Search results for: Lansat8 OLI image
2123 Analysis of Histogram Asymmetry for Waste Recognition
Authors: Janusz Bobulski, Kamila Pasternak
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Despite many years of effort and research, the problem of waste management is still current. So far, no fully effective waste management system has been developed. Many programs and projects improve statistics on the percentage of waste recycled every year. In these efforts, it is worth using modern Computer Vision techniques supported by artificial intelligence. In the article, we present a method of identifying plastic waste based on the asymmetry analysis of the histogram of the image containing the waste. The method is simple but effective (94%), which allows it to be implemented on devices with low computing power, in particular on microcomputers. Such de-vices will be used both at home and in waste sorting plants.Keywords: waste management, environmental protection, image processing, computer vision
Procedia PDF Downloads 1132122 Multi-Vehicle Detection Using Histogram of Oriented Gradients Features and Adaptive Sliding Window Technique
Authors: Saumya Srivastava, Rina Maiti
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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine
Procedia PDF Downloads 1142121 Enhancement of X-Rays Images Intensity Using Pixel Values Adjustments Technique
Authors: Yousif Mohamed Y. Abdallah, Razan Manofely, Rajab M. Ben Yousef
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X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image.Keywords: enhancement, x-rays, pixel intensity values, MatLab
Procedia PDF Downloads 4742120 Content-Aware Image Augmentation for Medical Imaging Applications
Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang
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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving
Procedia PDF Downloads 2102119 Automated Localization of Palpebral Conjunctiva and Hemoglobin Determination Using Smart Phone Camera
Authors: Faraz Tahir, M. Usman Akram, Albab Ahmad Khan, Mujahid Abbass, Ahmad Tariq, Nuzhat Qaiser
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The objective of this study was to evaluate the Degree of anemia by taking the picture of the palpebral conjunctiva using Smartphone Camera. We have first localized the region of interest from the image and then extracted certain features from that Region of interest and trained SVM classifier on those features and then, as a result, our system classifies the image in real-time on their level of hemoglobin. The proposed system has given an accuracy of 70%. We have trained our classifier on a locally gathered dataset of 30 patients.Keywords: anemia, palpebral conjunctiva, SVM, smartphone
Procedia PDF Downloads 4942118 SEM Image Classification Using CNN Architectures
Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran
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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope
Procedia PDF Downloads 1182117 Influence of the Paint Coating Thickness in Digital Image Correlation Experiments
Authors: Jesús A. Pérez, Sam Coppieters, Dimitri Debruyne
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In the past decade, the use of digital image correlation (DIC) techniques has increased significantly in the area of experimental mechanics, especially for materials behavior characterization. This non-contact tool enables full field displacement and strain measurements over a complete region of interest. The DIC algorithm requires a random contrast pattern on the surface of the specimen in order to perform properly. To create this pattern, the specimen is usually first coated using a white matt paint. Next, a black random speckle pattern is applied using any suitable method. If the applied paint coating is too thick, its top surface may not be able to exactly follow the deformation of the specimen, and consequently, the strain measurement might be underestimated. In the present article, a study of the influence of the paint thickness on the strain underestimation is performed for different strain levels. The results are then compared to typical paint coating thicknesses applied by experienced DIC users. A slight strain underestimation was observed for paint coatings thicker than about 30μm. On the other hand, this value was found to be uncommonly high compared to coating thicknesses applied by DIC users.Keywords: digital image correlation, paint coating thickness, strain
Procedia PDF Downloads 5102116 The Relationship between Body Image, Eating Behavior and Nutritional Status for Female Athletes
Authors: Selen Muftuoglu, Dilara Kefeli
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The present study was conducted by using the cross-sectional study design and to determine the relationship between body image, eating behavior and nutritional status in 80 female athletes who were basketball, volleyball, flag football, indoor soccer, and ice hockey players. This study demonstrated that 70.0% of the female athletes had skipped meal. Also, female athletes had a normal body mass index (BMI), but 65.0% of them indicated that want to be thinner. On the other hand, we analyzed that their daily nutrients intake, so we observed that 43.4% of the energy was from the fatty acids, especially saturated fatty acids, and they had lower fiber, calcium and iron intake. Also, we found that BMI, waist circumference, waist to hip ratio were negatively correlated with Multidimensional Body-Self Relations Questionnaire and The Dutch Eating Behavior Questionnaire score and they were lower in who had meal skipped or not received diet therapy. As a conclusion, nutrition education is frequently neglected in sports programs. There is a paucity of nutrition education interventions among different sports.Keywords: body image, eating behavior, eating disorders, female athletes, nutritional status
