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» Aggregation-based model reduction of a Hidden Markov Model
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ICMCS
2009
IEEE
115views Multimedia» more  ICMCS 2009»
15 years 1 months ago
A framework to detect and classify activity transitions in low-power applications
Minimizing the number of computations a low-power device makes is important to achieve long battery life. In this paper we present a framework for a low-power device to minimize t...
Jeffrey Boyd, Hari Sundaram
ICA
2012
Springer
13 years 11 months ago
A Non-negative Approach to Language Informed Speech Separation
Abstract. The use of high level information in source separation algorithms can greatly constrain the problem and lead to improved results by limiting the solution space to semanti...
Gautham J. Mysore, Paris Smaragdis
158
Voted
CVIU
2006
222views more  CVIU 2006»
15 years 4 months ago
Conditional models for contextual human motion recognition
We present algorithms for recognizing human motion in monocular video sequences, based on discriminative Conditional Random Field (CRF) and Maximum Entropy Markov Models (MEMM). E...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...
CVPR
1999
IEEE
16 years 6 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
AR
2011
14 years 11 months ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...