A Global Optimization Approach to Robust Multi-Model Fitting

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 model to the data and the similarity between its associated inliers. Departing from most previous optimizationbased approaches, the outcome of our method is a ranking of a given set of putative models, instead of a pre-specified number of “good” candidates (or an attempt to decide the right number of models). This is particularly useful when the number of structures in the data is a priori unascertainable due to unknown intent and purposes. Another key advantage of our approach is that it operates in a unified optimization framework, and the standard QP form of our problem formulation permits globally convergent optimization techniques. We tested our method on several geometric multi-model fitting problems on both synthetic and real data. Experiments show that our method consistently achieves state-...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Jin Yu, Tat-Jun Chin, David Suter
• Keywords: Computer Vision, Convergent Optimization Techniques, CVPR 2011, Multi-model fitting Problems, Standard Qp Form