Procedia PDF Downloads 1532115 Lacunarity measures on Mammographic Image Applying Fractal Dimension and Lacunarity Measures
Authors: S. Sushma, S. Balasubramanian, K. C. Latha, R. Sridhar
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Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and lacunarity contribute to assess breast cancer risk. Fractal Dimension represents the complexity while the lacunarity characterize the gap of a fractal dimension. In this paper, we present our result confirming that the lacunarity value resulted in algorithm using mammogram images states that level of lacunarity will be low when the Fractal Dimension value will be high.Keywords: breast cancer, fractal dimension, image analysis, lacunarity, mammogram
Procedia PDF Downloads 3812114 Recognition of Gene Names from Gene Pathway Figures Using Siamese Network
Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu
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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG
Procedia PDF Downloads 2802113 Characterization of Optical Systems for Intraocular Projection
Authors: Charles Q. Yu, Victoria H. Fan, Ahmed F. Al-Qahtani, Ibraim Viera
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Introduction: Over 12 million people are blind due to opacity of the cornea, the clear tissue forming the front of the eye. Current methods use plastic implants to produce a clear optical pathway into the eye but are limited by a high rate of complications. New implants utilizing completely inside-the-eye projection technology can overcome blindness due to scarring of the eye by producing images on the retina without need for a clear optical pathway into the eye and may be free of the complications of traditional treatments. However, the interior of the eye is a challenging location for the design of optical focusing systems which can produce a sufficiently high quality image. No optical focusing systems have previously been characterized for this purpose. Methods: 3 optical focusing systems for intraocular (inside the eye) projection were designed and then modeled with ray tracing software, including a pinhole system, a planoconvex, and an achromatic system. These were then constructed using off-the-shelf components and tested in the laboratory. Weight, size, magnification, depth of focus, image quality and brightness were characterized. Results: Image quality increased with complexity of system design, as did weight and size. A dual achromatic doublet optical system produced the highest image quality. The visual acuity equivalent achieved with this system was better than 20/200. Its weight was less than that of the natural human crystalline lens. Conclusions: We demonstrate for the first time that high quality images can be produced by optical systems sufficiently small and light to be implanted within the eye.Keywords: focusing, projection, blindness, cornea , achromatic, pinhole
Procedia PDF Downloads 1242112 A Robust Spatial Feature Extraction Method for Facial Expression Recognition
Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda
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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure
Procedia PDF Downloads 4222111 Development of Star Image Simulator for Star Tracker Algorithm Validation
Authors: Zoubida Mahi
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A successful satellite mission in space requires a reliable attitude and orbit control system to command, control and position the satellite in appropriate orbits. Several sensors are used for attitude control, such as magnetic sensors, earth sensors, horizon sensors, gyroscopes, and solar sensors. The star tracker is the most accurate sensor compared to other sensors, and it is able to offer high-accuracy attitude control without the need for prior attitude information. There are mainly three approaches in star sensor research: digital simulation, hardware in the loop simulation, and field test of star observation. In the digital simulation approach, all of the processes are done in software, including star image simulation. Hence, it is necessary to develop star image simulation software that could simulate real space environments and various star sensor configurations. In this paper, we present a new stellar image simulation tool that is used to test and validate the stellar sensor algorithms; the developed tool allows to simulate of stellar images with several types of noise, such as background noise, gaussian noise, Poisson noise, multiplicative noise, and several scenarios that exist in space such as the presence of the moon, the presence of optical system problem, illumination and false objects. On the other hand, we present in this paper a new star extraction algorithm based on a new centroid calculation method. We compared our algorithm with other star extraction algorithms from the literature, and the results obtained show the star extraction capability of the proposed algorithm.Keywords: star tracker, star simulation, star detection, centroid, noise, scenario
