Search results for: Image filters
552 Calibration Method for an Augmented Reality System
Authors: S. Malek, N. Zenati-Henda, M. Belhocine, S. Benbelkacem
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In geometrical camera calibration, the objective is to determine a set of camera parameters that describe the mapping between 3D references coordinates and 2D image coordinates. In this paper, a technique of calibration and tracking based on both a least squares method is presented and a correlation technique developed as part of an augmented reality system. This approach is fast and it can be used for a real time systemKeywords: Camera calibration, pinhole model, least squares method, augmented reality, strong calibration.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1988551 Person Identification using Gait by Combined Features of Width and Shape of the Binary Silhouette
Authors: M.K. Bhuyan, Aragala Jagan.
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Current image-based individual human recognition methods, such as fingerprints, face, or iris biometric modalities generally require a cooperative subject, views from certain aspects, and physical contact or close proximity. These methods cannot reliably recognize non-cooperating individuals at a distance in the real world under changing environmental conditions. Gait, which concerns recognizing individuals by the way they walk, is a relatively new biometric without these disadvantages. The inherent gait characteristic of an individual makes it irreplaceable and useful in visual surveillance. In this paper, an efficient gait recognition system for human identification by extracting two features namely width vector of the binary silhouette and the MPEG-7-based region-based shape descriptors is proposed. In the proposed method, foreground objects i.e., human and other moving objects are extracted by estimating background information by a Gaussian Mixture Model (GMM) and subsequently, median filtering operation is performed for removing noises in the background subtracted image. A moving target classification algorithm is used to separate human being (i.e., pedestrian) from other foreground objects (viz., vehicles). Shape and boundary information is used in the moving target classification algorithm. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Next, the Principal Component Analysis (PCA) is applied to the selected feature vector to reduce its dimensionality. These extracted feature vectors are used to train an Hidden Markov Model (HMM) for identification of some individuals. The proposed system is evaluated using some gait sequences and the experimental results show the efficacy of the proposed algorithm.Keywords: Gait Recognition, Gaussian Mixture Model, PrincipalComponent Analysis, MPEG-7 Angular Radial Transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1895550 2D Graphical Analysis of Wastewater Influent Capacity Time Series
Authors: Monika Chuchro, Maciej Dwornik
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The extraction of meaningful information from image could be an alternative method for time series analysis. In this paper, we propose a graphical analysis of time series grouped into table with adjusted colour scale for numerical values. The advantages of this method are also discussed. The proposed method is easy to understand and is flexible to implement the standard methods of pattern recognition and verification, especially for noisy environmental data.Keywords: graphical analysis, time series, seasonality, noisy environmental data
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1437549 A Selective Markovianity Approach for Image Segmentation
Authors: A. Melouah, H. Merouani
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A new Markovianity approach is introduced in this paper. This approach reduces the response time of classic Markov Random Fields approach. First, one region is determinated by a clustering technique. Then, this region is excluded from the study. The remaining pixel form the study zone and they are selected for a Markovianity segmentation task. With Selective Markovianity approach, segmentation process is faster than classic one.Keywords: Markovianity, response time, segmentation, study zone.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1447548 An Efficient Segmentation Method Based on Local Entropy Characteristics of Iris Biometrics
Authors: Ali Shojaee Bakhtiari, Ali Asghar Beheshti Shirazi, Amir Sepasi Zahmati
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An efficient iris segmentation method based on analyzing the local entropy characteristic of the iris image, is proposed in this paper and the strength and weaknesses of the method are analyzed for practical purposes. The method shows special strength in providing designers with an adequate degree of freedom in choosing the proper sections of the iris for their application purposes.Keywords: Iris segmentation, entropy, biocryptosystem, biometric identification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1416547 A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection
Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur
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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.
Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1084546 New Approach for Constructing a Secure Biometric Database
Authors: A. Kebbeb, M. Mostefai, F. Benmerzoug, Y. Chahir
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The multimodal biometric identification is the combination of several biometric systems; the challenge of this combination is to reduce some limitations of systems based on a single modality while significantly improving performance. In this paper, we propose a new approach to the construction and the protection of a multimodal biometric database dedicated to an identification system. We use a topological watermarking to hide the relation between face image and the registered descriptors extracted from other modalities of the same person for more secure user identification.
