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The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.
Technology: general issues --- History of engineering & technology --- supervised classification --- independent and non-identically distributed features --- analytical error probability --- empirical risk --- generalization error --- K-means clustering --- model compression --- population risk --- rate distortion theory --- vector quantization --- overfitting --- information criteria --- entropy --- model-based clustering --- merging mixture components --- component overlap --- interpretability --- time series prediction --- finite state machines --- hidden Markov models --- recurrent neural networks --- reservoir computers --- long short-term memory --- deep neural network --- information theory --- local information geometry --- feature extraction --- spiking neural network --- meta-learning --- information theoretic learning --- minimum error entropy --- artificial general intelligence --- closed-loop transcription --- linear discriminative representation --- rate reduction --- minimax game --- fairness --- HGR maximal correlation --- independence criterion --- separation criterion --- pattern dictionary --- atypicality --- Lempel–Ziv algorithm --- lossless compression --- anomaly detection --- information-theoretic bounds --- distribution and federated learning
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information theoretic analysis --- multiplexing system --- HSI for biology --- point target detection --- digital elevation model --- neural networks --- oxygen saturation --- black polymers --- PZT --- blood detection --- multivariate analysis --- integral imaging --- hemispherical conical reflectance factor (HCRF) --- sprouting --- fluorescence --- multitemporal hyperspectral images --- plant phenotyping --- hyperspectral data mining and compression --- Raman --- medical imaging by HSI --- compressive detection --- stereo imaging --- image processing --- wound healing --- quality control --- lossless compression --- infrared hyperspectral imaging --- spectral tracking --- time series --- remote sensing --- diabetic foot ulcer --- classification --- Raman spectroscopy --- imaging --- fingerprints --- fusion --- wavelength selection --- Cramer–Rao lower bound --- three-dimensional imaging --- chemical imaging --- CS-MUSI --- total variation --- coastal dynamics --- forward observation model --- hyperspectral imaging --- fluorescence hyperspectral imaging --- age determination --- potatoes --- painting samples --- predictive coding --- hyperspectral --- video --- bi-directional reflectance distribution function (BRDF) --- optimal binary filters --- watercolours --- deep learning --- spectroscopy --- moving vehicle imaging --- sorting --- maximum likelihood --- multivariate data analysis --- interval partial least squares --- disease detection --- Raman hyperspectral imaging --- primordial leaf count --- machine learning --- spatial light modulators (SLM) --- Virginia Coast Reserve Long Term Ecological Research (VCR LTER) --- digital micromirror device (DMD) --- hyperspectral microscopy --- alternating direction method of multipliers --- statistical methods for HSI --- multiband image fusion --- digital light processor (DLP) --- linear mixture model --- retouching pigments --- liquid crystal --- principal component analysis --- Chemometrics --- compressive sensing --- PLSR --- Hyperspectral imaging
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Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas.
History of engineering & technology --- image binarization --- optical character recognition --- local entropy filter --- thresholding --- image preprocessing --- image entropy --- image encryption --- medical color images --- RGB --- chaotic system --- crowd behavior analysis --- salient crowd motion detection --- repulsive force --- direction entropy --- node strength --- Pompe disease --- children --- quantitative muscle ultrasound --- texture-feature parametric imaging --- compound chaotic system --- S-box --- image information entropy --- image chaotic encryption --- cryptography --- Latin cube --- bit cube --- chosen plaintext attack --- atmosphere background --- engine flame --- infrared radiation --- detectability --- image quality evaluation --- image retrieval --- pooling method --- convolutional neural network --- feature distribution entropy --- lossless compression --- pattern classification --- machine learning --- malaria infection --- entropy --- Golomb–Rice codes --- image processing --- image segmentation --- weld segmentation --- weld evaluation --- convolution neural network --- Python --- Keras --- RSNNS --- MXNet --- brain-computer interface (BCI) --- electroencephalography (EEG) --- motor imagery (MI) --- continuous wavelet transform (CWT) --- convolutional neural network (CNN) --- hyperchaotic system --- filtering --- DNA computing --- diffusion --- deep neural network --- data expansion --- blind image quality assessment --- saliency and distortion --- human visual system --- declining quality --- data hiding --- AMBTC --- steganography --- stego image --- dictionary-based coding --- pixel value adjusting --- neuroaesthetics --- symmetry --- balance --- complexity --- chiaroscuro --- normalized entropy --- renaissance --- portrait paintings --- art history --- art statistics --- chaotic systems --- DNA coding --- security analysis --- magnetic resonance images --- non-maximum suppression --- object detection --- key-point detection --- IoU --- feature fusion --- quasi-resonant Rossby/drift wave triads --- Mordell elliptic curve --- pseudo-random numbers --- substitution box --- nuclear spin generator --- medical image --- peak signal-to-noise ratio --- key space calculation --- Duchenne muscular dystrophy --- ultrasound --- backscattered signals --- medical imaging --- neural engineering --- computer vision --- crowd motion detection --- security
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A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting
Technology: general issues --- on-board data compression --- CCSDS 123.0-B-2 --- near-lossless hyperspectral image compression --- hyperspectral image coding --- graph filterbanks --- integer-to-integer transforms --- graph signal processing --- compact data structure --- quadtree --- k2-tree --- k2-raster --- DACs --- 3D-CALIC --- M-CALIC --- hyperspectral images --- fully convolutional network --- semantic segmentation --- spectral image --- tensor decomposition --- HEVC --- intra coding --- JPEG 2000 --- high bit-depth compression --- multispectral satellite images --- crop classification --- Landsat-8 --- Sentinel-2 --- Elias codes --- Simple9 --- Simple16 --- PForDelta --- Rice codes --- hyperspectral scenes --- hyperspectral image --- lossy compression --- real time --- FPGA --- PCA --- JPEG2000 --- EBCOT --- multispectral --- hyperspectral --- CCSDS --- FAPEC --- data compression --- transform --- hyperspectral imaging --- on-board processing --- GPU --- real-time performance --- UAV --- parallel computing --- remote sensing --- image quality --- image classification --- visual quality metrics --- spectral–spatial feature --- multispectral image compression --- partitioned extraction --- group convolution --- rate-distortion --- compressed sensing --- invertible projection --- coupled dictionary --- singular value --- task-driven learning --- on board compression --- transform coding --- learned compression --- neural networks --- variational autoencoder --- complexity --- real-time compression --- on-board compression --- real-time transmission --- UAVs --- compressive sensing --- synthetic aperture sonar --- underwater sonar imaging --- remote sensing data compression --- lossless compression --- compression impact --- computational complexity
Choose an application
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas.
History of engineering & technology --- image binarization --- optical character recognition --- local entropy filter --- thresholding --- image preprocessing --- image entropy --- image encryption --- medical color images --- RGB --- chaotic system --- crowd behavior analysis --- salient crowd motion detection --- repulsive force --- direction entropy --- node strength --- Pompe disease --- children --- quantitative muscle ultrasound --- texture-feature parametric imaging --- compound chaotic system --- S-box --- image information entropy --- image chaotic encryption --- cryptography --- Latin cube --- bit cube --- chosen plaintext attack --- atmosphere background --- engine flame --- infrared radiation --- detectability --- image quality evaluation --- image retrieval --- pooling method --- convolutional neural network --- feature distribution entropy --- lossless compression --- pattern classification --- machine learning --- malaria infection --- entropy --- Golomb–Rice codes --- image processing --- image segmentation --- weld segmentation --- weld evaluation --- convolution neural network --- Python --- Keras --- RSNNS --- MXNet --- brain-computer interface (BCI) --- electroencephalography (EEG) --- motor imagery (MI) --- continuous wavelet transform (CWT) --- convolutional neural network (CNN) --- hyperchaotic system --- filtering --- DNA computing --- diffusion --- deep neural network --- data expansion --- blind image quality assessment --- saliency and distortion --- human visual system --- declining quality --- data hiding --- AMBTC --- steganography --- stego image --- dictionary-based coding --- pixel value adjusting --- neuroaesthetics --- symmetry --- balance --- complexity --- chiaroscuro --- normalized entropy --- renaissance --- portrait paintings --- art history --- art statistics --- chaotic systems --- DNA coding --- security analysis --- magnetic resonance images --- non-maximum suppression --- object detection --- key-point detection --- IoU --- feature fusion --- quasi-resonant Rossby/drift wave triads --- Mordell elliptic curve --- pseudo-random numbers --- substitution box --- nuclear spin generator --- medical image --- peak signal-to-noise ratio --- key space calculation --- Duchenne muscular dystrophy --- ultrasound --- backscattered signals --- medical imaging --- neural engineering --- computer vision --- crowd motion detection --- security
Choose an application
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting
Technology: general issues --- on-board data compression --- CCSDS 123.0-B-2 --- near-lossless hyperspectral image compression --- hyperspectral image coding --- graph filterbanks --- integer-to-integer transforms --- graph signal processing --- compact data structure --- quadtree --- k2-tree --- k2-raster --- DACs --- 3D-CALIC --- M-CALIC --- hyperspectral images --- fully convolutional network --- semantic segmentation --- spectral image --- tensor decomposition --- HEVC --- intra coding --- JPEG 2000 --- high bit-depth compression --- multispectral satellite images --- crop classification --- Landsat-8 --- Sentinel-2 --- Elias codes --- Simple9 --- Simple16 --- PForDelta --- Rice codes --- hyperspectral scenes --- hyperspectral image --- lossy compression --- real time --- FPGA --- PCA --- JPEG2000 --- EBCOT --- multispectral --- hyperspectral --- CCSDS --- FAPEC --- data compression --- transform --- hyperspectral imaging --- on-board processing --- GPU --- real-time performance --- UAV --- parallel computing --- remote sensing --- image quality --- image classification --- visual quality metrics --- spectral–spatial feature --- multispectral image compression --- partitioned extraction --- group convolution --- rate-distortion --- compressed sensing --- invertible projection --- coupled dictionary --- singular value --- task-driven learning --- on board compression --- transform coding --- learned compression --- neural networks --- variational autoencoder --- complexity --- real-time compression --- on-board compression --- real-time transmission --- UAVs --- compressive sensing --- synthetic aperture sonar --- underwater sonar imaging --- remote sensing data compression --- lossless compression --- compression impact --- computational complexity
Choose an application
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas.