Procedia PDF Downloads 892110 Imperial/Royal Renewal in Byzantium and Medieval Georgia: Case of Alexios I Komnenos (r. 1081–1118) and Davit IV the Builder (r. 1089–1125)
Authors: Sandro Nikolaishvili
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The end of the eleventh and the beginning of the twelfth century was a transitional period for the Byzantine empire as well as for the Caucasus. The empire was struggling for its survival under Alexios I Komnenos while Medieval Georgia was emerging as a dominant player in the Caucasus under Davit IV the Builder. The reigns of these two rulers were periods of renewal and transformation. I aim to compare the imperial image of Alexios I Komnenos with the renewed kingship ideology under Davit IV. I will hypothesize about the possible translation of the Byzantine political culture into the Medieval Georgia.Keywords: Byzantium, Georgia, imperial, image
Procedia PDF Downloads 4122109 Implementation of Edge Detection Based on Autofluorescence Endoscopic Image of Field Programmable Gate Array
Authors: Hao Cheng, Zhiwu Wang, Guozheng Yan, Pingping Jiang, Shijia Qin, Shuai Kuang
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Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm.Keywords: AFI, edge detection, adaptive threshold, morphological processing, OV6930, FPGA
Procedia PDF Downloads 2242108 Self-serving Anchoring of Self-judgments
Authors: Elitza Z. Ambrus, Bjoern Hartig, Ryan McKay
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Individuals’ self-judgments might be malleable and influenced by comparison with a random value. On the one hand, self-judgments reflect our self-image, which is typically considered to be stable in adulthood. Indeed, people also strive hard to maintain a fixed, positive moral image of themselves. On the other hand, research has shown the robustness of the so-called anchoring effect on judgments and decisions. The anchoring effect refers to the influence of a previously considered comparative value (anchor) on a consecutive absolute judgment and reveals that individuals’ estimates of various quantities are flexible and can be influenced by a salient random value. The present study extends the anchoring paradigm to the domain of the self. We also investigate whether participants are more susceptible to self-serving anchors, i.e., anchors that enhance participant’s self-image, especially their moral self-image. In a pre-reregistered study via the online platform Prolific, 249 participants (156 females, 89 males, 3 other and 1 who preferred not to specify their gender; M = 35.88, SD = 13.91) ranked themselves on eight personality characteristics. However, in the anchoring conditions, respondents were asked to first indicate whether they thought they would rank higher or lower than a given anchor value before providing their estimated rank in comparison to 100 other anonymous participants. A high and a low anchor value were employed to differentiate between anchors in a desirable (self-serving) direction and anchors in an undesirable (self-diminishing) direction. In the control treatment, there was no comparison question. Subsequently, participants provided their self-rankings on the eight personality traits with two personal characteristics for each combination of the factors desirable/undesirable and moral/non-moral. We found evidence of an anchoring effect for self-judgments. Moreover, anchoring was more efficient when people were anchored in a self-serving direction: the anchoring effect was enhanced when supporting a more favorable self-view and mitigated (even reversed) when implying a deterioration of the self-image. The self-serving anchoring was more pronounced for moral than for non-moral traits. The data also provided evidence in support of a better-than-average effect in general as well as a magnified better-than-average effect for moral traits. Taken together, these results suggest that self-judgments might not be as stable in adulthood as previously thought. In addition, considerations of constructing and maintaining a positive self-image might interact with the anchoring effect on self-judgments. Potential implications of our results concern the construction and malleability of self-judgments as well as the psychological mechanism shaping anchoring.Keywords: anchoring, better-than-average effect, self-judgments, self-serving anchoring
Procedia PDF Downloads 1742107 Digital Image Correlation: Metrological Characterization in Mechanical Analysis
Authors: D. Signore, M. Ferraiuolo, P. Caramuta, O. Petrella, C. Toscano
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The Digital Image Correlation (DIC) is a newly developed optical technique that is spreading in all engineering sectors because it allows the non-destructive estimation of the entire surface deformation without any contact with the component under analysis. These characteristics make the DIC very appealing in all the cases the global deformation state is to be known without using strain gages, which are the most used measuring device. The DIC is applicable to any material subjected to distortion caused by either thermal or mechanical load, allowing to obtain high-definition mapping of displacements and deformations. That is why in the civil and the transportation industry, DIC is very useful for studying the behavior of metallic materials as well as of composite materials. DIC is also used in the medical field for the characterization of the local strain field of the vascular tissues surface subjected to uniaxial tensile loading. DIC