Entropy in Image Analysis II
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...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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245 | 1 | 0 | |a Entropy in Image Analysis II |
260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
300 | |a 1 electronic resource (394 p.) | ||
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338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a 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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a image binarization | ||
653 | |a optical character recognition | ||
653 | |a local entropy filter | ||
653 | |a thresholding | ||
653 | |a image preprocessing | ||
653 | |a image entropy | ||
653 | |a image encryption | ||
653 | |a medical color images | ||
653 | |a RGB | ||
653 | |a chaotic system | ||
653 | |a crowd behavior analysis | ||
653 | |a salient crowd motion detection | ||
653 | |a repulsive force | ||
653 | |a direction entropy | ||
653 | |a node strength | ||
653 | |a Pompe disease | ||
653 | |a children | ||
653 | |a quantitative muscle ultrasound | ||
653 | |a texture-feature parametric imaging | ||
653 | |a compound chaotic system | ||
653 | |a S-box | ||
653 | |a image information entropy | ||
653 | |a image chaotic encryption | ||
653 | |a cryptography | ||
653 | |a Latin cube | ||
653 | |a bit cube | ||
653 | |a chosen plaintext attack | ||
653 | |a atmosphere background | ||
653 | |a engine flame | ||
653 | |a infrared radiation | ||
653 | |a detectability | ||
653 | |a image quality evaluation | ||
653 | |a image retrieval | ||
653 | |a pooling method | ||
653 | |a convolutional neural network | ||
653 | |a feature distribution entropy | ||
653 | |a lossless compression | ||
653 | |a pattern classification | ||
653 | |a machine learning | ||
653 | |a malaria infection | ||
653 | |a entropy | ||
653 | |a Golomb-Rice codes | ||
653 | |a image processing | ||
653 | |a image segmentation | ||
653 | |a weld segmentation | ||
653 | |a weld evaluation | ||
653 | |a convolution neural network | ||
653 | |a Python | ||
653 | |a Keras | ||
653 | |a RSNNS | ||
653 | |a MXNet | ||
653 | |a brain-computer interface (BCI) | ||
653 | |a electroencephalography (EEG) | ||
653 | |a motor imagery (MI) | ||
653 | |a continuous wavelet transform (CWT) | ||
653 | |a convolutional neural network (CNN) | ||
653 | |a hyperchaotic system | ||
653 | |a filtering | ||
653 | |a DNA computing | ||
653 | |a diffusion | ||
653 | |a deep neural network | ||
653 | |a data expansion | ||
653 | |a blind image quality assessment | ||
653 | |a saliency and distortion | ||
653 | |a human visual system | ||
653 | |a declining quality | ||
653 | |a data hiding | ||
653 | |a AMBTC | ||
653 | |a steganography | ||
653 | |a stego image | ||
653 | |a dictionary-based coding | ||
653 | |a pixel value adjusting | ||
653 | |a neuroaesthetics | ||
653 | |a symmetry | ||
653 | |a balance | ||
653 | |a complexity | ||
653 | |a chiaroscuro | ||
653 | |a normalized entropy | ||
653 | |a renaissance | ||
653 | |a portrait paintings | ||
653 | |a art history | ||
653 | |a art statistics | ||
653 | |a chaotic systems | ||
653 | |a DNA coding | ||
653 | |a security analysis | ||
653 | |a magnetic resonance images | ||
653 | |a non-maximum suppression | ||
653 | |a object detection | ||
653 | |a key-point detection | ||
653 | |a IoU | ||
653 | |a feature fusion | ||
653 | |a quasi-resonant Rossby/drift wave triads | ||
653 | |a Mordell elliptic curve | ||
653 | |a pseudo-random numbers | ||
653 | |a substitution box | ||
653 | |a nuclear spin generator | ||
653 | |a medical image | ||
653 | |a peak signal-to-noise ratio | ||
653 | |a key space calculation | ||
653 | |a Duchenne muscular dystrophy | ||
653 | |a ultrasound | ||
653 | |a backscattered signals | ||
653 | |a medical imaging | ||
653 | |a neural engineering | ||
653 | |a computer vision | ||
653 | |a crowd motion detection | ||
653 | |a security | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2933 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/69161 |7 0 |z DOAB: description of the publication |