Model-based recognition of human actions by trajectory matching in phase spaces

Adolfo López-Méndez; Josep R. Casas

doi:10.1016/j.imavis.2012.06.007

Model-based recognition of human actions by trajectory matching in phase spaces

Image and Vision Computing ◽

10.1016/j.imavis.2012.06.007 ◽

2012 ◽

Vol 30 (11) ◽

pp. 808-816 ◽

Cited By ~ 15

Author(s):

Adolfo López-Méndez ◽

Josep R. Casas

Keyword(s):

Human Actions ◽

Model Based ◽

Trajectory Matching

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Rejection of irrelevant human actions in real-time hidden Markov model based recognition systems for wearable computers

Proceedings of the 2nd Conference on Wireless Health - WH '11 ◽

10.1145/2077546.2077555 ◽

2011 ◽

Cited By ~ 3

Author(s):

Jerry Mannil ◽

Mohammad-Mahdi Bidmeshki ◽

Roozbeh Jafari

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Real Time ◽

Hidden Markov ◽

Wearable Computers ◽

Human Actions ◽

Model Based ◽

Recognition Systems

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3D model-based tracking of human actions from multiple image sequences

Systems and Computers in Japan ◽

10.1002/(sici)1520-684x(199807)29:8<48::aid-scj6>3.0.co;2-k ◽

1998 ◽

Vol 29 (8) ◽

pp. 48-56

Author(s):

Akitsugu Sato ◽

Satoshi Kawada ◽

Yoshihiko Osaki ◽

Masanobu Yamamoto

Keyword(s):

3D Model ◽

Image Sequences ◽

Human Actions ◽

Multiple Image ◽

Model Based

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Model-based imitation learning by probabilistic trajectory matching

2013 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2013.6630832 ◽

2013 ◽

Cited By ~ 14

Author(s):

Peter Englert ◽

Alexandros Paraschos ◽

Jan Peters ◽

Marc Peter Deisenroth

Keyword(s):

Imitation Learning ◽

Model Based ◽

Trajectory Matching

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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