Learn More
We present an approach to track human subjects using an articulated human framework. First, we describe the articulated hierarchical human model. Second, we develop a stochastic hierarchical, partitioned, particle filter based on the natural structure and limb dependency of the human body. We apply this to track human subjects in video sequences using(More)
Large-scale volumetric biomedical image data of three or more dimensions are a significant challenge for distributed browsing and visualisation. Many images now exceed 10GB which for most users is too large to handle in terms of computer RAM and network bandwidth. This is aggravated when users need to access tens or hundreds of such images from an archive.(More)
This paper describes the use of variable kernels based on the normalized Chamfer distance transform (NCDT) for mean shift, object tracking in colour video sequences. This replaces the more usual Epanechnikov kernel, improving target representation and localization without increasing the processing time, minimising the distance between successive frame RGB(More)
We present a novel method to provide fast access to large 3D volumetric data sets from biological or medical imaging atlases. We extend the Internet Imaging Protocol with an open specification for requesting tiled sections of 3D objects. We evaluate the performance of the protocol and demonstrate it with a platform independent web viewer that allows(More)
We present an approach to model articulated human movements and to analyse their behavioural semantics. First, we describe a novel dynamic and behavioural model that uses movements, a sequence of consecutive poses, from motion captured video data to establish priors for both tracking and behavioural analysis. Second, using that model, we show how we can(More)
  • 1