In this paper, the classical RANSAC approach is considered for robust matching to remove mismatches (outliers) in a list of putative correspondences. We will examine the justification for using the minimal size of sample set in a RANSAC trial and propose that the size of the sample set should be varied dynamically depending on the noise and data set. Using larger sample set will not increase th...

for Computer Vision Hanzi Wang and David Suter, Senior Member, IEEE Department of Electrical and Computer Systems Engineering Monash University, Clayton Vic. 3800, Australia. {hanzi.wang ; d.suter}@eng.monash.edu.au Abstract Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator fo...

RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solutio...

ii This thesis represents my own work in accordance with University regulations.

We propose a solution to the problem of robust subspace estimation using the projection based M-estimator. The new method handles more outliers than inliers, does not require a user defined scale of the noise affecting the inliers, handles noncentered data and nonorthogonal subspaces. Other robust methods like RANSAC, use an input for the scale, while methods for subspace segmentation, like GPC...

The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. T...

Autonomous vehicles need a means of detecting obstructions on its path, to avoid collision. In this paper, a novel approach to obstacle detection is presented. A camera moves on a visible ground plane with the optical axis parallel to the ground. Camera motion parameters are linearly related to rst order spatio-temporal derivatives of the taken image sequence; image ow is not needed. Motion is ...

RANSAC (random sample consensus) is a robust algorithm for model fitting and outliers’ removal, however, it is neither efficient nor reliable enough to meet the requirement of many applications where time and precision is critical. Various algorithms have been developed to improve its performance for model fitting. A new algorithm named PURSAC (purposive sample consensus) is introduced in this ...

The problem considered in this paper is that of estimating the projective transformation between two images in situations where the image motion is large and featurematching is not aided by a proximity heuristic. The overall algorithm designed is based on a multiresolution, multihypothesis scheme, and similarities between tracking and matching through multiple resolution levels are exploited. T...