Search results for: image multiplication

Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

Procedia PDF Downloads 187

Authors: T. Punjansing, M. Nakkuntod, S. Homchan, P. Inthima, A. Kongbangkerd

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The influence of organic supplements on growth and multiplication efficiency of Phaius tankervilleae var. alba seedlings was investigated. 12 week-old seedlings were cultured on half-strength semi-solid Murashige and Skoog (MS) medium supplemented with 30 g/L sucrose, 8 g/L agar and various concentrations of coconut water (0, 50, 100, 150 and 200 mL/L) combined with potato extract (0, 25 and 50 g/L) and the pH was adjusted to 5.8 prior to autoclaving. The cultures were then kept under constant photoperiod (16 h light: 8 h dark) at 25 ± 2 °C for 12 weeks. The highest number of shoots (3.0 shoots/explant) was obtained when cultured on the medium added with 50 ml/L coconut water and 50 g/L potato extract whereas the highest number of leaves (5.9 leaves/explant) and roots (6.1 roots/explant) could receive on the medium supplemented with 150 ml/L coconut water and 50 g/L potato extract. with 150 ml/L coconut water and 50 g/L potato extract. Additionally, plantlets of P. tankervilleae var. alba were transferred to grow into seven different substrates i.e. soil, sand, coconut husk chip, soil-sand mix (1: 1), soil-coconut husk chip mix (1: 1), sand-coconut husk chip mix (1: 1) and soil-sand-coconut husk chip mix (1: 1: 1) for four weeks. The results found that acclimatized plants showed 100% of survivals when sand, coconut husk chip and sand-coconut husk chip mix are used as substrates. The number of leaves induced by sand-coconut husk chip mix was significantly higher than that planted in other substrates (P > 0.05). Meanwhile, no significant difference in new shoot formation among these substrates was observed (P < 0.05). This precursory developing protocol was likely to be applied for more large scale of plant production as well as conservation of germplasm of this orchid species.

Keywords: organic supplements, acclimatization, Phaius tankervilleae var. alba, orchid

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Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

Procedia PDF Downloads 168

Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith

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Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.

Keywords: cycle consistency, deformable multimodal image registration, deep learning, GAN

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Authors: Rashmi Ranade, Neelu Joshi

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Barleria prionitis L. is a well explored Indian medicinal plant valued for its stem and leaf which forms an important ingredient of many Ayurvedic formulations. It is used for the treatment of various disorders like toothache, bleeding gums, strengthening gums, whooping cough, inflammation, arthritis, enlargement of scrotum and sciatica etc. The plant is propagated vegetatively through stem cuttings. Frequent harvesting of this plant has led to the shortage of planting material, and it has acquired the status of vulnerable plant species. Plant tissue culture technology offers a very good alternative for propagation and conservation of such plant species. The present investigation was undertaken to develop in vitro regeneration protocol for B. prionitis L. via callus organogenesis pathway. Stem and leaf explants were used for this purpose. Different media and plant growth regulators were optimized to develop the protocol. The problem of phenol secretion and browning and in vitro cultures at the establishment phase was successfully curbed with the usage of antibrowning agents such as ascorbic acid and activated charcoal. Optimum shoot multiplication was achieved by the use of liquid media and incorporation of silver nitrate and TIBA (triiodobenzoic acid) into the media. High percent rooting (76%) was observed on WPM media supplemented with IBA (2.0 mg/l), IAA (0.5 mg/l), GA3(0.5) and activated charcoal(500 mg/l). The rooted plantlets were subjected to in vitro hardening on sterile potting mix (soil:farmyard manure:compost; 1:2:1) and acclimatized under greenhouse conditions. Around 85% survival of plantlets was recorded upon acclimatization. This lab scale protocol would be tested for in vitro scaling up production of B. prionitis L.

