Search results for: quantum image encryption

Authors: Faci Youcef

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In the present work, an experimental study is carried out using the digital image correlation (DIC) technique to analyze the damage and behavior of woven composite carbon/epoxy under tensile loading. The tension mechanisms associated with failure modes of bolted joints in advanced composites are studied, as well as displacement distribution and strain distribution. The evolution value of bolt angle inclination during tensile tests was studied. In order to compare the distribution of displacements and strains along the surface, figures of image mapping are made. Several factors that are responsible for the failure of fiber-reinforced polymer composite materials are observed. It was found that strain concentrations observed in the specimens can be used to identify full-field damage onset and to monitor damage progression during loading. Moreover, there is an interaction between laminate pattern, laminate thickness, fastener size and type, surface strain concentrations, and out-of-plane displacement. Conclusions include a failure analysis associated with bolt angle inclinations and supported by microscopic visualizations of the composite specimen. The DIC results can be used to develop and accurately validate numerical models.

Keywords: Carbone, woven, damage, digital image, bolted joint, the inclination of angle

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Authors: J. M. Hernández-Ontiveros, E. E. García-Guerrero, E. Inzunza-González, O. R. López-Bonilla

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This paper presents the implementation of an algorithm that identifies and accounts different fish species: Catfish, Sea bream, Sawfish, Tilapia, and Totoaba. The main contribution of the method is the fusion of the characteristics of invariance to the position, rotation and scale of the Hu moments, with the proper counting of fish. The identification and counting is performed, from an image under different noise conditions. From the experimental results obtained, it is inferred the potentiality of the proposed algorithm to be applied in different scenarios of aquaculture production.

Keywords: counting fish, digital image processing, invariant moments, pattern recognition

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Authors: Artion Kashuri, Rozana Liko

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In this paper, the authors establish an integral identity using delta differentiable functions. By applying this identity, some new results via a general class of convex functions with respect to two nonnegative functions on a time scale are given. Also, for suitable choices of nonnegative functions, some special cases are deduced. Finally, in order to illustrate the efficiency of our main results, some applications to special means are obtained as well. We hope that current work using our idea and technique will attract the attention of researchers working in mathematical analysis, mathematical inequalities, numerical analysis, special functions, fractional calculus, quantum mechanics, quantum calculus, physics, probability and statistics, differential and difference equations, optimization theory, and other related fields in pure and applied sciences.

Keywords: convex functions, Hermite-Hadamard inequality, special means, time scale

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Authors: M. Shaker, S. Minaei, M. H. Khoshtaghaza, A. Banakar, A. Jafari

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In this study a system of machine vision and singulation was developed to separate paddy from rice and determine paddy husking and rice breakage percentages. The machine vision system consists of three main components including an imaging chamber, a digital camera, a computer equipped with image processing software. The singulation device consists of a kernel holding surface, a motor with vacuum fan, and a dimmer. For separation of paddy from rice (in the image), it was necessary to set a threshold. Therefore, some images of paddy and rice were sampled and the RGB values of the images were extracted using MATLAB software. Then mean and standard deviation of the data were determined. An Image processing algorithm was developed using MATLAB to determine paddy/rice separation and rice breakage and paddy husking percentages, using blue to red ratio. Tests showed that, a threshold of 0.75 is suitable for separating paddy from rice kernels. Results from the evaluation of the image processing algorithm showed that the accuracies obtained with the algorithm were 98.36% and 91.81% for paddy husking and rice breakage percentage, respectively. Analysis also showed that a suction of 45 mmHg to 50 mmHg yielding 81.3% separation efficiency is appropriate for operation of the kernel singulation system.

Keywords: breakage, computer vision, husking, rice kernel

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Authors: S. Jagadeesh Kumar

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Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation.

Keywords: curvature analysis, ring correction in polar coordinate method, face detection, face recognition, human computer interaction

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Authors: Micheal Olaolu Arowolo, Muhammad Azam, Fei He, Mihail Popescu, Dong Xu

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As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy.

