Integration of bottom-up/top-down approaches for 2D pose estimation using probabilistic Gaussian modelling

Paul Kuo; Dimitrios Makris; Jean Christophe Nebel

doi:10.1016/j.cviu.2010.09.001

Integration of bottom-up/top-down approaches for 2D pose estimation using probabilistic Gaussian modelling

Paul Kuo, Dimitrios Makris, Jean Christophe Nebel

Kingston University

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	242-255
Journal	Computer Vision and Image Understanding
Volume	115
Issue number	2
DOIs	https://doi.org/10.1016/j.cviu.2010.09.001
Publication status	Published - Feb 2011

Bibliographical note

Note: This work was supported by Engineering and Physical Sciences Research Council (EPSRC) sponsored MEDUSA, and PROCESS projects (Grant No. EP/E001025/1 and EP/E033288 respectively)

Keywords

human body pose estimation
stochastic clustering
gaussian mixture modelling
pattern classification
object recognition
confidence measure
ground truth
nonlinear dimensionality reduction
human-body configurations
human motion capture
3d human motion
tracking people
segmentation
framework
parts
Computer science and informatics

Access to Document

10.1016/j.cviu.2010.09.001

http://dx.doi.org/10.1016/j.cviu.2010.09.001

Cite this

@article{5d90bbebe42f405c9c030153d93b11e8,

title = "Integration of bottom-up/top-down approaches for 2D pose estimation using probabilistic Gaussian modelling",

keywords = "human body pose estimation, stochastic clustering, gaussian mixture modelling, pattern classification, object recognition, confidence measure, ground truth, nonlinear dimensionality reduction, human-body configurations, human motion capture, 3d human motion, tracking people, segmentation, framework, parts, Computer science and informatics",

author = "Paul Kuo and Dimitrios Makris and Nebel, \{Jean Christophe\}",

note = "Note: This work was supported by Engineering and Physical Sciences Research Council (EPSRC) sponsored MEDUSA, and PROCESS projects (Grant No. EP/E001025/1 and EP/E033288 respectively)",

year = "2011",

month = feb,

doi = "10.1016/j.cviu.2010.09.001",

language = "English",

volume = "115",

pages = "242--255",

journal = "Computer Vision and Image Understanding",

issn = "1077-3142",

publisher = "Academic Press Inc.",

number = "2",

}

TY - JOUR

T1 - Integration of bottom-up/top-down approaches for 2D pose estimation using probabilistic Gaussian modelling

AU - Kuo, Paul

AU - Makris, Dimitrios

AU - Nebel, Jean Christophe

N1 - Note: This work was supported by Engineering and Physical Sciences Research Council (EPSRC) sponsored MEDUSA, and PROCESS projects (Grant No. EP/E001025/1 and EP/E033288 respectively)

PY - 2011/2

Y1 - 2011/2

KW - human body pose estimation

KW - stochastic clustering

KW - gaussian mixture modelling

KW - pattern classification

KW - object recognition

KW - confidence measure

KW - ground truth

KW - nonlinear dimensionality reduction

KW - human-body configurations

KW - human motion capture

KW - 3d human motion

KW - tracking people

KW - segmentation

KW - framework

KW - parts

KW - Computer science and informatics

U2 - 10.1016/j.cviu.2010.09.001

DO - 10.1016/j.cviu.2010.09.001

M3 - Article

SN - 1077-3142

VL - 115

SP - 242

EP - 255

JO - Computer Vision and Image Understanding

JF - Computer Vision and Image Understanding

IS - 2

ER -