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Dellaert, Frank

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A system for wearable audio navigation integrating advanced localization and auditory display

2009-12-06 , Walker, Bruce N. , Dellaert, Frank

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Bayesian Surprise and Landmark Detection

2009-05 , Ranganathan, Ananth , Dellaert, Frank

Automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. We present the use of Bayesian surprise, introduced in computer vision, for landmark detection. Further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection. Our scheme is agnostic to the sensor type, and we demonstrate this by implementing a simple laser model for computing surprise. We evaluate our landmark detector using appearance and laser measurements in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.

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Binding Balls: Fast Detection of Binding Sites Using a Property of Spherical Fourier Transform

2009 , Comin, Matteo , Guerra, Concettina , Dellaert, Frank

The functional prediction of proteins is one of the most challenging problems in modern biology. An established computational technique involves the identification of threedimensional local similarities in proteins. In this article, we present a novel method to quickly identify promising binding sites. Our aim is to efficiently detect putative binding sites without explicitly aligning them. Using the theory of Spherical Harmonics, a candidate binding site is modeled as a Binding Ball. The Binding Ball signature, offered by the Spherical Fourier coefficients, can be efficiently used for a fast detection of putative regions. Our contribution includes the Binding Ball modeling and the definition of a scoring function that does not require aligning candidate regions. Our scoring function can be computed efficiently using a property of Spherical Fourier transform (SFT) that avoids the evaluation of all alignments. Experiments on different ligands show good discrimination power when searching for known binding sites. Moreover, we prove that this method can save up to 40% in time compared with traditional approaches.

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