Layout consistent segmentation of 3-D meshes via conditional random fields and spatial ordering constraints.

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.

Image-based respiratory motion compensation for fluoroscopic coronary roadmapping.

We present a new image-based respiratory motion compensation method for coronary roadmapping in fluoroscopic images. A temporal analysis scheme is proposed to identify static structures in the image gradient domain. An extended Lucas-Kanade algorithm involving a weighted sum-of-squared-difference (WSSD) measure is proposed to estimate the soft tissue motion in the presence of static structures. A temporally compositional motion model is used to deal with large image motion incurred by deep breathing. Promising results have been shown in the experiments conducted on clinical data.

A deformation tracking approach to 4D coronary artery tree reconstruction.

This paper addresses reconstruction of a temporally deforming 3D coronary vessel tree, i.e., 4D reconstruction from a sequence of angiographic X-ray images acquired by a rotating C-arm. Our algorithm starts from a 3D coronary tree that was reconstructed from images of one cardiac phase. Driven by gradient vector flow (GVF) fields, the method then estimates deformation such that projections of deformed models align with X-ray images of corresponding cardiac phases. To allow robust tracking of the coronary tree, the deformation estimation is regularized by smoothness and cyclic deformation constraints. Extensive qualitative and quantitative tests on clinical data sets suggest that our algorithm reconstructs accurate 4D coronary trees and regularized estimation significantly improves robustness. Our experiments also suggest that a hierarchy of deformation models with increasing complexities are desirable when input data are noisy or when the quality of the 3D model is low.

Stereo matching with linear superposition of layers.

In this paper, we address stereo matching in the presence of a class of non-Lambertian effects, where image formation can be modeled as the additive superposition of layers at different depths. The presence of such effects makes it impossible for traditional stereo vision algorithms to recover depths using direct color matching-based methods. We develop several techniques to estimate both depths and colors of the component layers. Depth hypotheses are enumerated in pairs, one from each layer, in a nested plane sweep. For each pair of depth hypotheses, matching is accomplished using spatial-temporal differencing. We then use graph cut optimization to solve for the depths of both layers. This is followed by an iterative color update algorithm which we proved to be convergent. Our algorithm recovers depth and color estimates for both synthetic and real image sequences.

A computational model for periodic pattern perception based on frieze and wallpaper groups.

We present a computational model for periodic pattern perception based on the mathematical theory of crystallographic groups. In each N-dimensional Euclidean space, a finite number of symmetry groups can characterize the structures of an infinite variety of periodic patterns. In 2D space, there are seven frieze groups describing monochrome patterns that repeat along one direction and 17 wallpaper groups for patterns that repeat along two linearly independent directions to tile the plane. We develop a set of computer algorithms that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs. We also extend this computational model for near-periodic patterns using geometric AIC. Applications of such a computational model include pattern indexing, texture synthesis, image compression, and gait analysis.