Rapid rises in population and urbanization can cause crucial and expeditious changes on land cover and land use. For this reason, monitoring in frequent periods of changes in the environment and heterogeneous areas is...
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Rapid rises in population and urbanization can cause crucial and expeditious changes on land cover and land use. For this reason, monitoring in frequent periods of changes in the environment and heterogeneous areas is needed for strategic planning of sustainable applications and optimized management issues. As known, various regional and global initiations co-operates on Earth Observation services for environmental monitoring. Some main topics for these services are land management projects, cadastre, forestry, agriculture, rural and urban planning, environmental monitoring and so on. The improving technology and studies enable to use and analyze many different data sources efficiently and develop new methods for image interpretation, geo-information extraction, and processing. Today, geospatial intelligence influences all spatial and geographical sciences, as well image analysis. Widespread usage of remote sensing images for concerning both Earth's physical features and increase of man-made environmental changes bring semi-automatic and automatic analysis classification methods by side. Especially, VHS resolution imagery is started to be used as object-based image analyses methods, rule-based classification methods. Developing countries are started to standardize the land use and land cover (LULC) classification systems and nomenclature managing information more effectively and rapidly at national or regional levels years ago. In this study, it is searched of advantages and requirements of ABM and agent based image analysis as an additional method to other image analysis methods for regional monitoring programs. In additionally, ABM stages of the classification algorithm and several ABM approaches (based on probability, Bayesian, Neural Network, and Genetic algorithms) are investigated and described with examples on remotely sensed data. In the result of the models;the different land cover and land use map products are compared and interpreted in a scientific expli
image segmentation is one of the most significant and inevitable task in variety areas ranging from face/object/character recognition and medical imaging applications to robotic control and self-driving vehicular syst...
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ISBN:
(纸本)9781538676424
image segmentation is one of the most significant and inevitable task in variety areas ranging from face/object/character recognition and medical imaging applications to robotic control and self-driving vehicular systems. Accuracy and processing time of image segmentation processes are also prominent parameters for quality of such computer vision systems. The proposed method incorporates three main pre-processing techniques such as Down Scaling/Sampling, Gamma Correction and Edge Preserving Smoothing so as to achieve accuracy and robustness of the segmentation. Pre-processing techniques are performed for both Fuzzy C-means (FCM) and K-means algorithm and all RGB information of image are taken into consideration while segmenting the image rather than using only gray scale. Performance analysis are performed on real-world images. Experiments show that, our method achieve higher accuracy levels and feasible processing time results compared to conventional FCM and K-means algorithms.
The proceedings contain 32 papers. The topics discussed include: micro-Doppler removal in radar imaging in the case of non-compensated rigid body acceleration;a decentralized platform for heterogeneous IoT networks ma...
ISBN:
(纸本)9781538636206
The proceedings contain 32 papers. The topics discussed include: micro-Doppler removal in radar imaging in the case of non-compensated rigid body acceleration;a decentralized platform for heterogeneous IoT networks management;on applying evolutionary algorithms for hybrid neural networks' architecture synthesis;an efficient MPPT algorithm for PV modules under partial shading and sudden change in irradiance;efficiency optimization of electrical devices;determination of the total impulse of the solid rocket motor by using two mathematical methods;review spam detection using machine learning;energy storage systems: an overview of existing technologies and analysis of their applications within the power system of montenegro;single phasing of three phase induction motors under various load conditions;security of AMR system in HPP Perucica;state-of-charge estimation of lithium-ion batteries using extended Kalman filter and unscented Kalman filter;imageprocessing based anomaly detection approach for synchronous movements in cyber-physical systems;one approach to acoustic signals contamination detection;optimization of fractal antennas in CST with chaotic optimization algorithm;and the virtual museum development with the use of intelligent and 3d technologies on the basis of the maritime museum in kotor.
The proceedings contain 69 papers. The special focus in this conference is on Advanced Machine Learning Technologies and Applications. The topics include: Pairwise Global Sequence Alignment Using Sine-Cosine Optimizat...
ISBN:
(纸本)9783319746890
The proceedings contain 69 papers. The special focus in this conference is on Advanced Machine Learning Technologies and Applications. The topics include: Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm;an Automated Fish Species Identification System Based on Crow Search Algorithm;Design and Implementation of Fuzzy PID Controller into Multi Agent Smart Library System Prototype;fuzzy Logic Controller with Color Vision System Tracking for Mobile Manipulator Robot;interactive Fuzzy Cellular Automata for Fast Person Re-Identification;decision Support System for Determination of Forces Applied in Orthodontic Based on Fuzzy Logic;design and Implementation of IoT Platform for Real Time systems;machine Learning: A Convergence of Emerging Technologies in Computing;discrimination of Satellite Signals from Opencast Mining of Mineral Ores of Hematite and Uranium Using Digital imageprocessing and Geostatistical algorithms;Fractional Order Sliding Mode PID Controller/Observer for Continuous Nonlinear Switched systems with PSO Parameter Tuning;detecting Cross-Site Scripting Attacks Using Machine Learning;text Mining Approach to Extract Associations Between Obesity and Arabic Herbal Plants;a Reinforcement Learning-Based Adaptive Learning System;Performance Evaluation of SVM-Based Amazighe Named Entity Recognition;trained Neural Networks Ensembles Weight Connections Analysis;An Empirical Analysis of User Behavior for P2P IPTV Workloads;intelligent Decision Framework to Explore and Control Infection of Hepatitis C Virus;supervised Rainfall Learning Model Using Machine Learning algorithms;reducing Stage Weight Estimation Error of Slow Task Detection in MapReduce Scheduling;analysis of Complete-Link Clustering for Identifying Multi-attributes Software Quality Data;modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine.