Keywords: biological pathway, gene identification, object detection, Siamese network

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: K. V. Nabichu

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The rationale of this research was to review how Germans perceive Uganda as a tourism destination, after German visitors arrivals to Uganda remain few compared to other destinations like Kenya. It was assumed that Uganda suffers a negative image in Germany due to negative media influence. The study findings indicate that Uganda is not a popular travel destination in Germany, there is generally lack of travel information about Uganda. Despite the respondents’ hearing about Uganda’s and her beautiful attractions, good climate and friendly people, they also think Uganda is unsafe for travel. Findings further show that Uganda is a potential travel destination for Germans due to her beautifull landscape, rich culture, wild life, primates and the Nile, however political unrest, insecurity, the fear for diseases and poor hygiene hinder Germans from travelling to Uganda. The media, internet as well as friends and relatives were the major primary sources of information on Uganda while others knew about Uganda through their school lessons and sports. Uganda is not well advertised and promoted in Germany.

Keywords: destination Uganda and Germany, image, perception, negative media influence

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Authors: Guang-Ho Cha

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This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.

Keywords: context-sensitive search, image search, similarity ranking, similarity search

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Authors: Helene Martin, Solmaz Boroomandi Barati, Jean-Charles Pinoli, Stephane Valette, Yann Gavet

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In the present work we developed an image processing algorithm to measure water droplets characteristics during dropwise condensation on pillared surfaces. The main problem in this process is the similarity between shape and size of water droplets and the pillars. The developed method divides droplets into four main groups based on their size and applies the corresponding algorithm to segment each group. These algorithms generate binary images of droplets based on both their geometrical and intensity properties. The information related to droplets evolution during time including mean radius and drops number per unit area are then extracted from the binary images. The developed image processing algorithm is verified using manual detection and applied to two different sets of images corresponding to two kinds of pillared surfaces.

Keywords: dropwise condensation, textured surface, image processing, watershed

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Authors: Chang Hung Chen

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Typically the Cause-Related Marketing (CRM) is effective for promoting products, and is also accepted as a role of communication tool for creating a positive image of the corporate. Today, companies are taking Corporate Social Responsibility (CSR) as core activities to build a goal of sustainable development. CRM is not a synonym of CSR. Actually, CRM is a part of CSR, or a type of marketing strategy in CSR framework. This article focuses on the relationship between CSR and CRM, and how the CRM improves the CSR performance of the corporate. The research was conducted through review of literature on the subject area.

Keywords: cause-related marketing, corporate social responsibility, corporate image, consumer behavior

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Authors: S. K. Khosrowshahi, E. Güler

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This study investigates the reinforcement function of geosynthetics on the shear strength and strain profile of sand. Conducting a series of simple shear tests, the shearing behavior of the samples under static and cyclic loads was evaluated. Three different types of geosynthetics including geotextile and geonets were used as the reinforcement materials. An image processing analysis based on the optical flow method was performed to measure the lateral displacements and estimate the shear strains. It is shown that besides improving the shear strength, the geosynthetic reinforcement leads a remarkable reduction on the shear strains. The improved layer reduces the required thickness of the soil layer to resist against shear stresses. Consequently, the geosynthetic reinforcement can be considered as a proper approach for the sustainable designs, especially in the projects with huge amount of geotechnical applications like subgrade of the pavements, roadways, and railways.

Keywords: image processing, soil reinforcement, geosynthetics, simple shear test, shear strain profile

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Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: A. Abdullah, S. Abushalmat, A. Bakshwain, A. Basuhail, A. Aslam

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GIS (Geographic Information System) applications require geo-referenced data, this data could be available as databases or in the form of digital or hard-copy agro-meteorological maps. These parameter maps are color-coded with different regions corresponding to different parameter values, converting these maps into a database is not very difficult. However, text and different planimetric elements overlaid on these maps makes an accurate image to database conversion a challenging problem. The reason being, it is almost impossible to exactly replace what was underneath the text or icons; thus, pointing to the need for inpainting. In this paper, we propose a probabilistic inpainting approach that uses the probability of spatial locality of colors in the map for replacing overlaid elements with underlying color. We tested the limits of our proposed technique using non-textual simulated data and compared text removing results with a popular image editing tool using public domain data with promising results.