Keywords: Biometric databases, Multimodal biometrics, security authentication, Digital watermarking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2073545 A New Implementation of PCA for Fast Face Detection
Authors: Hazem M. El-Bakry
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Principal Component Analysis (PCA) has many different important applications especially in pattern detection such as face detection / recognition. Therefore, for real time applications, the response time is required to be as small as possible. In this paper, new implementation of PCA for fast face detection is presented. Such new implementation is designed based on cross correlation in the frequency domain between the input image and eigenvectors (weights). Simulation results show that the proposed implementation of PCA is faster than conventional one.Keywords: Fast Face Detection, PCA, Cross Correlation, Frequency Domain
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1783544 Safe, Effective, and Cost-Efficient Air Cleaning for Populated Rooms and Entire Buildings Based on the Disinfecting Power of Vaporized Hypochlorous Acid
Authors: D. Boecker, R. Breves, F. Herth, Z. Zhang, C. Bulitta
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Pathogen-carrying aerosol particles are recognized as important infection carriers like those in the current Corona pandemic. This infection route is often underestimated yet represents the infection route that has been least systematically countered to date. Particularly, the transmission indoors is of the highest concern but current indoor safety measures (e.g.: distancing, masks, filters) provide only limited protection. Inhalation of hypochlorous acid (HOCl) containing aerosols may become an alternate route to attack the incubating microbes in-situ and so potentially lead to a reduction of symptoms of already infected individuals. We investigated a facility-wide air-disinfection concept utilizing the potential of vaporized HOCl to become a disinfecting agent for populated indoor atmospheres. Aerosolized bacterial microbes were used as surrogates for a viral contamination, particularly the enveloped coronavirus. For the room air purification tests we aerosolized bacterial suspensions into lab chambers preloaded with vaporized HOCl solutions. Concentration of ‘free active chlorine’ in the test chamber atmosphere was determined with a special gas sensor system (Draeger AG, Lübeck, Germany) controlling the amount of vaporized HOCl via an aerosolis® device (oji Europe GmbH, Nauen, Germany). We could confirm the disinfecting power of HOCl in suspensions and determined the high efficacy of vaporized HOCl to disinfect atmospheres of populated indoor places at safe and non-irritant levels.
Keywords: Hypochlorous acid, HOCl, indoor air cleaning, infection control, microbial air burden, protective atmosphere.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 381543 Hand Gesture Detection via EmguCV Canny Pruning
Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae
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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.
Keywords: Canny pruning, hand recognition, machine learning, skin tracking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1278542 Normal and Peaberry Coffee Beans Classification from Green Coffee Bean Images Using Convolutional Neural Networks and Support Vector Machine
Authors: Hira Lal Gope, Hidekazu Fukai
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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.
Keywords: Convolutional neural networks, coffee bean, peaberry, sorting, support vector machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1505541 A Constrained Clustering Algorithm for the Classification of Industrial Ores
Authors: Luciano Nieddu, Giuseppe Manfredi
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In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures the information on the closed two-dimensional contour of an image, on images of industrial mineral ores.Keywords: K-means, Industrial ores classification, Invariant Features, Supervised Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1368540 Integrating Low and High Level Object Recognition Steps
Authors: András Barta, István Vajk
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In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.Keywords: Object recognition, Bayesian network, Wavelets, Document processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1471539 Intelligent Audio Watermarking using Genetic Algorithm in DWT Domain
Authors: M. Ketcham, S. Vongpradhip
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In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.
Keywords: Intelligent Audio Watermarking, GeneticAlgorithm, DWT Domain.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2043538 Algorithm for Bleeding Determination Based On Object Recognition and Local Color Features in Capsule Endoscopy
Authors: Yong-Gyu Lee, Jin Hee Park, Youngdae Seo, Gilwon Yoon
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Automatic determination of blood in less bright or noisy capsule endoscopic images is difficult due to low S/N ratio. Especially it may not be accurate to analyze these images due to the influence of external disturbance. Therefore, we proposed detection methods that are not dependent only on color bands. In locating bleeding regions, the identification of object outlines in the frame and features of their local colors were taken into consideration. The results showed that the capability of detecting bleeding was much improved.Keywords: Endoscopy, object recognition, bleeding, image processing, RGB.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1918537 Integrating Low and High Level Object Recognition Steps by Probabilistic Networks
Authors: András Barta, István Vajk
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In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.
