G
Geoff West
Researcher at Curtin University
Publications - 207
Citations - 3749
Geoff West is an academic researcher from Curtin University. The author has contributed to research in topics: Point cloud & Image segmentation. The author has an hindex of 31, co-authored 207 publications receiving 3543 citations. Previous affiliations of Geoff West include Cooperative Research Centre & City University London.
Papers
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Journal ArticleDOI
Policy recognition in the abstract hidden Markov model
TL;DR: This paper introduces the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network, and proposes a novel plan recognition framework based on the AHMM as the plan execution model.
Journal ArticleDOI
Nonparametric segmentation of curves into various representations
Paul L. Rosin,Geoff West +1 more
TL;DR: The operation and performance of an algorithm for segmenting connected points into a combination of representations such as lines, circular, elliptical and superelliptical arcs, and polynomials is described and demonstrated.
Journal ArticleDOI
Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data
TL;DR: Two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data, based on a robust z-score and a Mahalanobis type robust distance are proposed.
Proceedings ArticleDOI
Robust Segmentation in Laser Scanning 3D Point Cloud Data
TL;DR: Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation.
Proceedings ArticleDOI
Recognizing and monitoring high-level behaviors in complex spatial environments
TL;DR: Experimental results showing the ability of the AHMEM system to perform real-time monitoring and recognition of complex behaviors of people from observing their trajectories within a real, complex indoor environment are presented.