Keywords: noise, image, GIS, digital map, inpainting

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Authors: Amhimmid Mohammed Saffour, Abdelkader Salama

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Filtering is a part of image enhancement techniques, it is used to enhance certain details such as edges in the image that are relevant to the application. Additionally, filtering can even be used to eliminate unwanted components of noise. Medical images typically contain salt and pepper noise and Poisson noise. This noise appears to the presence of minute grey scale variations within the image. In this paper, different filters techniques namely (Median, Wiener, Rank order3, Rank order5, and Average) were applied on CT medical images (Brain and chest). We using all these filters to remove salt and pepper noise from these images. This type of noise consists of random pixels being set to black or white. Peak Signal to Noise Ratio (PSNR), Mean Square Error r(MSE) and Histogram were used to evaluated the quality of filtered images. The results, which we have achieved shows that, these filters, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patients with no difficulty.

Keywords: CT imaging, median filter, adaptive filter and average filter, MATLAB

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Rabindranath Bag, Surjeet Singh

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Materials in which the electrons are strongly correlated provide some of the most challenging and exciting problems in condensed matter physics today. After the discovery of high critical temperature superconductivity in layered or two-dimensional copper oxides, many physicists got attention in cuprates and it led to an upsurge of interest in the synthesis and physical properties of copper-oxide based material. The quest to understand superconducting mechanism in high-temperature cuprates, drew physicist’s attention to somewhat simpler compounds consisting of spin-chains or one-dimensional lattice of coupled spins. Low-dimensional quantum magnets are of huge contemporary interest in basic sciences as well emerging technologies such as quantum computing and quantum information theory, and heat management in microelectronic devices. Spin ladder is an example of quasi one-dimensional quantum magnets which provides a bridge between one and two dimensional materials. One of the examples of quasi one-dimensional spin-ladder compounds is Sr14Cu24O41, which exhibits a lot of interesting and exciting physical phenomena in low dimensional systems. Very recently, the ladder compound Sr14Cu24O41 was shown to exhibit long-distance quantum entanglement crucial to quantum information theory. Also, it is well known that hole-compensation in this material results in very high (metal-like) anisotropic thermal conductivity at room temperature. These observations suggest that Sr14Cu24O41 is a potential multifunctional material which invites further detailed investigations. To investigate these properties one must needs a large and high quality of single crystal. But these systems are showing incongruently melting behavior, which brings many difficulties to grow a large and quality of single crystals. Hence, we are using TSFZ (Travelling Solvent Floating Zone) method to grow the high quality of single crystals of the low dimensional magnets. Apart from this, it has unique crystal structure (alternating stacks of plane containing edge-sharing CuO2 chains, and the plane containing two-leg Cu2O3 ladder with intermediate Sr layers along the b- axis), which is also incommensurate in nature. It exhibits abundant physical phenomenon such as spin dimerization, crystallization of charge holes and charge density wave. The maximum focus of research so far involved in introducing defects on A-site (Sr). However, apart from the A-site (Sr) doping, there are only few studies in which the B-site (Cu) doping of polycrystalline Sr14Cu24O41 have been discussed and the reason behind this is the possibility of two doping sites for Cu (CuO2 chain and Cu2O3 ladder). Therefore, in our present work, the crystals (pristine and Cu-site doped) were grown by using TSFZ method by tuning the growth parameters. The Laue diffraction images, optical polarized microscopy and Scanning Electron Microscopy (SEM) images confirm the quality of the grown crystals. Here, we report the single crystal growth, magnetic and transport properties of Sr14Cu24O41 and its lightly doped variants (magnetic and non-magnetic) containing less than 1% of Co, Ni, Al and Zn impurities. Since, any real system will have some amount of weak disorder, our studies on these ladder compounds with controlled dilute disorder would be significant in the present context.

Keywords: low-dimensional quantum magnets, single crystal, spin-ladder, TSFZ technique

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Authors: Khaled Bahgat

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In the present work, the characterization of 4-Amino-3-phenyl-1H-1,2,4-triazole-5(4H)-thione (APTT) molecule was carried out by quantum chemical method and vibrational spectral techniques. The FT-IR (4000–400 cm_1) and FT-Raman (4000–100 cm_1) spectra of APTT were recorded in solid phase. The UV–Vis absorption spectrum of the APTT was recorded in the range of 200–400 nm. The molecular geometry, harmonic vibrational frequencies and bonding features of APTT in the ground state have been calculated by HF and DFT methods using 6-311++G(d,p) basis set. The complete vibrational frequency assignments were made by normal coordinate analysis (NCA) following the scaled quantum mechanical force field methodology (SQMF). The molecular stability and bond strength were investigated by applying the natural bond orbital analysis (NBO) and natural localized molecular orbital (NLMO) analysis. The electronic properties, such as excitation energies, absorption wavelength, HOMO and LUMO energies were performed by time depended DFT (TD-DFT) approach. The 1H and 13C nuclear magnetic resonance chemical shift of the molecule were calculated using the gauge-including atomic orbital (GIAO) method and compared with experimental results. Finally, the calculation results were analyzed to simulate infrared, FT-Raman and UV spectra of the title compound which shows better agreement with observed spectra.