Keywords: Object recognition, Bayesian network, Wavelets, Document processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1658536 A Parallel Architecture for the Real Time Correction of Stereoscopic Images
Authors: Zohir Irki, Michel Devy
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In this paper, we will present an architecture for the implementation of a real time stereoscopic images correction's approach. This architecture is parallel and makes use of several memory blocs in which are memorized pre calculated data relating to the cameras used for the acquisition of images. The use of reduced images proves to be essential in the proposed approach; the suggested architecture must so be able to carry out the real time reduction of original images.Keywords: Image reduction, Real-time correction, Parallel architecture, Parallel treatment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1095535 A Sentence-to-Sentence Relation Network for Recognizing Textual Entailment
Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee
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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1440534 A Robust Watermarking using Blind Source Separation
Authors: Anil Kumar, K. Negrat, A. M. Negrat, Abdelsalam Almarimi
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In this paper, we present a robust and secure algorithm for watermarking, the watermark is first transformed into the frequency domain using the discrete wavelet transform (DWT). Then the entire DWT coefficient except the LL (Band) discarded, these coefficients are permuted and encrypted by specific mixing. The encrypted coefficients are inserted into the most significant spectral components of the stego-image using a chaotic system. This technique makes our watermark non-vulnerable to the attack (like compression, and geometric distortion) of an active intruder, or due to noise in the transmission link.Keywords: Blind source separation (BSS), Chaotic system, Watermarking, DWT.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1516533 The Effect of Innovation Factors to Customer Loyalty by Structural Equation Model
Authors: M. Dachyar, Fatkhurrohman
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Innovation is being view from four areas of innovation, product, service, technology, and marketing. Whereas customer loyalty is composed of customer expectation, perceived quality, perceived value, corporate image, customer satisfaction, customer trust/confidence, customer commitment, customer complaint, and customer loyalty. This study aimed to investigate the influence of innovation factors to customer loyalty to GSM in the telecom companies where use of products and services. Structural Equation Modeling (SEM) using to analyze innovation factors. It was found the factor of innovation have significant influence on customer loyalty.Keywords: Innovation, telecommunication, customer loyalty, SEM
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3409532 The Use of Artificial Intelligence in Digital Forensics and Incident Response in a Constrained Environment
Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane
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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence (AI) is invaluable in identifying crime. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISAs). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The proposed framework development is implemented using the Java Agent Development Framework, Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISAs and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5% of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.
Keywords: Artificial intelligence, computer science, criminal investigation, digital forensics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1208531 Hello Kitty's Popularity and Its Change of Representation
Authors: Miho Tsukamoto
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Since “Hello Kitty” was manufactured in the market in 1974, the manufacturer, Sanrio Co., Ltd. gains high profits not only Kitty’s products but also Kitty license, which gives us a picture of Sanrio’s sales strategy in the global market. Kitty’s history, its products, and Sanrio’s sales strategy are researched in this paper. Comparing it to American Girl, and focusing on KITTYLAB, a type of attraction where you can enjoy games with Kitty, and choose its parts to build your own Kitty, the image of the cultural icon can be altered.
Keywords: American Girl, Change, Kitty, Popularity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2068530 Self Organizing Analysis Platform for Wear Particle
Authors: Qurban A. Memon, Mohammad S. Laghari
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Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear particle analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self organizing maps. This is achieved using relationship measurements among corresponding attributes of various measurements for wear particle. Finally, visualization technique is proposed that helps the viewer in understanding and utilizing these relationships that enable accurate diagnostics.Keywords: Neural Network, Relationship Measurement, Selforganizing Clusters, Wear Particle Analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2196529 Study of Qualitative and Quantitative Metric for Pixel Factor Mapping and Extended Pixel Mapping Method
Authors: Indradip Banerjee, Souvik Bhattacharyya, Gautam Sanyal
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In this paper, an approach is presented to investigate the performance of Pixel Factor Mapping (PFM) and Extended PMM (Pixel Mapping Method) through the qualitative and quantitative approach. These methods are tested against a number of well-known image similarity metrics and statistical distribution techniques. The PFM has been performed in spatial domain as well as frequency domain and the Extended PMM has also been performed in spatial domain through large set of images available in the internet.Keywords: Qualitative, quantitative, PFM, EXTENDED PMM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1049528 Analysis on the Feasibility of Landsat 8 Imagery for Water Quality Parameters Assessment in an Oligotrophic Mediterranean Lake
Authors: V. Markogianni, D. Kalivas, G. Petropoulos, E. Dimitriou