Keywords: explant browning, liquid culture, micropropagation, shoot multiplication, phenolic secretion

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Authors: Mohamed A. Almoghalis, Osman M. Hegazy, Ibrahim F. Imam, Ali H. Elbastawessy

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This paper presents a new hyperspectral image segmentation scheme with respect to both spatial and spectral contexts. The scheme uses the 8-pixels spatial pattern to build a weight structure that holds the number of outlier bands for each pixel among its neighborhood windows in different directions. The number of outlier bands for a pixel is obtained using bands quartile box plots profile among spatial 8-pixels pattern windows. The quartile box plot weight structure represents the spatial-spectral context in the image. Instead of starting segmentation process by single pixels, the proposed methodology starts by pixels groups that proved to share the same spectral features with respect to their spatial context. As a result, the segmentation scheme starts with Jigsaw pieces that build a mosaic image. The following step builds a model for each Jigsaw piece in the mosaic image. Each Jigsaw piece will be merged with another Jigsaw piece using KNN applied to their bands' quartile box plots profiles. The scheme iterates till required number of segments reached. Experiments use two data sets obtained from Earth Observer 1 (EO-1) sensor for Egypt and France. Initial results qualitative analysis showed encouraging results compared with ground truth. Quantitative analysis for the results will be included in the final paper.

Keywords: hyperspectral image segmentation, image processing, remote sensing, box plot

Procedia PDF Downloads 589

Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

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The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: Zihan Yu, Kohei Inoue, Kiichi Urahama

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We present a method for de-hazing images. A dark envelope image is derived with the bilateral minimum filter and a bright envelope is derived with the bilateral maximum filter. The ambient light and transmission of the scene are estimated from these two envelope images. An image without haze is reconstructed from the estimated ambient light and transmission.

Keywords: image dehazing, bilateral minimum filter, bilateral maximum filter, local contrast

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Authors: Kevin Wongleedee

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

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

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

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Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: features extraction, image segmentation, medical images, tumor detection

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: Kuo-Cheng Liu

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In the perceptual image coding, the objective is to shape the coding distortion such that the amplitude of distortion does not exceed the error visibility threshold, or to remove perceptually redundant signals from the image. While most researches focus on color image coding, the perceptual-based quantizer developed for luminance signals are always directly applied to chrominance signals such that the color image compression methods are inefficient. In this paper, the internal generative mechanism is integrated into the design of a color image compression method. The internal generative mechanism working model based on the structure-based spatial masking is used to assess the subjective distortion visibility thresholds that are visually consistent to human eyes better. The estimation method of structure-based distortion visibility thresholds for color components is further presented in a locally adaptive way to design quantization process in the wavelet color image compression scheme. Since the lowest subband coefficient matrix of images in the wavelet domain preserves the local property of images in the spatial domain, the error visibility threshold inherent in each coefficient of the lowest subband for each color component is estimated by using the proposed spatial error visibility threshold assessment. The threshold inherent in each coefficient of other subbands for each color component is then estimated in a local adaptive fashion based on the distortion energy allocation. By considering that the error visibility thresholds are estimated using predicting and reconstructed signals of the color image, the coding scheme incorporated with locally adaptive perceptual color quantizer does not require side information. Experimental results show that the entropies of three color components obtained by using proposed IGM-based color image compression scheme are lower than that obtained by using the existing color image compression method at perceptually lossless visual quality.

Keywords: internal generative mechanism, structure-based spatial masking, visibility threshold, wavelet domain

Procedia PDF Downloads 239

Authors: Aika Umemuro, Mitsuru Sato, Mizuki Narita, Saya Hori, Saya Sakurai, Tomomi Nakayama, Ayano Nakazawa, Toshihiro Ogura

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Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful.

Keywords: EEG scanner, eye-detector, mammography, observers

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Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Shayan Rezvanigilkolaei, Shayesteh Vefaghnematollahi

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Electrical impedance tomography is a non-invasive medical imaging technique suitable for medical applications. This paper describes an electrical impedance tomography device with the ability to navigate a robotic arm to manipulate a target object. The design of the device includes various hardware and software sections to perform medical imaging and control the robotic arm. In its hardware section an image is formed by 16 electrodes which are located around a container. This image is used to navigate a 3DOF robotic arm to reach the exact location of the target object. The data set to form the impedance imaging is obtained by having repeated current injections and voltage measurements between all electrode pairs. After performing the necessary calculations to obtain the impedance, information is transmitted to the computer. This data is fed and then executed in MATLAB which is interfaced with EIDORS (Electrical Impedance Tomography Reconstruction Software) to reconstruct the image based on the acquired data. In the next step, the coordinates of the center of the target object are calculated by image processing toolbox of MATLAB (IPT). Finally, these coordinates are used to calculate the angles of each joint of the robotic arm. The robotic arm moves to the desired tissue with the user command.