Keywords: 4-amino-3-phenyl-1H-1, 2, 4-triazole-5(4H)-thione, vibrational assignments, normal coordinate analysis, quantum mechanical calculations

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Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: M. Gomez, A. Molina

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The concept of brand equity in the wine tourism area is an interesting topic to explore the factors that determine it. The aim of this study is to address this gap by investigating wine tourism destinations brand equity, and understanding the impact that the denomination of origin (DO) brand image and the destination image have on brand equity. Managing and monitoring the branding of wine tourism destinations is crucial to attract tourist arrivals. The multiplicity of stakeholders involved in the branding process calls for research that, unlike previous studies, adopts a broader perspective and incorporates an internal and an external perspective. Therefore, this gap by comparing managers’ and visitors’ approaches to wine tourism destination brand equity has been addressed. A survey questionnaire for data collection purposes was used. The hypotheses were tested using winery managers and winery visitors, each leading a different position relative to the wine tourism destination brand equity. All the interviews were conducted face-to-face. The survey instrument included several scales related to DO brand image, destination image, and wine tourism destination brand equity. All items were measured on seven-point Likert scales. Partial least squares was used to analyze the accuracy of scales, the structural model, and multi-group analysis to identify the differences in the path coefficients and to test the hypotheses. The results show that the positive influence of DO brand image on wine tourism destination brand equity is stronger for wineries than for visitors, but there are no significant differences between the two groups. However, there are significant differences in the positive effect of destination brand image on both wine tourism destination brand equity and DO brand image. The results of this study are important for consultants, practitioners, and policy makers. The gap between managers and visitors calls for the development of a number of campaigns to enhance the image that visitors hold and, thus, increase tourist arrivals. Events such as wine gatherings and gastronomic symposiums held at universities and culinary schools and participation in business meetings can enhance the perceptions and in turn, the added value, brand equity of the wine tourism destinations. The images of destinations and DOs can help strengthen the brand equity of the wine tourism destinations, especially for visitors. Thus, the development and reinforcement of favorable, strong, and unique destination associations and DO associations are important to increase that value. Joint campaigns are advisable to enhance the images of destinations and DOs and, as a consequence, the value of the wine tourism destination brand.

Keywords: brand equity, managers, visitors, wine tourism

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Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

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Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified.

Keywords: halogenated phenols, toxicity mechanism, hydrophobicity, electrophilicity index, quantitative stucture-toxicity relationships

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Authors: Vahid Anari, Leila Shahmohammadi

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Diagnosing and interpreting manually from a large cohort dataset of immunohistochemically stained tissue of tumors using an optical microscope involves subjectivity and also is tedious for pathologist specialists. Moreover, digital pathology today represents more of an evolution than a revolution in pathology. In this paper, we develop and test an unsupervised algorithm that can automatically enhance the IHC image of a meningioma tumor and classify cells into positive (proliferative) and negative (normal) cells. A dataset including 150 images is used to test the scheme. In addition, a new adaptive color image enhancement method is proposed based on a vector directional filter (VDF) and statistical properties of filtering the window. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: digital pathology, cell segmentation, immunohistochemically, noise reduction

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Authors: Abdellatif Baba

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In order to facilitate medical images analysis by improving their quality and readability, we present in this paper a new dynamic band pass filter as a general and suitable operator for different types of medical images. Our objective is to enrich the details of any treated medical image to make it sufficiently clear enough to give an understood and simplified meaning even for unspecialized people in the medical domain.

