors: Josef Slapal

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Digital images are approximations of real ones and, therefore, to be able to study them, we need the digital plane Z2 to be equipped with a convenient structure that behaves analogously to the Euclidean topology on the real plane. In particular, it is required that such a structure allows for a digital analogue of the Jordan curve theorem. We introduce certain adjacency graphs on the digital plane and prove digital Jordan curves for them thus showing that the graphs provide convenient structures on Z2 for the study and processing of digital images. Further convenient structures including the wellknown Khalimsky and Marcus-Wyse adjacency graphs may be obtained as quotients of the graphs introduced. Since digital Jordan curves represent borders of objects in digital images, the adjacency graphs discussed may be used as background structures on the digital plane for solving the problems of digital image processing that are closely related to borders like border detection, contour filling, pattern recognition, thinning, etc.

Keywords: digital plane, adjacency graph, Jordan curve, quotient adjacency

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Authors: Fares Alsewailem

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Material recycling of thermoplastic waste offers practical solution for municipal solid waste reduction. Post-consumer plastics such as polyethylene (PE), polyethyleneterephtalate (PET), and polystyrene (PS) may be separated from each other by physical methods such as density difference and hence processed as single plastic, however one should be cautious about the contaminants presence in the waste stream inform of paper, glue, etc. since these articles even in trace amount may deteriorate properties of the recycled plastics especially the mechanical properties. furthermore, melt processing methods used to recycle thermoplastics such as extrusion and compression molding may induce degradation of some of the recycled plastics such as PET and PS. In this research, it is shown that care should be taken when processing recycled plastics by melt processing means in two directions, first contaminants should be extremely minimized, and secondly melt processing steps should also be minimum.

Keywords: Recycling, PET, PS, HDPE, mechanical

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: intelligent transportation system, object detection, vehicle couting, vehicle classification, video processing

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Authors: Ming-Yang Guo, Cheng-Xian Wu, Wei-Xiang Chen, Chun-Yuan Lin, Yen-Jen Lin, Ann-Shyn Chiang

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With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070.

Keywords: Drosophila driver image, Drosophila neuron images, intersection/union computation, parallel processing, OpenMP

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Authors: Lauren Currell, Gemma Hurst

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Introduction: The present study experimentally investigated the impact of attractive Instagram images on female’s body image. It also examined whether physical appearance perfectionism and cognitive coping predicted body image following upward comparisons to idealised bodies on Instagram. Methods: One-hundred and fifty-eight females (mean age 24.35 years) were randomly assigned to an experimental (where they compared their bodies to those of Instagram models) or control condition (where they critiqued landscape painting). All participants completed measures on physical appearance perfectionism, cognitive coping, and pre- and post-measures of body image. Results: Comparing one’s body to idealised bodies on Instagram resulted in increased appearance and weight dissatisfaction and decreased confidence, compared to the control condition. Physical appearance perfectionism and cognitive coping both predicted body image outcomes for the experimental condition. Discussion: Clinical implications, such as the prevention and treatment of body dissatisfaction, are discussed. Strengths and limitations of the current study are also noted, and suggestions for future research are provided.

Keywords: perfectionism, cognitive coping, body image, social media

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Authors: Farshideh Einsele, Hassan Foroosh

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using wellknown geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: camera-based OCR, feature extraction, document, image processing, grocery products

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Authors: Jen-Fa Huang, Yu-Wei Chiu, Jhe-Ren Cheng

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Visible light communication (VLC) technique has become useful method via LED light blinking. Several issues on indoor mobile robot positioning with LED blinking are examined in the paper. In the transmitter, we control the transceivers blinking message. Orthogonal Walsh codes are adopted for such purpose on auto-correlation function (ACF) to detect signal sequences. In the robot receiver, we set the frame of time by 1 ns passing signal from the transceiver to the mobile robot. After going through many periods of time detecting the peak value of ACF in the mobile robot. Moreover, the transceiver transmits signal again immediately. By capturing three times of peak value, we can know the time difference of arrival (TDOA) between two peak value intervals and finally analyze the accuracy of the robot position.

Keywords: Visible Light Communication, Auto-Correlation Function (ACF), peak value of ACF, Time difference of Arrival (TDOA)

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Authors: Mahadzirah Mohamad, Nur Izzati Ab Ghani

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Worldwide, tourism sustained growth and remained to be one of the fast-growing sectors. Malaysia tourism industry experienced an unstable and declining pattern of international tourist arrival’s growth rate. The situation suggested that the industry was competitive and denoted the need to study factors that influence tourist loyalty. The primary purpose of this study was to develop a model that examined how destination image, perceived value, service quality and tourist satisfaction affect destination loyalty. The study was conducted at the Kuala Lumpur International Airport and Kota Kinabalu International Airport. The respondents were international tourists from United Kingdom and Australia and they were selected using simple random sampling method. A total of 337 respondents were subjected to data analysis using structural equation modelling. The study uncovered that perceived value and destination image was highly correlated and the model suggested that these constructs should be treated as one construct. The construct was labelled as overall destination image. Overall image had significant direct effect on service quality, satisfaction and loyalty. Service quality had a significant indirect effect on loyalty through satisfaction as a moderating variable. However, satisfaction had no mediating effect on the relationship between overall destination image and loyalty. The study suggested that more efforts should be focused on portraying the image of experiencing joy with many interesting natural scenic places to see whilst on a holiday to Malaysia. In addition, the destination management office should promote tourist visiting to Malaysia would enjoy quality service related to accommodation, information facilities, health, and shopping. Tourist satisfaction empirically proved to be an important construct that influenced destination loyalty. This study contributed to the extended knowledge that postulated overall image of a destination was measured by perceived value and destination image.

Keywords: destination image, destination loyalty, structural equation modelling, tourist satisfaction

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Samira Eskandari

Abstract:

The image of Tehran has been impacted in recent years due to poor urban management and fragmented governance. There is no cohesive urban beautification framework in Tehran to enforce builders take aesthetic factors seriously when design and construct new buildings. The existing guidelines merely provide people with recommendations, not regulations. Obviously, Tehran needs a more comprehensive and strict urban beautification framework to restore its image. The damaged image has impacted the city’s social, economic and environmental growth. This research aims to find and examine a solution by which the employment of urban beautification regulation would be guaranteed, and city image would be organized. The methodology is based on a qualitative approach associated with analytical methods, in-depth surveys and interviews with Tehran citizens, authorities and experts, and use of academic resources as well as simulation. As a result, one practical solution is to incorporate aesthetic guidelines into a survival-related framework like a seismic guideline. Tehran is a seismic site, and all the buildings in Tehran have to be retrofitted against earthquake during construction. Hence, by integrating seismic regulations and aesthetic disciplines, urban beautification will be somehow guaranteed. Besides, the seismic image can turn into Tehran’s brand and enhances city identity. This research is trying to increase the social, environmental, and economic interconnectedness between urban planning and seismic planning by the usage of landscape architecture methods. As a case study, the potential outcomes are simulated in Tajrish, a suburb located in the north of Tehran. The result is that, by the redefinition of the morphology of seismic retrofitting systems, used in the significant city image elements, and re-function them in accordance with the Iranian culture and traditions, the city image would become more harmonized and legible.

Keywords: earthquake, retrofitting systems, Tehran image, urban beautification

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

Abstract:

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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