This paper introduces the problem of long-range monocular depth estimation for outdoor urban environments. Range sensors and traditional depth estimation algorithms (both stereo and single view) predict depth for dist...
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ISBN:
(纸本)9781538680940
This paper introduces the problem of long-range monocular depth estimation for outdoor urban environments. Range sensors and traditional depth estimation algorithms (both stereo and single view) predict depth for distances of less than 100 meters in outdoor settings and 10 meters in indoor settings. The shortcomings of outdoor single view methods that use learning approaches are, to some extent, due to the lack of long-range ground truth training data, which in turn is due to limitations of range sensors. To circumvent this, we first propose a novel strategy for generating synthetic long-range ground truth depth data. We utilize Google Earth images to reconstruct large-scale 3D models of different cities with proper scale. The acquired repository of 3D models and associated RGB views along with their long-range depth renderings are used as training data for depth prediction. We then train two deep neural network models for long-range depth estimation: i) a Convolutional Neural Network (CNN) and ii) a Generative Adversarial Network (GAN). We found in our experiments that the GAN model predicts depth more accurately. We plan to open-source the database and the baseline models for public use.
This book constitutes the refereed proceedings of the 12th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations, AIAI 2016, and three parallel workshops, held in Thessaloniki, ...
ISBN:
(纸本)9783319831688
This book constitutes the refereed proceedings of the 12th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations, AIAI 2016, and three parallel workshops, held in Thessaloniki, Greece, in September 2016. The workshops are the Third Workshop on New Methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. The 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. The 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. The papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.
The article describes the processes of collecting and adapting the diagnostic information necessary for use in automated analysis of three-dimensional images and surgical navigation. The work is carried out on the bas...
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The article describes the processes of collecting and adapting the diagnostic information necessary for use in automated analysis of three-dimensional images and surgical navigation. The work is carried out on the basis of the NMRC Obstetrics, Gynecology And Perinatology named after V.I. Kulakov of the Ministry of Health of the Russian Federation with the fmancial support of the Ministry of Science and Education of the Russian Federation (Agreement dated 03.10.2016 No. 14.607.21.0162, unique identifier REMEF160716X0162) The work is devoted to the features of collection, segmentation and description of the results of preoperative radiological diagnostics of newborn patients with congenital malformations of the lungs, such as bronchopulmonary sequestration (BS) and congenital cystic adenomatous malformation (CCAM). The goal of the work is the development of standards for the collection, classification and segmentation of various diagnostic information of congenital lung malformations in newborns necessary to use in automated three-dimensional image analysis and surgical navigation. In order to expand the scope of application, it was decided to supplement the data bank with information from the patient's phenotypic chart, compiled by the clinical geneticist when examining the patient. According to the developed and implemented algorithms we collected and segmented 924 series of images belonging to 148 patients with lung anomalies and 356 series of normal lung. Available text descriptions of the series are reconstructed to the original developed standard. At present, using this data bank, a subsystem of neural network analysis and reconstruction of diagnostic images of newborn patients is being developed, as well as a surgical navigation system for performing endoscopic surgical manipulations on patients for congenital malformations of the lungs. (C) 2018 The Authors. Published by Elsevier Ltd.
The retinal vascular tortuosity is a commonly used parameter for the early diagnosis of several diseases that affects the circulatory system. The manual analysis of fundus images for the tortuosity characterization is...
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The retinal vascular tortuosity is a commonly used parameter for the early diagnosis of several diseases that affects the circulatory system. The manual analysis of fundus images for the tortuosity characterization is a time-consuming and subjective task that presents a high inter-rater variability. Thus, automatic imageprocessing methods allow the efficient computation of objective and stable parameters for the issue. The validation of these methods is crucial to ensure an objective and reliable environment for the retinal. This paper describes a multi-expert analysis that measure the clinical performance as well as validation procedure of the computational tortuosity module of the Sirius framework, a computer-aided diagnoses platform for analyzing retinal imges. (C) 2018 The Authors. Published by Elseiver Ltd.
imageprocessing could be done in CPU or in Graphical processing Unit (GPU), using sequential programming or parallel programming respectively. Sequential and parallel programming are good in their own paradigm. This ...
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ISBN:
(纸本)9781509047154
imageprocessing could be done in CPU or in Graphical processing Unit (GPU), using sequential programming or parallel programming respectively. Sequential and parallel programming are good in their own paradigm. This paper analyses the performances of various basic imageprocessingalgorithms on GPU as well as CPU. Various images with a range of dimensions have been used for the testing purpose. The results show that the usability of the GPU for imageprocessing problems is highly depends on the nature of the problem and also on the size of the problem domain.
Traffic congestion remains a serious problem in transportation networks. Widely used navigation systems can only react to the presence of traffic jams but not to prevent their creation. One of the possibilities to pre...
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