can be carried out in the two dimension mode (2D DIC) if a single camera is used or in a three dimension mode (3D DIC) if two cameras are involved. Each point of the test surface framed by the cameras can be associated with a specific pixel of the image, and the coordinates of each point are calculated knowing the relative distance between the two cameras together with their orientation. In both arrangements, when a component is subjected to a load, several images related to different deformation states can be are acquired through the cameras. A specific software analyzes the images via the mutual correlation between the reference image (obtained without any applied load) and those acquired during the deformation giving the relative displacements. In this paper, a metrological characterization of the digital image correlation is performed on aluminum and composite targets both in static and dynamic loading conditions by comparison between DIC and strain gauges measures. In the static test, interesting results have been obtained thanks to an excellent agreement between the two measuring techniques. In addition, the deformation detected by the DIC is compliant with the result of a FEM simulation. In the dynamic test, the DIC was able to follow with a good accuracy the periodic deformation of the specimen giving results coherent with the ones given by FEM simulation. In both situations, it was seen that the DIC measurement accuracy depends on several parameters such as the optical focusing, the parameters chosen to perform the mutual correlation between the images and, finally, the reference points on image to be analyzed. In the future, the influence of these parameters will be studied, and a method to increase the accuracy of the measurements will be developed in accordance with the requirements of the industries especially of the aerospace one.Keywords: accuracy, deformation, image correlation, mechanical analysis
Procedia PDF Downloads 3042106 The Need for Career Education Based on Self-Esteem in Japanese Youths
Authors: Kumiko Inagaki
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Because of the rapidly changing social and industrial world, career education in Japan has recently gained in popularity with the government’s support. However, it has not fostered proactive mindsets and attitudes in the youths. This paper first provides a background of career education in Japan. Next, based on the International Survey of Youth Attitude, Japanese youths’ views of themselves and their future were identified and then compared to the views of youths in six other countries. Assessments of the feelings of self-satisfaction and future hopes of Japanese youths returned very low scores. Suggestions were offered on career education in order to promote a positive self-image.Keywords: career education, self-esteem, self-image, youth attitude
Procedia PDF Downloads 4732105 A Pragmatic Approach of Memes Created in Relation to the COVID-19 Pandemic
Authors: Alexandra-Monica Toma
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Internet memes are an element of computer mediated communication and an important part of online culture that combines text and image in order to generate meaning. This term coined by Richard Dawkings refers to more than a mere way to briefly communicate ideas or emotions, thus naming a complex and an intensely perpetuated phenomenon in the virtual environment. This paper approaches memes as a cultural artefact and a virtual trope that mirrors societal concerns and issues, and analyses the pragmatics of their use. Memes have to be analysed in series, usually relating to some image macros, which is proof of the interplay between imitation and creativity in the memes’ writing process. We believe that their potential to become viral relates to three key elements: adaptation to context, reference to a successful meme series, and humour (jokes, irony, sarcasm), with various pragmatic functions. The study also uses the concept of multimodality and stresses how the memes’ text interacts with the image, discussing three types of relations: symmetry, amplification, and contradiction. Moreover, the paper proves that memes could be employed as speech acts with illocutionary force, when the interaction between text and image is enriched through the connection to a specific situation. The features mentioned above are analysed in a corpus that consists of memes related to the COVID-19 pandemic. This corpus shows them to be highly adaptable to context, which helps build the feeling of connection and belonging in an otherwise tremendously fragmented world. Some of them are created based on well-known image macros, and their humour results from an intricate dialogue between texts and contexts. Memes created in relation to the COVID-19 pandemic can be considered speech acts and are often used as such, as proven in the paper. Consequently, this paper tackles the key features of memes, makes a thorough analysis of the memes sociocultural, linguistic, and situational context, and emphasizes their intertextuality, with special accent on their illocutionary potential.Keywords: context, memes, multimodality, speech acts
Procedia PDF Downloads 1912104 Image Segmentation: New Methods
Authors: Flaurence Benjamain, Michel Casperance
