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 /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Schwenker, Friedhelm (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Scherer, Stefan (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Lecture Notes in Artificial Intelligence ; 11377
• Θέματα: Artificial intelligence.; Optical data processing.; Computer communication systems.; User interfaces (Computer systems).; Artificial Intelligence.; Image Processing and Computer Vision.; Computer Communication Networks.; User Interfaces and Human Computer Interaction.

 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. 

 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.