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MaxEnt 2019—Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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ISBN: 9783039284764 9783039284771 Year: Pages: 312 DOI: 10.3390/books978-3-03928-477-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Mathematics
Added to DOAB on : 2020-04-07 23:07:09
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This Proceedings book presents papers from the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2019. The workshop took place at the Max Planck Institute for Plasma Physics in Garching near Munich, Germany, from 30 June to 5 July 2019, and invited contributions on all aspects of probabilistic inference, including novel techniques, applications, and work that sheds new light on the foundations of inference. Addressed are inverse and uncertainty quantification (UQ) and problems arising from a large variety of applications, such as earth science, astrophysics, material and plasma science, imaging in geophysics and medicine, nondestructive testing, density estimation, remote sensing, Gaussian process (GP) regression, optimal experimental design, data assimilation, and data mining.

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

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ISBN: 9783038976981 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

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ISBN: 9783039213757 9783039213764 Year: Pages: 344 DOI: 10.3390/books978-3-03921-376-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
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This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR

Keywords

Vehicle-to-X communications --- Intelligent Transport Systems --- VANET --- DSRC --- Geobroadcast --- multi-sensor --- fusion --- deep learning --- LiDAR --- camera --- ADAS --- object tracking --- kernel based MIL algorithm --- Gaussian kernel --- adaptive classifier updating --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- joystick --- two-wheeled --- terrestrial vehicle --- path planning --- infinity norm --- p-norm --- kinematic control --- navigation --- actuation systems --- maneuver algorithm --- automated driving --- cooperative systems --- communications --- interface --- automated-manual transition --- driver monitoring --- visual tracking --- discriminative correlation filter bank --- occlusion --- sub-region --- global region --- autonomous vehicles --- driving decision-making model --- the emergency situations --- red light-running behaviors --- ethical and legal factors --- T-S fuzzy neural network --- road lane detection --- map generation --- driving assistance --- autonomous driving --- real-time object detection --- autonomous driving assistance system --- urban object detector --- convolutional neural networks --- machine vision --- biological vision --- deep learning --- convolutional neural network --- Gabor convolution kernel --- recurrent neural network --- enhanced learning --- autonomous vehicle --- crash injury severity prediction --- support vector machine model --- emergency decisions --- relative speed --- total vehicle mass of the front vehicle --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- drowsiness detection --- smart band --- electrocardiogram (ECG) --- photoplethysmogram (PPG) --- recurrence plot (RP) --- convolutional neural network (CNN) --- squeeze-and-excitation --- residual learning --- depthwise separable convolution --- blind spot detection --- machine learning --- neural networks --- predictive --- vehicle dynamics --- electric vehicles --- FPGA --- GPU --- parallel architectures --- optimization --- panoramic image dataset --- road scene --- object detection --- deep learning --- convolutional neural network --- driverless --- autopilot --- deep leaning --- object detection --- generative adversarial nets --- image inpainting --- n/a

Mobile Mapping Technologies

Authors: --- --- ---
ISBN: 9783039280186 9783039280193 Year: Pages: 334 DOI: 10.3390/books978-3-03928-019-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-01-07 09:08:26
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Mobile Mapping technologies have seen a rapid growth of research activity and interest in the last years, due to the increased demand of accurate, dense and geo-referenced 3D data. Their main characteristic is the ability of acquiring 3D information of large areas dynamically. This versatility has expanded their application fields from the civil engineering to a broader range (industry, emergency response, cultural heritage...), which is constantly widening. This increased number of needs, some of them specially challenging, is pushing the Scientific Community, as well as companies, towards the development of innovative solutions, ranging from new hardware / open source software approaches and integration with other devices, up to the adoption of artificial intelligence methods for the automatic extraction of salient features and quality assessment for performance verification The aim of the present book is to cover the most relevant topics and trends in Mobile Mapping Technology, and also to introduce the new tendencies of this new paradigm of geospatial science.

Keywords

cultural heritage --- restoration --- indoor mapping --- laser scanning --- wearable mobile laser system --- 3D digitalization --- SLAM --- visual landmark sequence --- indoor topological localization --- convolutional neural network (CNN) --- second order hidden Markov model --- ORB-SLAM2 --- binary vocabulary --- small-scale vocabulary --- rapid relocation --- terrestrial laser scanning --- tunnel central axis --- tunnel cross section --- enhanced RANSAC --- quadric fitting --- constrained nonlinear least-squares problem --- visual simultaneous localization and mapping --- dynamic environment --- RGB-D camera --- encoder --- OctoMap --- IMMS --- indoor mapping --- MLS --- mobile laser scanning --- SLAM --- point clouds --- 2D laser scanner --- 2D laser range-finder --- LiDAR --- LRF --- sensors configurations --- Lidar localization system --- unmanned vehicle --- segmentation-based feature extraction --- category matching --- multi-group-step L-M optimization --- map management --- indoor mapping --- room type tagging --- semantic enrichment --- grammar --- Bayesian inference --- indoor localization --- crowdsourcing trajectory --- fingerprinting --- smartphone --- mobile mapping --- laser scanning --- self-calibration --- 3D point clouds --- geometric features --- motion estimation --- trajectory fusion --- mobile mapping --- sensor fusion --- optical sensors --- robust statistical analysis --- portable mobile mapping system --- handheld --- 3D processing --- point cloud --- Vitis vinifera --- terrestrial laser scanning --- plant vigor --- mobile mapping --- precision agriculture --- vine size --- visual positioning --- indoor scenes --- automated database construction --- image retrieval

