Sökning: "Convex relaxations"
Visar resultat 1 - 5 av 11 avhandlingar innehållade orden Convex relaxations.
1. Computational Methods for Computer Vision : Minimal Solvers and Convex Relaxations
Sammanfattning : Robust fitting of geometric models is a core problem in computer vision. The most common approach is to use a hypothesize-and-test framework, such as RANSAC. In these frameworks the model is estimated from as few measurements as possible, which minimizes the risk of selecting corrupted measurements. LÄS MER
2. Rank Reduction with Convex Constraints
Sammanfattning : This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as finite dimensional optimization problems that are convex, apart from a low-rank constraint. LÄS MER
3. Robust Estimation of Motion Parameters and Scene Geometry : Minimal Solvers and Convexification of Regularisers for Low-Rank Approximation
Sammanfattning : In the dawning age of autonomous driving, accurate and robust tracking of vehicles is a quintessential part. This is inextricably linked with the problem of Simultaneous Localisation and Mapping (SLAM), in which one tries to determine the position of a vehicle relative to its surroundings without prior knowledge of them. LÄS MER
4. Convex Optimization for Assignment and Generalized Linear Regression Problems
Sammanfattning : This thesis considers optimization techniques with applications in assignment and generalized linear regression problems. The first part of the thesis investigates the worst-case robust counterparts of combinatorial optimization problems with least squares (LS) cost functions, where the uncertainty lies on the linear transformation of the design variables. LÄS MER
5. Sparse Modeling Heuristics for Parameter Estimation - Applications in Statistical Signal Processing
Sammanfattning : This thesis examines sparse statistical modeling on a range of applications in audio modeling, audio localizations, DNA sequencing, and spectroscopy. In the examined cases, the resulting estimation problems are computationally cumbersome, both as one often suffers from a lack of model order knowledge for this form of problems, but also due to the high dimensionality of the parameter spaces, which typically also yield optimization problems with numerous local minima. LÄS MER