Sökning: "Geometric Computer Vision"
Visar resultat 1 - 5 av 36 avhandlingar innehållade orden Geometric Computer Vision.
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. Polynomial Solvers for Geometric Problems - Applications in Computer Vision and Sensor Networks
Sammanfattning : Given images of a scene taken by a moving camera or recordings of a moving smart phone playing a song by a microphone array, how hard is it to reconstruct the scene structure or the moving trajectory of the phone? In this thesis, we study and solve several fundamental geometric problems in order to provide solutions to these problems. The key underlying technique for solving such geometric problems is solving systems of polynomial equations. LÄS MER
3. Higher-Order Regularization in Computer Vision
Sammanfattning : At the core of many computer vision models lies the minimization of an objective function consisting of a sum of functions with few arguments. The order of the objective function is defined as the highest number of arguments of any summand. LÄS MER
4. Geometric Models for Rolling-shutter and Push-broom Sensors
Sammanfattning : Almost all cell-phones and camcorders sold today are equipped with a CMOS (Complementary Metal Oxide Semiconductor) image sensor and there is also a general trend to incorporate CMOS sensors in other types of cameras. The CMOS sensor has many advantages over the more conventional CCD (Charge-Coupled Device) sensor such as lower power consumption, cheaper manufacturing and the potential for onchip processing. LÄS MER
5. Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision
Sammanfattning : This thesis is focused on geometric computer vision problems. The first part of the thesis aims at solving one fundamental problem, namely low-rank matrix factorization. We provide several novel insights into the problem. In brief, we characterize, generate, parametrize and solve the minimal problems associated with low-rank matrix factorization. LÄS MER