Keywords: electrical impedance tomography, EIT, surgeon robot, image processing of electrical impedance tomography

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Authors: Hajji Lobna, Chattaoui Mayssa, Regaieg Hajer, M'Hamdi-Boughalleb Naima, Rhouma Ali, Horrigue-Raouani Najet

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In this study, three local isolates of Trichoderma (Tr1: T. viride, Tr2: T. harzianum and Tr3: T. asperellum) were isolated and evaluated for their biocontrol effectiveness under in vitro conditions and in greenhouse. In vitro bioassay revealed a biopotential control against Fusarium oxysporum f. sp. radicis lycopersici and Meloidogyne javanica (RKN) separately. All species of Trichoderma exhibited biocontrol performance and (Tr1) Trichoderma viride was the most efficient. In fact, growth rate inhibition of Fusarium oxysporum f. sp. radicis lycopersici (FORL) was reached 75.5% with Tr1. Parasitism rate of root-knot nematode was 60% for juveniles and 75% for eggs with the same one. Pots experiment results showed that Tr1 and Tr2, compared to chemical treatment, enhanced the plant growth and exhibited better antagonism against root-knot nematode and root-rot fungi separated or combined. All Trichoderma isolates revealed a bioprotection potential against Fusarium oxysporum f. sp. radicis lycopersici. When pathogen fungi inoculated alone, Fusarium wilt index and browning vascular rate were reduced significantly with Tr1 (0.91, 2.38%) and Tr2 (1.5, 5.5%), respectively. In the case of combined infection with Fusarium and nematode, the same isolate of Trichoderma Tr1 and Tr2 decreased Fusarium wilt index at 1.1 and 0.83 and reduced the browning vascular rate at 6.5% and 6%, respectively. Similarly, the isolate Tr1 and Tr2 caused maximum inhibition of nematode multiplication. Multiplication rate was declined at 4% with both isolates either tomato infected by nematode separately or concomitantly with Fusarium. The chemical treatment was moderate in activity against Meloidogyne javanica and Fusarium oxysporum f. sp. radicis lycopersici alone and combined.

Keywords: trichoderma spp., meloidogyne javanica, Fusarium oxysporum f.sp.radicis lycopersici, biocontrol

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Authors: Toshanlal Meenpal, Ankita Meenpal

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A very important technique in reversible data hiding field is Difference expansion. Secret message as well as the cover image may be completely recovered without any distortion after data extraction process due to reversibility feature. In general, in any difference expansion scheme embedding is performed by integer transform in the difference image acquired by grouping two neighboring pixel values. This paper proposes an improved reversible difference expansion embedding scheme. We mainly consider edge direction for embedding by modifying the difference of two neighboring pixels values. In general, the larger difference tends to bring a degraded stego image quality than the smaller difference. Image quality in the range of 0.5 to 3.7 dB in average is achieved by the proposed scheme, which is shown through the experimental results. However payload wise it achieves almost similar capacity in comparisons with previous method.

Keywords: information hiding, wedge direction, difference expansion, integer transform

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Authors: Samrah Saeed

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Introduction: This study considers a measure of positive body image and the associations between body appreciation, beauty ideals internalization, dietary habits, and physical activity in young adults. Positive body image is assessed by Body Appreciation Scale 2. It is used to assess a person's acceptance of the body, the degree of positivity, and respect for the body.Regular physical activity and healthy eating arebasically important for the body, and they play an important role in creating a positive image of the body. Objectives: To identify the associations between body appreciation and beauty ideals internalization. To compare body appreciation and body ideals internalization among students of different physical activity. To explore the associations between dietary habits (unhealthy, healthy), body appreciation and body ideals internalization. Research methods and organization: Study participants were young adult students, aged 18-35, both male and female.The research questionnaire consisted of four areas: body appreciation, beauty ideals internalization, dietary habits, and physical activity.The questionnaire was created in Google Forms online survey platform.The questionnaire was filled out anonymously Result and Discussion: Physical dissatisfaction, diet, eating disorders and exercise disorders are found in young adults all over the world.Thorough nutrition helps people understand who they are by reassuring them that they are okay without judging or accepting themselves. Social media can positively influence body image in many ways.A healthy body image is important because it affect self-esteem, self-acceptance, and your attitude towards food and exercise.

Keywords: pysical activity, dietary habits, body image, beauty ideals internalization, body appreciation

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Authors: Mehtab Alam

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Sustainable tourism practices require to improve the decreased performance levels in phases of change management for image branding. This paper addresses the innovative approach of using sustainable tourism for image branding as a contemporary tool of foreign policy. The sustainable tourism-based foreign policy promotes cultural values, green tourism, economy, and image management for the avoidance of rising global conflict. The mixed-method approach (quantitative 382 surveys, qualitative 11 interviews at saturation point) implied for the data analysis. The research finding provides the potential of using sustainable tourism by implying skills and knowledge, capacity, and personal factors of change management in improving tourism-based performance levels. It includes the valuable tourism performance role for the success of a foreign policy through sustainable tourism. Change management in tourism-based foreign policy provides the destination readiness for international engagement and curbing of climate issues through green tourism. The research recommends the impact of change management in improving the tourism-based performance levels of image branding for a coercive foreign policy. The paper’s future direction for the immediate implementation of tourism-based foreign policy is to overcome the contemporary issues of travel marketing management, green infrastructure, and cross-border regulation.

Keywords: decrease performance levels, change management, sustainable tourism, image branding, foreign policy

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Zhipeng Dong, Jing Guo

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Graphene-semiconductor contacts have been extensively studied recently, both as a stand-alone diode device for potential applications in photodetectors and solar cells, and as a building block to vertical transistors. Graphene is a two-dimensional nanomaterial with vanishing density-of-states at the Dirac point, which differs from conventional metal. In this work, image-charge-induced barrier lowering (BL) in graphene-semiconductor contacts is studied and compared to that in metal Schottky contacts. The results show that despite of being a semimetal with vanishing density-of-states at the Dirac point, the image-charge-induced BL is significant. The BL value can be over 50% of that of metal contacts even in an intrinsic graphene contacted to an organic semiconductor, and it increases as the graphene doping increases. The dependences of the BL on the electric field and semiconductor dielectric constant are examined, and an empirical expression for estimating the image-charge-induced BL in graphene-semiconductor contacts is provided.

Keywords: graphene, semiconductor materials, schottky barrier, image charge, contacts

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Authors: Paramjit Kaur, Harish Kundra

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In the recent years, swarm intelligence based firefly algorithm has become a great focus for the researchers to solve the real time optimization problems. Here, firefly algorithm is used for the application of satellite image classification. For experimentation, Alwar area is considered to multiple land features like vegetation, barren, hilly, residential and water surface. Alwar dataset is considered with seven band satellite images. Firefly Algorithm is based on the attraction of less bright fireflies towards more brightener one. For the evaluation of proposed concept accuracy assessment parameters are calculated using error matrix. With the help of Error matrix, parameters of Kappa Coefficient, Overall Accuracy and feature wise accuracy parameters of user’s accuracy & producer’s accuracy can be calculated. Overall results are compared with BBO, PSO, Hybrid FPAB/BBO, Hybrid ACO/SOFM and Hybrid ACO/BBO based on the kappa coefficient and overall accuracy parameters.

Keywords: image classification, firefly algorithm, satellite image classification, terrain classification

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Authors: Mehtab Alam, Mudiarasan Kuppusamy, Puvaneswaran Kunaserkaran

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The purpose of this paper is to bridge the gap and complete the relationship between tourism destinations and image branding as a choice of edifice foreign policy. Such options became a crucial component for individuals interested in leisure and travel activities. The destination management factors have been evaluated and analyzed using the primary and secondary data in a mixed-methods approach (quantitative sample of 384 and qualitative 8 semi-structured interviews at saturated point). The study chose the Environmental Management Accounting (EMA) and Image Restoration (IR) theories, along with a schematic diagram and an analytical framework supported by NVivo software 12, for two locations in Abbottabad, KPK, Pakistan: Shimla Hill and Thandiani. This incorporates the use of PLS-SEM model for assessing validity of data while SPSS for data screening of descriptive statistics. The results show that destination management's promotion of tourism has significantly improved Pakistan's state image. The use of institutional setup, environmental drivers, immigration, security, and hospitality as well as recreational initiatives on destination management is encouraged. The practical ramifications direct the heads of tourism projects, diplomats, directors, and policymakers to complete destination projects before inviting people to Pakistan. The paper provides the extent of knowledge for academic tourism circles to use tourism destinations as brand ambassadors.

