@phdthesis{oai:tsukuba.repo.nii.ac.jp:00008707,
 author = {Raytchev, Bisser Roumenov},
 month = {},
 note = {This Thesis proposes a user-independent gesture recognition system, which operates in real-time under real-world conditions, and as such could be useful as a building block in a more sophisticated human-computer interface. In the proposed system, efficient and robust extraction/representation of motion patterns is achieved by utilizing relative-motion dependent primitive features. This bottom-up approach for primitive feature extraction in the early processing stage of the system is combined with multivariate analysis based top-down learning and Dynamic Buffer Structures for online gesture segmentation. The proposed system exhibits good generalization abilities even if trained with only a few samples. In test experiments conducted on several different data sets it was observed that system’s performance is robust to changes in the background and in the illumination conditions, to subjects’ body size and external appearance, able to cope with the non-uniformity in the performance speed of the gestures, etc., all of which are important conditions for a successful application in human-computer interfaces. Gesture recognition in the present system is not restricted to hand gestures only ? larger-scale whole body gestures can be processed simultaneously with relatively smaller-scale hand or head gestures. No manual segmentation of any kind or use of markers, sensors, etc. is necessary. Also, since no domain knowledge is utilized, the system proposed here can be easily adapted and applied to other problems involving motion recognition., 1999, Bibliography: p. 82-87, List of publication: p.88},
 school = {筑波大学, University of Tsukuba},
 title = {Gesture recognition by motion extraction and multivariate analysis},
 year = {2000}
}