Statistical Machine Learning for Human Behaviour Analysis
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 hum...
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| Other Authors: | , , , , |
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04847naaaa2201165uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_68631 | ||
| 005 | 20210501 | ||
| 020 | |a books978-3-03936-229-5 | ||
| 020 | |a 9783039362288 | ||
| 020 | |a 9783039362295 | ||
| 024 | 7 | |a 10.3390/books978-3-03936-229-5 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a TBX |2 bicssc | |
| 100 | 1 | |a Moeslund, Thomas |4 edt | |
| 700 | 1 | |a Escalera, Sergio |4 edt | |
| 700 | 1 | |a Anbarjafari, Gholamreza |4 edt | |
| 700 | 1 | |a Nasrollahi, Kamal |4 edt | |
| 700 | 1 | |a Wan, Jun |4 edt | |
| 700 | 1 | |a Moeslund, Thomas |4 oth | |
| 700 | 1 | |a Escalera, Sergio |4 oth | |
| 700 | 1 | |a Anbarjafari, Gholamreza |4 oth | |
| 700 | 1 | |a Nasrollahi, Kamal |4 oth | |
| 700 | 1 | |a Wan, Jun |4 oth | |
| 245 | 1 | 0 | |a Statistical Machine Learning for Human Behaviour Analysis |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (300 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |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. | ||
| 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 multi-objective evolutionary algorithms | ||
| 653 | |a rule-based classifiers | ||
| 653 | |a interpretable machine learning | ||
| 653 | |a categorical data | ||
| 653 | |a hand sign language | ||
| 653 | |a deep learning | ||
| 653 | |a restricted Boltzmann machine (RBM) | ||
| 653 | |a multi-modal | ||
| 653 | |a profoundly deaf | ||
| 653 | |a noisy image | ||
| 653 | |a ensemble methods | ||
| 653 | |a adaptive classifiers | ||
| 653 | |a recurrent concepts | ||
| 653 | |a concept drift | ||
| 653 | |a stock price direction prediction | ||
| 653 | |a toe-off detection | ||
| 653 | |a gait event | ||
| 653 | |a silhouettes difference | ||
| 653 | |a convolutional neural network | ||
| 653 | |a saliency detection | ||
| 653 | |a foggy image | ||
| 653 | |a spatial domain | ||
| 653 | |a frequency domain | ||
| 653 | |a object contour detection | ||
| 653 | |a discrete stationary wavelet transform | ||
| 653 | |a attention allocation | ||
| 653 | |a attention behavior | ||
| 653 | |a hybrid entropy | ||
| 653 | |a information entropy | ||
| 653 | |a single pixel single photon image acquisition | ||
| 653 | |a time-of-flight | ||
| 653 | |a action recognition | ||
| 653 | |a fibromyalgia | ||
| 653 | |a Learning Using Concave and Convex Kernels | ||
| 653 | |a Empatica E4 | ||
| 653 | |a self-reported survey | ||
| 653 | |a speech emotion recognition | ||
| 653 | |a 3D convolutional neural networks | ||
| 653 | |a k-means clustering | ||
| 653 | |a spectrograms | ||
| 653 | |a context-aware framework | ||
| 653 | |a accuracy | ||
| 653 | |a false negative rate | ||
| 653 | |a individual behavior estimation | ||
| 653 | |a statistical-based time-frequency domain and crowd condition | ||
| 653 | |a emotion recognition | ||
| 653 | |a gestures | ||
| 653 | |a body movements | ||
| 653 | |a Kinect sensor | ||
| 653 | |a neural networks | ||
| 653 | |a face analysis | ||
| 653 | |a face segmentation | ||
| 653 | |a head pose estimation | ||
| 653 | |a age classification | ||
| 653 | |a gender classification | ||
| 653 | |a singular point detection | ||
| 653 | |a boundary segmentation | ||
| 653 | |a blurring detection | ||
| 653 | |a fingerprint image enhancement | ||
| 653 | |a fingerprint quality | ||
| 653 | |a speech | ||
| 653 | |a committee of classifiers | ||
| 653 | |a biometric recognition | ||
| 653 | |a multimodal-based human identification | ||
| 653 | |a privacy | ||
| 653 | |a privacy-aware | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2393 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/68631 |7 0 |z DOAB: description of the publication |