Analysis and Description of the Semantic Content of Cell Biological Videos

摘要: We present a system for extracting and organizing semantic content metadata from biological microscopy videos featuring living cells. The uses image processing understanding procedures to identify objects events automatically within the digitized videos, thus leading generation of information (semantic metadata) high value by automated analysis their visual content. When such are properly organized in searchable database conformed with our proposed model, content-based video query retrieval may be developed locate particular objects, or behaviours. Furthermore these can applied find hidden relationships as correlations between behavioural alterations changes environmental conditions suitability functionality organizational model is demonstrated five different types experiments, recording epithelial wound healing, bacterial multiplication, rotations tethered bacteria, swimming motile bacteria human sperm. have named prototype analytical VAMPORT (video production object recognition tracking). conclude that use an audiovisual description standard MPEG7 will contribute valuably towards interoperability system.