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Lake water quality monitoring in combination with the use of earth observation products constitutes a major component in many water quality monitoring programs. Landsat 8 images of Trichonis Lake (Greece) acquired on 30/10/2013 and 30/08/2014 were used in order to explore the possibility of Landsat 8 to estimate water quality parameters and particularly CDOM absorption at specific wavelengths, chlorophyll-a and nutrient concentrations in this oligotrophic freshwater body, characterized by inexistent quantitative, temporal and spatial variability. Water samples have been collected at 22 different stations, on late August of 2014 and the satellite image of the same date was used to statistically correlate the in-situ measurements with various combinations of Landsat 8 bands in order to develop algorithms that best describe those relationships and calculate accurately the aforementioned water quality components. Optimal models were applied to the image of late October of 2013 and the validation of the results was conducted through their comparison with the respective available in-situ data of 2013. Initial results indicated the limited ability of the Landsat 8 sensor to accurately estimate water quality components in an oligotrophic waterbody. As resulted by the validation process, ammonium concentrations were proved to be the most accurately estimated component (R = 0.7), followed by chl-a concentration (R = 0.5) and the CDOM absorption at 420 nm (R = 0.3). In-situ nitrate, nitrite, phosphate and total nitrogen concentrations of 2014 were measured as lower than the detection limit of the instrument used, hence no statistical elaboration was conducted. On the other hand, multiple linear regression among reflectance measures and total phosphorus concentrations resulted in low and statistical insignificant correlations. Our results were concurrent with other studies in international literature, indicating that estimations for eutrophic and mesotrophic lakes are more accurate than oligotrophic, owing to the lack of suspended particles that are detectable by satellite sensors. Nevertheless, although those predictive models, developed and applied to Trichonis oligotrophic lake are less accurate, may still be useful indicators of its water quality deterioration.Keywords: Landsat 8, oligotrophic lake, remote sensing, water quality.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1530527 Bleeding Detection Algorithm for Capsule Endoscopy
Authors: Yong-Gyu Lee, Gilwon Yoon
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Automatic detection of bleeding is of practical importance since capsule endoscopy produces an extremely large number of images. Algorithm development of bleeding detection in the digestive tract is difficult due to different contrasts among the images, food dregs, secretion and others. In this study, were assigned weighting factors derived from the independent features of the contrast and brightness between bleeding and normality. Spectral analysis based on weighting factors was fast and accurate. Results were a sensitivity of 87% and a specificity of 90% when the accuracy was determined for each pixel out of 42 endoscope images.Keywords: bleeding, capsule endoscopy, image analysis, weighted spectrum
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2097526 Streamwise Vorticity in the Wake of a Sliding Bubble
Authors: R. O’Reilly Meehan, D. B. Murray
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In many practical situations, bubbles are dispersed in a liquid phase. Understanding these complex bubbly flows is therefore a key issue for applications such as shell and tube heat exchangers, mineral flotation and oxidation in water treatment. Although a large body of work exists for bubbles rising in an unbounded medium, that of bubbles rising in constricted geometries has received less attention. The particular case of a bubble sliding underneath an inclined surface is common to two-phase flow systems. The current study intends to expand this knowledge by performing experiments to quantify the streamwise flow structures associated with a single sliding air bubble under an inclined surface in quiescent water. This is achieved by means of two-dimensional, two-component particle image velocimetry (PIV), performed with a continuous wave laser and high-speed camera. PIV vorticity fields obtained in a plane perpendicular to the sliding surface show that there is significant bulk fluid motion away from the surface. The associated momentum of the bubble means that this wake motion persists for a significant time before viscous dissipation. The magnitude and direction of the flow structures in the streamwise measurement plane are found to depend on the point on its path through which the bubble enters the plane. This entry point, represented by a phase angle, affects the nature and strength of the vortical structures. This study reconstructs the vorticity field in the wake of the bubble, converting the field at different instances in time to slices of a large-scale wake structure. This is, in essence, Taylor’s ”frozen turbulence” hypothesis. Applying this to the vorticity fields provides a pseudo three-dimensional representation from 2-D data, allowing for a more intuitive understanding of the bubble wake. This study provides insights into the complex dynamics of a situation common to many engineering applications, particularly shell and tube heat exchangers in the nucleate boiling regime.Keywords: Bubbly flow, particle image velocimetry, two-phase flow, wake structures.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1900525 A Support System to Consult Remote another Doctor on Assessment and/or Medical Treatment Plan when a Doctor has a Patient not in His/Her Major
Authors: T. Gotoh, T. Takayama, M. Ishiki, T. Ikeda
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Recently, majors of doctors are divided into terribly lots of detailed areas. However, it is actually not a rare case that a doctor has a patient who is not in his/her major. He/She must judge an assessment and make a medical treatment plan for this patient. According to our investigation, conventional approaches such as image diagnosis cooperation are insufficient. This paper proposes an 'Assessment / Medical Treatment Plan Consulting System'. We have implemented a pilot system based on our proposition. Its effectiveness is clarified by an evaluation.
Keywords: Application, computational intelligence and telecommunications, medicine, intelligent systems.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1715524 Classification of Radio Communication Signals using Fuzzy Logic
Authors: Zuzana Dideková, Beata Mikovičová
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Characterization of radio communication signals aims at automatic recognition of different characteristics of radio signals in order to detect their modulation type, the central frequency, and the level. Our purpose is to apply techniques used in image processing in order to extract pertinent characteristics. To the single analysis, we add several rules for checking the consistency of hypotheses using fuzzy logic. This allows taking into account ambiguity and uncertainty that may remain after the extraction of individual characteristics. The aim is to improve the process of radio communications characterization.Keywords: fuzzy classification, fuzzy inference system, radio communication signals, telecommunications
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1957523 A Novel Approach to Persian Online Hand Writing Recognition
Authors: Ramin Halavati, Mansour Jamzad, Mahdieh Soleymani
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Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.
Keywords: Image Processing, Pattern Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1307