@inproceedings{oberhoff_gates_2011,
	address = {Berlin, Heidelberg},
	title = {Gates for {Handling} {Occlusion} in {Bayesian} {Models} of {Images}: {An} {Initial} {Study}},
	isbn = {978-3-642-24455-1},
	shorttitle = {Gates for {Handling} {Occlusion} in {Bayesian} {Models} of {Images}},
	doi = {10.1007/978-3-642-24455-1_21},
	abstract = {Probabilistic systems for image analysis have enjoyed increasing popularity within the last few decades, yet principled approaches to incorporating occlusion as a feature into such systems are still few [11,10,7]. We present an approach which is strongly influenced by the work on noisy-or generative factor models (see e.g. [3]). We show how the intractability of the hidden variable posterior of noisy-or models can be (conditionally) lifted by introducing gates on the input combined with a sparsifying prior, allowing for the application of standard inference procedures. We demonstrate the feasibility of our approach on a computer vision toy problem.},
	language = {en},
	booktitle = {{KI} 2011: {Advances} in {Artificial} {Intelligence}},
	publisher = {Springer},
	author = {Oberhoff, Daniel and Endres, Dominik and Giese, Martin A. and Kolesnik, Marina},
	editor = {Bach, Joscha and Edelkamp, Stefan},
	year = {2011},
	keywords = {Bayesian Model, Dirichlet Process, Input Pixel, Latent Variable, Mixture Model},
	pages = {228--232},
}