Random hypothesis generation is central to robust geometric model fitting in computer
vision. The predominant technique is to randomly sample minimal subsets of the data, and …

We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of
geometric structures in vision data. Our objective function enforces both the fidelity of a …

Data correspondence/grouping under an unknown parametric model is a fundamental topic
in computer vision. Finding feature correspondences between two images is probably the …

Branch-and-bound methods are used in various data analysis problems, such as clustering,
seriation and feature selection. Classical approaches of branch-and-bound based clustering …

Three-dimensional reconstruction of line and plane structures from two images is a major
task in urban building modeling. However, traditional line segment (LS) matching methods …

Our goal is to fit the multiple instances (or structures) of a generic model existing in data.
Here we propose a novel model selection scheme to estimate the number of genuine …

An efficient and robust framework is proposed for two-view multiple structure-and-motion
segmentation of unknown number of rigid objects. The segmentation problem has three …

Multi-structure model fitting has traditionally taken a two-stage approach: First, sample a
(large) number of model hypotheses, then select the subset of hypotheses that optimise a …

Segmentation problems are one of the most important areas of research in computer vision.
While segmentation problems are generally solved with clustering paradigms, they …

In modeling large-scale urban scenes, extracting reliable dominant planes from initial 3D
points plays an important role for inferring the complete scene structures. However …