Spatially Coherent RANSAC for Multi-Model Fitting

RANSAC [15, 38, 1] is a reliable method for fitting parametric models to sparse data with many outliers. Originally designed for extracting a single model, RANSAC also has variants for fitting multiple models when supported by data. Our main insight is that, in practice, inliers for each model are often spatially coherent — all previous RANSAC-based methods ignore this. Our new method fits an unspecified number of models to data by combining ideas of random sampling and spatial regularization. As in basic RANSAC, we randomly sample data points to generate a set of proposed models (labels). We formulate model selection and inlier classification as a single problem — labeling of triangulated data points. Geometric fit errors and spatial coherence are combined in one MRF-based energy. In contrast to basic RANSAC, inlier classification does not depend on a fixed threshold. Moreover, our optimization framework allows iterative re-estimation of models/inliers with a clear stopping criteria and convergence guarantees. We show that our new method, SCO- RANSAC, can significantly improve results on synthetic and real data supporting multiple linear, affine, and homographic models

Efficient particle filtering using RANSAC with application to 3D face tracking

Particle filtering is a very popular technique for sequential state estimation. However, in high-dimensional cases where the state dynamics are complex or poorly modeled, thousands of particles are usually required for real applications. This paper presents a hybrid sampling solution that combines RANSAC and particle filtering. In this approach, RANSAC provides proposal particles that, with high probability, represent the observation likelihood. Both conditionally independent RANSAC sampling and boosting-like conditionally dependent RANSAC sampling are explored. We show that the use of RANSAC-guided sampling reduces the necessary number of particles to dozens for a full 3D tracking problem. This is method is particularly advantageous when state dynamics are poorly modeled. We show empirically that the sampling efficiency (in terms of likelihood) is much higher with the use of RANSAC. The algorithm has been applied to the problem of 3D face pose tracking with changing expression. We demonstrate the validity of our approach with several video sequences acquired in an unstructured environment

Visual motion estimation and tracking of rigid bodies by physical simulation

This thesis applies knowledge of the physical dynamics of objects to estimating object motion from vision when estimation from vision alone fails. It differentiates itself from existing physics-based vision by building in robustness to situations where existing visual estimation tends to fail: fast motion, blur, glare, distractors, and partial or full occlusion. A real-time physics simulator is incorporated into a stochastic framework by adding several different models of how noise is injected into the dynamics. Several different algorithms are proposed and experimentally validated on two problems: motion estimation and object tracking. The performance of visual motion estimation from colour histograms of a ball moving in two dimensions is improved considerably when a physics simulator is integrated into a MAP procedure involving non-linear optimisation and RANSAC-like methods. Process noise or initial condition noise in conjunction with a physics-based dynamics results in improved robustness on hard visual problems. A particle filter applied to the task of full 6D visual tracking of the pose an object being pushed by a robot in a table-top environment is improved on difficult visual problems by incorporating a simulator as a dynamics model and injecting noise as forces into the simulator.EThOS - Electronic Theses Online ServiceGBUnited Kingdo