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We present in this paper, first, a comparative study of three mathematical theories to achieve the fusion of information sources. This study aims to identify the characteristics inherent in theories of possibilities, belief functions (DST) and plausible and paradoxical reasoning to establish a strategy of choice that allows us to adopt the most appropriate theory to solve a problem of fusion in order, taking into account the acquired information and imperfections that accompany them. Using the new theory of plausible and paradoxical reasoning, also called Dezert-Smarandache Theory (DSmT), to fuse information multi-sources needs, at first step, the generation of the composites events witch is, in general, difficult. Thus, we present in this paper a new approach to construct pertinent paradoxical classes based on gray levels histograms, which also allows to reduce the cardinality of the hyper-powerset. Secondly, we developed a new technique for order and coding generalized focal elements. This method is exploited, in particular, to calculate the cardinality of Dezert and Smarandache. Then, we give an experimentation of classification of a remote sensing image that illustrates the given methods and we compared the result obtained by the DSmT with that resulting from the use of the DST and theory of possibilities.Keywords: segmentation, image, approach, vision computing
Procedia PDF Downloads 2682103 Counting People Utilizing Space-Time Imagery
Authors: Ahmed Elmarhomy, K. Terada
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An automated method for counting passerby has been proposed using virtual-vertical measurement lines. Space-time image is representing the human regions which are treated using the segmentation process. Different color space has been used to perform the template matching. A proper template matching has been achieved to determine direction and speed of passing people. Distinguish one or two passersby has been investigated using a correlation between passerby speed and the human-pixel area. Finally, the effectiveness of the presented method has been experimentally verified.Keywords: counting people, measurement line, space-time image, segmentation, template matching
Procedia PDF Downloads 4462102 The Democratization of 3D Capturing: An Application Investigating Google Tango Potentials
Authors: Carlo Bianchini, Lorenzo Catena
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The appearance of 3D scanners and then, more recently, of image-based systems that generate point clouds directly from common digital images have deeply affected the survey process in terms of both capturing and 2D/3D modelling. In this context, low cost and mobile systems are increasingly playing a key role and actually paving the way to the democratization of what in the past was the realm of few specialized technicians and expensive equipment. The application of Google Tango on the ancient church of Santa Maria delle Vigne in Pratica di Mare – Rome presented in this paper is one of these examples.Keywords: the architectural survey, augmented/mixed/virtual reality, Google Tango project, image-based 3D capturing
Procedia PDF Downloads 1442101 Image Processing Approach for Detection of Three-Dimensional Tree-Rings from X-Ray Computed Tomography
Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz
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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology
Procedia PDF Downloads 2132100 Derivation of Bathymetry Data Using Worldview-2 Multispectral Images in Shallow, Turbid and Saline Lake Acıgöl
Authors: Muhittin Karaman, Murat Budakoglu
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In this study, derivation of lake bathymetry was evaluated using the high resolution Worldview-2 multispectral images in the very shallow hypersaline Lake Acıgöl which does not have a stable water table due to the wet-dry season changes and industrial usage. Every year, a great part of the lake water budget has been consumed for the industrial salt production in the evaporation ponds, which are generally located on the south and north shores of Lake Acıgöl. Therefore, determination of the water level changes from a perspective of remote sensing-based lake water by bathymetry studies has a great importance in the sustainability-control of the lake. While the water table interval is around 1 meter between dry and wet season, dissolved ion concentration, salinity and turbidity also show clear differences during these two distinct seasonal periods. At the same time, with the satellite data acquisition (June 9, 2013), a field study was conducted to collect the salinity values, Secchi disk depths and turbidity levels. Max depth, Secchi disk depth and salinity were determined as 1,7 m, 0,9 m and 43,11 ppt, respectively. Eight-band Worldview-2 image was corrected for atmospheric effects by ATCOR technique. For each sampling point in the image, mean reflectance values in 1*1, 3*3, 5*5, 7*7, 9*9, 11*11, 13*13, 15*15, 17*17, 19*19, 21*21, 51*51 pixel reflectance neighborhoods were calculated separately. A unique image has been derivated for each matrix resolution. Spectral values and depth relation were evaluated for these distinct resolution images. Correlation coefficients were determined for the 1x1 matrix: 0,98, 0,96, 0,95 and 0,90 for the 724 nm, 831 nm, 908 nm and 659 nm, respectively. While 15x5 matrix characteristics with 0,98, 0,97 and 0,97 correlation values for the 724 nm, 908 nm and 831 nm, respectively; 51x51 matrix shows 0,98, 0,97 and 0,96 correlation values for the 724 nm, 831 nm and 659 nm, respectively. Comparison of all matrix resolutions indicates that RedEdge band (724 nm) of the Worldview-2 satellite image has the best correlation with the saline shallow lake of Acıgöl in-situ depth.Keywords: bathymetry, Worldview-2 satellite image, ATCOR technique, Lake Acıgöl, Denizli, Turkey
