Robust Segmentation Models Using an Uncertainty Slice Sampling-Based Annotation Workflow

Slice sampling mixture models

We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45–54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables...

Robust Visual Odometry Using Uncertainty Models

In dense, urban environments, GPS by itself cannot be relied on to provide accurate positioning information. Signal reception issues (e.g. occlusion, multi-path effects) often prevent the GPS receiver from getting a positional lock, causing holes in the absolute positioning data. In order to keep assisting the driver, other sensors are required to track the vehicle motion during these periods o...

Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting

Identifying the underlying models in a set of data points contaminated by noise and outliers, leads to a highly complex multi-model fitting problem. This problem can be posed as a clustering problem by the projection of higher order affinities between data points into a graph, which can then be clustered using spectral clustering. Calculating all possible higher order affinities is computationa...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models

Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional pro...