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» PAC-Learning of Markov Models with Hidden State
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ICMCS
2006
IEEE
197views Multimedia» more  ICMCS 2006»
14 years 23 days ago
Identify Sports Video Shots with "Happy" or "Sad" Emotions
Semantic video content extraction and selection are critical steps in sports video analysis and editing. The identification of video segments can be from various semantic perspec...
Jinjun Wang, Engsiong Chng, Changsheng Xu, Hanqing...
HAPTICS
2002
IEEE
13 years 11 months ago
Building a Task Language for Segmentation and Recognition of User Input to Cooperative Manipulation Systems
We present the results of using Hidden Markov Models (HMMs) for automatic segmentation and recognition of user motions. Previous work on recognition of user intent with man/machin...
C. Sean Hundtofte, Gregory D. Hager, Allison M. Ok...
ICASSP
2011
IEEE
12 years 10 months ago
Improving acoustic event detection using generalizable visual features and multi-modality modeling
Acoustic event detection (AED) aims to identify both timestamps and types of multiple events and has been found to be very challenging. The cues for these events often times exist...
Po-Sen Huang, Xiaodan Zhuang, Mark Hasegawa-Johnso...
CORR
2004
Springer
195views Education» more  CORR 2004»
13 years 6 months ago
Detecting User Engagement in Everyday Conversations
This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin...
Chen Yu, Paul M. Aoki, Allison Woodruff
PERCOM
2007
ACM
14 years 6 months ago
Sensor Scheduling for Optimal Observability Using Estimation Entropy
We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process ...
Mohammad Rezaeian