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210512 ||| eng |
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|a 9783039431601
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|a 9783039431618
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|a books978-3-03943-161-8
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|a Sparavigna, Amelia Carolina
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|a Entropy in Image Analysis II
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (394 p.)
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|a machine learning
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|a DNA coding
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|a quantitative muscle ultrasound
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|a infrared radiation
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|a filtering
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|a texture-feature parametric imaging
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|a feature fusion
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|a image preprocessing
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|a chaotic systems
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|a diffusion
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|a chiaroscuro
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|a medical color images
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|a neural engineering
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|a complexity
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|a dictionary-based coding
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|a image information entropy
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|a compound chaotic system
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|a data expansion
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|a declining quality
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|a symmetry
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|a MXNet
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|a art history
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|a image encryption
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|a balance
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|a image binarization
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|a DNA computing
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|a convolution neural network
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|a renaissance
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|a substitution box
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|a malaria infection
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|a security
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|a feature distribution entropy
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|a Mordell elliptic curve
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|a chaotic system
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|a RGB
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|a peak signal-to-noise ratio
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|a computer vision
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|a detectability
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|a pseudo-random numbers
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|a blind image quality assessment
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|a saliency and distortion
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|a electroencephalography (EEG)
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|a IoU
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|a steganography
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|a image entropy
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|a hyperchaotic system
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|a image chaotic encryption
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|a security analysis
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|a key space calculation
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| 653 |
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|a brain-computer interface (BCI)
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|a backscattered signals
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|a bit cube
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|a crowd behavior analysis
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|a Pompe disease
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|a key-point detection
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|a image processing
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|a cryptography
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|a magnetic resonance images
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|a ultrasound
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|a chosen plaintext attack
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|a stego image
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|a portrait paintings
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|a normalized entropy
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| 653 |
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|a History of engineering and technology / bicssc
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|a deep neural network
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|a pixel value adjusting
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|a nuclear spin generator
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|a node strength
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|a data hiding
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|a salient crowd motion detection
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|a non-maximum suppression
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|a Duchenne muscular dystrophy
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| 653 |
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|a children
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|a crowd motion detection
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|a image retrieval
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|a motor imagery (MI)
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|a medical image
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|a object detection
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|a continuous wavelet transform (CWT)
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|a thresholding
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|a pattern classification
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|a quasi-resonant Rossby/drift wave triads
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|a medical imaging
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|a Golomb-Rice codes
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|a AMBTC
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|a weld evaluation
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|a direction entropy
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|a pooling method
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|a engine flame
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|a art statistics
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| 653 |
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|a local entropy filter
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| 653 |
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|a atmosphere background
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| 653 |
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|a image quality evaluation
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| 653 |
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|a Latin cube
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| 653 |
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|a entropy
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| 653 |
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|a Keras
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| 653 |
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|a convolutional neural network (CNN)
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| 653 |
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|a S-box
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| 653 |
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|a RSNNS
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| 653 |
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|a repulsive force
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| 653 |
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|a weld segmentation
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| 653 |
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|a optical character recognition
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| 653 |
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|a convolutional neural network
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| 653 |
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|a Python
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| 653 |
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|a human visual system
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| 653 |
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|a neuroaesthetics
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| 653 |
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|a lossless compression
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| 653 |
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|a image segmentation
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| 700 |
1 |
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|a Sparavigna, Amelia Carolina
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-03943-161-8
|
| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/2933
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/69161
|z DOAB: description of the publication
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|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.
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