@article {17809,
	title = {Temporal probabilistic object bases},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {15},
	year = {2003},
	month = {2003/08//july},
	pages = {921 - 939},
	abstract = {There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.},
	keywords = {algebra;, algebraic, bases;, constraints;, data, database, database;, databases;, distribution, explicit, functions;, handling;, implicit, instances;, integrity;, intervals;, management;, model;, models;, object, object-oriented, operations;, probabilistic, probability, probability;, relational, temporal, theory;, Uncertainty, uncertainty;},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2003.1209009},
	author = {Biazzo,V. and Giugno,R. and Lukasiewicz,T. and V.S. Subrahmanian}
}
@conference {14839,
	title = {Error propagation in full 3D-from-2D object recognition},
	booktitle = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR {\textquoteright}94., 1994 IEEE Computer Society Conference on},
	year = {1994},
	month = {1994/06//},
	pages = {892 - 898},
	abstract = {Robust recognition systems require a careful understanding of the effects of error in sensed features. Error in these image features results in uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty when model poses are based on matching three image and model points. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute this uncertainty when poses are based on any number of initial matches},
	keywords = {3D-from-2D, algorithm;, error, extraction;, feature, features;, handling;, image, initial, linear, matches;, object, Programming, programming;, propagation;, recognition, recognition;, robust, sequences;, systems;, Uncertainty, uncertainty;},
	doi = {10.1109/CVPR.1994.323920},
	author = {Alter,T. D. and Jacobs, David W.}
}
