39 research outputs found
Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model
Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand’s interaction with an active site of a protein-enzyme. However, currently, this interaction is widely estimated using some statistical properties of known ligand-protein complex properties. Such statistical properties are described by quantitative structure-activity relationships (QSAR). Bayesian networks can help us to evaluate stability of a ligand-protein complex using found statistics. Moreover, we are possible to prove optimality of Naive Bayes model that makes these evaluations simple and easy for practical realization. We prove here optimality of Naive Bayes model using as an illustration ligand-protein interaction
Vision-Based Navigation III: Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map
An algorithm for pose and motion estimation using corresponding features in
omnidirectional images and a digital terrain map is proposed. In previous
paper, such algorithm for regular camera was considered. Using a Digital
Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables
recovering the absolute position and orientation of the camera. In order to do
this, the DTM is used to formulate a constraint between corresponding features
in two consecutive frames. In this paper, these constraints are extended to
handle non-central projection, as is the case with many omnidirectional
systems. The utilization of omnidirectional data is shown to improve the
robustness and accuracy of the navigation algorithm. The feasibility of this
algorithm is established through lab experimentation with two kinds of
omnidirectional acquisition systems. The first one is polydioptric cameras
while the second is catadioptric camera.Comment: 6 pages, 9 figure