Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Complete genome sequences of seven Helicoverpa armigera SNPV-AC53-derived strains

Wild-type baculovirus isolates typically consist of multiple strains. We report the full genome sequences of seven alphabaculovirus strains derived by passage through tissue culture from Helicoverpa armigera SNPV-AC53 (KJ909666).