image binarization --- optical character recognition --- local entropy filter --- thresholding --- image preprocessing --- image entropy --- image encryption --- medical color images --- RGB --- chaotic system --- crowd behavior analysis --- salient crowd motion detection --- repulsive force --- direction entropy --- node strength --- Pompe disease --- children --- quantitative muscle ultrasound --- texture-feature parametric imaging --- compound chaotic system --- S-box --- image information entropy --- image chaotic encryption --- cryptography --- Latin cube --- bit cube --- chosen plaintext attack --- atmosphere background --- engine flame --- infrared radiation --- detectability --- image quality evaluation --- image retrieval --- pooling method --- convolutional neural network --- feature distribution entropy --- lossless compression --- pattern classification --- machine learning --- malaria infection --- entropy --- Golomb–Rice codes --- image processing --- image segmentation --- weld segmentation --- weld evaluation --- convolution neural network --- Python --- Keras --- RSNNS --- MXNet --- brain-computer interface (BCI) --- electroencephalography (EEG) --- motor imagery (MI) --- continuous wavelet transform (CWT) --- convolutional neural network (CNN) --- hyperchaotic system --- filtering --- DNA computing --- diffusion --- deep neural network --- data expansion --- blind image quality assessment --- saliency and distortion --- human visual system --- declining quality --- data hiding --- AMBTC --- steganography --- stego image --- dictionary-based coding --- pixel value adjusting --- neuroaesthetics --- symmetry --- balance --- complexity --- chiaroscuro --- normalized entropy --- renaissance --- portrait paintings --- art history --- art statistics --- chaotic systems --- DNA coding --- security analysis --- magnetic resonance images --- non-maximum suppression --- object detection --- key-point detection --- IoU --- feature fusion --- quasi-resonant Rossby/drift wave triads --- Mordell elliptic curve --- pseudo-random numbers --- substitution box --- nuclear spin generator --- medical image --- peak signal-to-noise ratio --- key space calculation --- Duchenne muscular dystrophy --- ultrasound --- backscattered signals --- medical imaging --- neural engineering --- computer vision --- crowd motion detection --- security
Choose an application
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting
on-board data compression --- CCSDS 123.0-B-2 --- near-lossless hyperspectral image compression --- hyperspectral image coding --- graph filterbanks --- integer-to-integer transforms --- graph signal processing --- compact data structure --- quadtree --- k2-tree --- k2-raster --- DACs --- 3D-CALIC --- M-CALIC --- hyperspectral images --- fully convolutional network --- semantic segmentation --- spectral image --- tensor decomposition --- HEVC --- intra coding --- JPEG 2000 --- high bit-depth compression --- multispectral satellite images --- crop classification --- Landsat-8 --- Sentinel-2 --- Elias codes --- Simple9 --- Simple16 --- PForDelta --- Rice codes --- hyperspectral scenes --- hyperspectral image --- lossy compression --- real time --- FPGA --- PCA --- JPEG2000 --- EBCOT --- multispectral --- hyperspectral --- CCSDS --- FAPEC --- data compression --- transform --- hyperspectral imaging --- on-board processing --- GPU --- real-time performance --- UAV --- parallel computing --- remote sensing --- image quality --- image classification --- visual quality metrics --- spectral–spatial feature --- multispectral image compression --- partitioned extraction --- group convolution --- rate-distortion --- compressed sensing --- invertible projection --- coupled dictionary --- singular value --- task-driven learning --- on board compression --- transform coding --- learned compression --- neural networks --- variational autoencoder --- complexity --- real-time compression --- on-board compression --- real-time transmission --- UAVs --- compressive sensing --- synthetic aperture sonar --- underwater sonar imaging --- remote sensing data compression --- lossless compression --- compression impact --- computational complexity
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