title: Layout consistent segmentation of 3-D meshes via conditional random fields and spatial ordering constraints.
abstract: We address the problem of 3-D Mesh segmentation for categories of objects with known part structure. Part labels are derived from a semantic interpretation of non-overlapping subsurfaces. Our approach models the label distribution using a Conditional Random Field (CRF) that imposes constraints on the relative spatial arrangement of neighboring labels, thereby ensuring semantic consistency. To this end, each label variable is associated with a rich shape descriptor that is intrinsic to the surface. Randomized decision trees and cross validation are employed for learning the model, which is eventually applied using graph cuts. The method is flexible enough for segmenting even geometrically less structured regions and is robust to local and global shape variations.
authors.affiliation: Siemens Corporate Research, Inc., Princeton, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Zouhar
authors.forename: Sajjad
authors.initials: S
authors.lastname: Baloch
authors.forename: Yanghai
authors.initials: Y
authors.lastname: Tsin
authors.forename: Tong
authors.initials: T
authors.lastname: Fang
authors.forename: Siegfried
authors.initials: S
authors.lastname: Fuchs
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004431:Ear, External / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique