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Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction: 5th IAPR TC 9 Workshop, MPRSS 2018, Beijing, China, August 20, 2018, Revised Selected Papers de Friedhelm Schwenker

Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction: 5th IAPR TC 9 Workshop, MPRSS 2018, Beijing, China, August 20, 2018, Revised Selected Papers: Lecture Notes in Computer Science, cartea 11377

Editat de Friedhelm Schwenker, Stefan Scherer
en Limba Engleză Paperback – 15 mai 2019
This book constitutes the refereed post-workshop proceedings of the 5th IAPR TC9 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2018, held in Beijing, China, in August 2018. 
The 10 revised papers presented in this book focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition and pain intensity estimation, especially the question how to distinguish between human emotions from pain or stress induced by pain is discussed.
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Specificații

ISBN-13: 9783030209834
ISBN-10: 3030209830
Pagini: 119
Ilustrații: VII, 117 p. 117 illus., 32 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.19 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Multi-Focus Image Fusion with PCA Filters of PCANet.- An Image Captioning Method for Infant Sleeping Environment Diagnosis.- A First-Person Vision Dataset of Office Activities.- Perceptual Judgments to Detect Computer Generated Forged Faces in Social Media.- Combining Deep and Hand-crafted Features for Audio-based Pain Intensity Classification.- Deep Learning Algorithms for Emotion Recognition on Low Power Single Board Computers.- Improving Audio-Visual Speech Recognition Using Gabor Recurrent Neural Networks.- Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity.- Visualizing Facial Expression Features of Pain and Emotion Data.

Caracteristici

Includes supplementary material: sn.pub/extras