Keywords: medical image enhancement, dynamic band pass filter, analysis improvement

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Authors: Abdullah Bal, Sevdenur Bal

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This paper purposes a new type of hardware application for training of cellular neural networks (CNN) using optical joint transform correlation (JTC) architecture for image feature extraction. CNNs require much more computation during the training stage compare to test process. Since optoelectronic hardware applications offer possibility of parallel high speed processing capability for 2D data processing applications, CNN training algorithm can be realized using Fourier optics technique. JTC employs lens and CCD cameras with laser beam that realize 2D matrix multiplication and summation in the light speed. Therefore, in the each iteration of training, JTC carries more computation burden inherently and the rest of mathematical computation realized digitally. The bipolar data is encoded by phase and summation of correlation operations is realized using multi-object input joint images. Overlapping properties of JTC are then utilized for summation of two cross-correlations which provide less computation possibility for training stage. Phase-only JTC does not require data rearrangement, electronic pre-calculation and strict system alignment. The proposed system can be incorporated simultaneously with various optical image processing or optical pattern recognition techniques just in the same optical system.

Keywords: CNN training, image processing, joint transform correlation, optoelectronic hardware

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Authors: Chih-Yi Ho, Tzu-Fang Chang, Chih-Chia Huang, Chia-Yen Lee

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In dental clinics, the dentists use the periapical radiography image to assess the effectiveness of endodontic treatment of teeth with chronic apical periodontitis. Periapical radiography images are taken at different times to assess alveolar bone variation before and after the root canal treatment, and furthermore to judge whether the treatment was successful. Current clinical assessment of apical tissue recovery relies only on dentist personal experience. It is difficult to have the same standard and objective interpretations due to the dentist or radiologist personal background and knowledge. If periapical radiography images at the different time could be registered well, the endodontic treatment could be evaluated. In the image registration area, it is necessary to assign representative control points to the transformation model for good performances of registration results. However, detection of representative control points (feature points) on periapical radiography images is generally very difficult. Regardless of which traditional detection methods are practiced, sufficient feature points may not be detected due to the low-contrast characteristics of the x-ray image. Barnard detector is an algorithm for feature point detection based on grayscale value gradients, which can obtain sufficient feature points in the case of gray-scale contrast is not obvious. However, the Barnard detector would detect too many feature points, and they would be too clustered. This study uses the local extrema of clustering feature points and the suppression radius to overcome the problem, and compared different feature point detection methods. In the preliminary result, the feature points could be detected as representative control points by the proposed method.

Keywords: feature detection, Barnard detector, registration, periapical radiography image, endodontic treatment

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Authors: Masoud Nasiri Sarvi, Yunhua Luo

Abstract:

Low-trauma hip fracture, usually caused by fall from standing height, has become a main source of morbidity and mortality for the elderly. Factors affecting hip fracture include sex, race, age, body weight, height, body mass distribution, etc., and thus, hip fracture risk in fall differs widely from subject to subject. It is therefore necessary to develop a subject-specific biomechanical model to predict hip fracture risk. The objective of this study is to develop a two-level, image-based, subject-specific biomechanical model consisting of a whole-body dynamics model and a proximal-femur finite element (FE) model for more accurately assessing the risk of hip fracture in lateral falls. Required information for constructing the model is extracted from a whole-body and a hip DXA (Dual Energy X-ray Absorptiometry) image of the subject. The proposed model considers all parameters subject-specifically, which will provide a fast, accurate, and non-expensive method for predicting hip fracture risk.

Keywords: bone mineral density, hip fracture risk, impact force, sideways falls

Procedia PDF Downloads 534

Authors: Te-Jen Chang, I-Hui Pan, Kuang-Hsiung Tan, Shan-Jen Cheng, Chien-Wu Lan, Chih-Chan Hu

Abstract:

With the event of multimedia age in order to protect data not to be tampered, damaged, and faked, information hiding technologies are proposed. Information hiding means important secret information is hidden into cover multimedia and then camouflaged media is produced. This camouflaged media has the characteristic of natural protection. Under the undoubted situation, important secret information is transmitted out.Reversible information hiding technologies for high capacity is proposed in this paper. The gray images are as cover media in this technology. We compress gray images and compare with the original image to produce the estimated differences. By using the estimated differences, expression information hiding is used, and higher information capacity can be achieved. According to experimental results, the proposed technology can be approved. For these experiments, the whole capacity of information payload and image quality can be satisfied.

Keywords: cover media, camouflaged media, reversible information hiding, gray image

Procedia PDF Downloads 326

Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

Abstract:

In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: image compression, color image, q-coder, quantization, edge-detection

Procedia PDF Downloads 327