Keywords: tourism, management, state image, foreign policy, image branding

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Authors: Hui Wei, Zheng Dong

Abstract:

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

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

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Authors: Nidhi Jindal, Ashok Chaudhury, Manisha Mangal

Abstract:

Andrographis paniculata, (Burm f.) Wallich ex. Nees (Family Acanthaceae) popularly known as King of Bitters, is an important medicinal herb. It has an astonishingly wide range of medicinal properties such as anti-inflammatory,antidiarrhoeal, antiviral, antimalarial, hepatoprotective, cardiovascular, anticancer, and immunostimulatory activities. It is widely cultivated in southern Asia. Though propagation of this herb generally occurs through seeds, it has many germination problems which intrigued scientists to work out on the alternative techniques for its mass production. The potential of tissue culture techniques as an alternative tool for AP multiplication was found to be promising. However, the high mortality rate of explants caused by phenolic browning of explants is one of the difficulties reported. Low multiplication rates were reported in the proliferation phase, as well as cultures decline characterized by leaf fall and loss of overall vigor. In view of above problems, a study was undertaken to overcome seed dormancy to improve germination potential and to investigate further on the possible means for successful proliferation of cultures via preventive approaches to overcome failures caused by phenolic browning. Experiments were conducted to improve germination potential and among all the chemical and mechanical trials, scarification of seeds with sand paper proved to be the best method to enhance the germination potential (82.44%) within 7 days. Similarly, several pretreatments and media combinations were tried to overcome browning of explants leading to the conclusion that addition of 0.1% citric acid and 0.2% of ascorbic acid in the media followed by rapid sub culturing of explants controlled browning and decline of explants by 67.45%.

Keywords: plant tissue culture, empirical measure, germination, tissue culture

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Authors: Javaria Farooqui

Abstract:

This study, “Transmogrification of the Danse Macabre Image: Capturing the Journey towards Creativity,” traces the evolution of the concept of Danse Macabre. In Every man death takes away the sinful when they least expect it, in Solyman and Perseda everyone falls prey to death irrespective of their deeds and in Tauba-tun-Nasuh, the sinner is plagued. The climatic point in this brief research comes with the Modern texts, The Moon and Sixpence, Roohe-e-Insani and Amédéé, ou Comment s’en débarrasser, when Danse Macabre extends its boundaries, uniting the idea of creativity with death. Similarly in the visual context, Danse Macabre image, initially a horrifying idea, becomes a part of the present day comics and serves an entertaining rather than a cathartic purpose.

Keywords: Danse macabre, transmogrification, Medieval, death, character

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Authors: Taekyong Lee, Joon-Young Kim, Jae-Min Cha

Abstract:

Piping and instrumentation diagram (P&ID) is an essential design drawing describing the interconnection of process equipment and the instrumentation installed to control the process. P&IDs are modified and managed throughout a whole life cycle of a process plant. For the ease of data transfer, P&IDs are generally handed over from a design company to an engineering company as portable document format (PDF) which is hard to be modified. Therefore, engineering companies have to deploy a great deal of time and human resources only for manually converting P&ID images into a computer aided design (CAD) file format. To reduce the inefficiency of the P&ID conversion, various symbols and texts in P&ID images should be automatically recognized. However, recognizing information in P&ID images is not an easy task. A P&ID image usually contains hundreds of symbol and text objects. Most objects are pretty small compared to the size of a whole image and are densely packed together. Traditional recognition methods based on geometrical features are not capable enough to recognize every elements of a P&ID image. To overcome these difficulties, state-of-the-art deep learning models, RetinaNet and connectionist text proposal network (CTPN) were used to build a system for recognizing symbols and texts in a P&ID image. Using the RetinaNet and the CTPN model carefully modified and tuned for P&ID image dataset, the developed system recognizes texts, equipment symbols, piping symbols and instrumentation symbols from an input P&ID image and save the recognition results as the pre-defined extensible markup language format. In the test using a commercial P&ID image, the P&ID information recognition system correctly recognized 97% of the symbols and 81.4% of the texts.

Keywords: object recognition system, P&ID, symbol recognition, text recognition

Procedia PDF Downloads 140