title: Robust pose estimation and recognition using non-gaussian modeling of appearance subspaces.
abstract: We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm.
authors.affiliation: Philips Research Europe, Hamburg, Germany. torbjoern.vik@philips.com
authors.forename: Torbjørn
authors.initials: T
authors.lastname: Vik
authors.forename: Fabrice
authors.initials: F
authors.lastname: Heitz
authors.forename: Pierre
authors.initials: P
authors.lastname: Charbonnier
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity