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210512 ||| eng |
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|a 9783039362288
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|a books978-3-03936-229-5
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|a 9783039362295
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|a Moeslund, Thomas
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|a Statistical Machine Learning for Human Behaviour Analysis
|h Elektronische Ressource
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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|a 1 electronic resource (300 p.)
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|a head pose estimation
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|a false negative rate
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|a body movements
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|a accuracy
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|a speech
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|a Empatica E4
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|a k-means clustering
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|a multi-modal
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|a concept drift
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|a privacy-aware
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|a restricted Boltzmann machine (RBM)
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|a attention behavior
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|a gait event
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|a silhouettes difference
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|a privacy
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|a frequency domain
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|a biometric recognition
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|a singular point detection
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|a deep learning
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|a individual behavior estimation
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|a face segmentation
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|a age classification
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|a neural networks
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|a spatial domain
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|a History of engineering and technology / bicssc
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|a categorical data
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|a attention allocation
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|a 3D convolutional neural networks
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|a ensemble methods
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|a self-reported survey
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|a profoundly deaf
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|a multi-objective evolutionary algorithms
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|a statistical-based time-frequency domain and crowd condition
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|a emotion recognition
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|a speech emotion recognition
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|a spectrograms
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|a fingerprint quality
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|a saliency detection
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|a information entropy
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|a context-aware framework
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|a Learning Using Concave and Convex Kernels
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|a face analysis
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|a hand sign language
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|a toe-off detection
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|a recurrent concepts
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|a committee of classifiers
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|a boundary segmentation
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|a fingerprint image enhancement
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|a gender classification
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|a Kinect sensor
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|a stock price direction prediction
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|a object contour detection
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|a action recognition
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|a gestures
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|a hybrid entropy
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|a blurring detection
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|a interpretable machine learning
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|a noisy image
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|a foggy image
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|a discrete stationary wavelet transform
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|a convolutional neural network
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|a adaptive classifiers
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|a multimodal-based human identification
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|a rule-based classifiers
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|a fibromyalgia
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|a time-of-flight
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|a single pixel single photon image acquisition
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|a Escalera, Sergio
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|a Anbarjafari, Gholamreza
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|a Nasrollahi, Kamal
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|a eng
|2 ISO 639-2
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|b DOAB
|a Directory of Open Access Books
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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|a 10.3390/books978-3-03936-229-5
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|u https://directory.doabooks.org/handle/20.500.12854/68631
|z DOAB: description of the publication
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|u https://www.mdpi.com/books/pdfview/book/2393
|7 0
|x Verlag
|3 Volltext
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|a 400
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|a 900
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|a 000
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|a 576
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|a 140
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|a 600
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|a 620
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|a This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
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