Procedia PDF Downloads 4342099 Liver and Liver Lesion Segmentation From Abdominal CT Scans
Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid
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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithmKeywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm
Procedia PDF Downloads 4432098 Stereotypes in Perception of Otherness in Balkans Literature from the Last Part of 20ᵗʰ Century
Authors: Magdalena Kostova-Panayotova, Neda-Maria Panayotova
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The article is focused on a problem that tends to be extremely characteristic and essential to European literature – the relations between the Balkan Peninsula and Europe and the stereotypes the Balkans evoke – a melting pot, a powder keg, a bridge, a crossroads, along with other negative definitions. The stereotypes and visions are examined as the layered images of a particular nation. The work deals with the Balkan writers’ way of confronting stereotypes by reversing the image of the ‘dark’ Balkans and the ‘bright’ Europe and thus establishing the Balkans as a place of beauty, music, and poetry. In many aspects, the European image of the Balkans (the so-called Balkanism) is comparable to the European attitude to the Orient (the so-called Orientalism). On the basis of the analysis of specific texts by Balkan authors, the article proves that the identity of the person of the late 20th and early 21st century is something individual and much more complicated than a patriotic self-definition because the identity of the contemporary person is multilayered. It is not flattering to be a bridge, a crossroads or a corner. However, a person is a creature of transition. Our idea demonstrates that the state of transition always brings both weakness and strength – it is the Balkans that connect Europe to the world.Keywords: image, Slavs, Balkans, identity of the modern Balkan person
Procedia PDF Downloads 1372097 Isolation and Classification of Red Blood Cells in Anemic Microscopic Images
Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George
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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC
Procedia PDF Downloads 3982096 Effect of Mindfulness-Based Self-Care Training on Self-Esteem and Body Image Concern on Candidate Patients of Orthognathic Surgery
Authors: Hamide Azimi Lolaty, Fateme Alsadat Ghanipoor, Azar Ramzani, Reza Ali Mohammadpoor, Alireza Babaei
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Background and Objective: Despite the merits behind orthognathic surgery, self-care training in such patients seems logical. The current research was performed pursuing the goal of outlining the effect of training mindfulness-based self-care on Self-Esteem (SE) and Body Image Concern (BIC) of orthognathic surgery candidate patients. Material and Methods: The present study was performed using a semi-experimental method with pre-and post-design in the control and intervention groups. The eligible patients to enter the Babol-based Shahid Beheshti Orthognathic Surgery Clinic were conveniently divided into two 25-person groups. The variables of Self-Esteem and Body Image Concern were measured before and after executing the eight 90-minute training sessions and in the follow-up period done three months after executing the intervention using Cooper Smith’s Self-Esteem Inventory (CSEI) and Body Image Concern Inventory (BICI). The data were analyzed using ANOVA and the independent t-test and using SPSS-26, the data were analyzed at a 0.05 level. Results: As a result of the intervention, the intervention group’s SE score critically changed on average from 25.4±7.31 in the pre-intervention to 31.16±7.05 in the post-intervention and to 40.45±3.51 in the follow-up period (P=0.01), the intervention group’s BIC score changed on average from 60.28±16.47 in the pre-intervention to 47.15±80.47 in the post-intervention and to 32.20 ± 10.73 in the follow-up period. This difference was meaningful (P=0.001). But due to time and the intervention interaction, the control group underwent this significant reduction with a delay. The study revealed the scores of the SE as 32± 6.84 and that of the BIC as 43.32±10.64 in the control group didn’t result in any meaningful statistical difference (P<0.05). Conclusion: Training mindfulness-based self-care exerts an effect on the SE and BIC of the patients undergoing orthognathic surgery. Therefore, it’s recommended to train mindfulness-based self-care for orthognathic surgery candidate patients.Keywords: self-care, mindfulness, self-esteem, body image concern, orthognathic surgery
Procedia PDF Downloads 1092095 Local Spectrum Feature Extraction for Face Recognition
Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd ZaizuIlyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh
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This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates.Keywords: local features modelling, face recognition system, Gaussian mixture models, Feret
Procedia PDF Downloads 6562094 An Image Processing Scheme for Skin Fungal Disease Identification
Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya
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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification
Procedia PDF Downloads 226