Visual Sensors

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ISBN: 9783039283385 9783039283392 Year: Pages: 738 DOI: 10.3390/books978-3-03928-339-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:09
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Visual sensors are able to capture a large quantity of information from the environment around them. A wide variety of visual systems can be found, from the classical monocular systems to omnidirectional, RGB-D, and more sophisticated 3D systems. Every configuration presents some specific characteristics that make them useful for solving different problems. Their range of applications is wide and varied, including robotics, industry, agriculture, quality control, visual inspection, surveillance, autonomous driving, and navigation aid systems. In this book, several problems that employ visual sensors are presented. Among them, we highlight visual SLAM, image retrieval, manipulation, calibration, object recognition, navigation, etc.

Keywords

3D reconstruction --- RGB-D sensor --- non-rigid reconstruction --- pedestrian detection --- boosted decision tree --- scale invariance --- receptive field correspondence --- soft decision tree --- single-shot 3D shape measurement --- digital image correlation --- warp function --- inverse compositional Gauss-Newton algorithm --- UAV image --- dynamic programming --- seam-line --- optical flow --- image mosaic --- iris recognition --- presentation attack detection --- convolutional neural network --- support vector machines --- content-based image retrieval --- textile retrieval --- textile localization --- texture retrieval --- texture description --- visual sensors --- iris recognition --- iris segmentation --- semantic segmentation --- convolutional neural network (CNN) --- visible light and near-infrared light camera sensors --- laser sensor --- line scan camera --- lane marking detection --- support vector machine (SVM) --- image binarization --- lane marking reconstruction --- automated design --- vision system --- FOV --- illumination --- recognition algorithm --- action localization --- action segmentation --- 3D ConvNets --- LSTM --- visual sensors --- image retrieval --- hybrid histogram descriptor --- perceptually uniform histogram --- motif co-occurrence histogram --- omnidirectional imaging --- visual localization --- catadioptric sensor --- visual information fusion --- image processing --- underwater imaging --- embedded systems --- stereo vision --- visual odometry --- 3D reconstruction --- handshape recognition --- sign language --- finger alphabet --- skeletal data --- visual odometry --- ego-motion estimation --- stereo --- RGB-D --- mobile robots --- around view monitor (AVM) system --- automatic calibration --- lane marking --- parking assist system --- advanced driver assistance system (ADAS) --- pose estimation --- symmetry axis --- point cloud --- sweet pepper --- semantic mapping --- RGB-D SLAM --- visual mapping --- indoor visual SLAM --- adaptive model --- motion estimation --- stereo camera --- person re-identification --- end-to-end architecture --- appearance-temporal features --- Siamese network --- pivotal frames --- visual tracking --- correlation filters --- motion-aware --- adaptive update strategy --- confidence response map --- camera calibration --- Gray code --- checkerboard --- visual sensor --- image retrieval --- human visual system --- local parallel cross pattern --- pose estimation --- straight wing aircraft --- structure extraction --- consistent line clustering --- parallel line --- planes intersection --- salient region detection --- appearance based model --- regression based model --- human visual attention --- background dictionary --- quality control --- fringe projection profilometry --- depth image registration --- 3D reconstruction --- speed measurement --- stereo-vision --- large field of view --- vibration --- calibration --- CLOSIB --- statistical information of gray-levels differences --- Local Binary Patterns --- texture classification --- texture description --- Visual Sensors --- SLAM --- RGB-D --- indoor environment --- Manhattan frame estimation --- orientation relevance --- spatial transformation --- robotic welding --- seam tracking --- visual detection --- narrow butt joint --- GTAW --- LRF --- camera calibration --- extrinsic calibration --- sensors combination --- geometric moments --- camera pose --- rotation-angle --- measurement error --- robotics --- robot manipulation --- depth vision --- star image prediction --- star sensor --- Richardson-Lucy algorithm --- neural network --- tightly-coupled VIO --- SLAM --- fused point and line feature matching --- pose estimates --- simplified initialization strategy --- patrol robot --- map representation --- vision-guided robotic grasping --- object recognition --- pose estimation --- global feature descriptor --- iterative closest point --- n/a

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