@inproceedings{endres_segmentation_2011,
	address = {Berlin, Heidelberg},
	title = {Segmentation of {Action} {Streams} {Human} {Observers} vs. {Bayesian} {Binning}},
	isbn = {978-3-642-24455-1},
	doi = {10.1007/978-3-642-24455-1_7},
	abstract = {Natural body movements are temporal sequences of individual actions. In order to realise a visual analysis of these actions, the human visual system must accomplish a temporal segmentation of action sequences. We attempt to reproduce human temporal segmentations with Bayesian binning (BB) [8]. Such a reproduction would not only help our understanding of human visual processing, but would also have numerous potential applications in computer vision and animation. BB has the advantage that the observation model can be easily exchanged. Moreover, being an exact Bayesian method, BB allows for the automatic determination of the number and positions of segmentation points. We report our experiments with polynomial (in time) observation models on joint angle data obtained by motion capture. To obtain human segmentation points, we generated videos by animating sequences from the motion capture data. Human segmentation was then assessed by an interactive adjustment paradigm, where participants had to indicate segmentation points by selection of the relevant frames. We find that observation models with polynomial order ≥ 3 can match human segmentations closely.},
	language = {en},
	booktitle = {{KI} 2011: {Advances} in {Artificial} {Intelligence}},
	publisher = {Springer},
	author = {Endres, Dominik and Christensen, Andrea and Omlor, Lars and Giese, Martin A.},
	editor = {Bach, Joscha and Edelkamp, Stefan},
	year = {2011},
	keywords = {Elbow Angle, Joint Angle, Motion Capture Data, Observation Model, Segmentation Point},
	pages = {75--86},
}