Shaogang Gong

A Multi-View Nonlinear Active Shape Model Using Kernel PCA.

Recovering the shape of any 3D object using multiple 2D views requires establishing correspondence between feature points at different views. However changes in viewpoint introduce self-occlusions, resulting nonlinear variations in the shape and inconsistent 2D features between views. Here we introduce a multi-view nonlinear shape model utilising 2D view-dependent constraint without explicit reference to 3D structures. For nonlinear model transformation, we adopt Kernel PCA based on Support Vector Machines.

Stochastic temporal models of human activities

Human activities are characterised by the spatio-temporal structure of their motion pattern. Such structures are probabilistic and often rather ambiguous. Modelling such spatio-temporal structures as static templates can be very sensitive to noise and cannot capture variations in observation measurements caused by different subjects performing the same act. In this paper we introduce the concept of modelling temporal structures by statistical dynamic systems using first-order Markov process descriptions. Prior knowledge is learned from training sequences and recognition is performed through continuous propagation of density distributions. Taking current observations into account to temporarily augment the learned prior leads to more accurate recognition with less computational costs.