Several novel structure from motion algorithms are presented that are designed to more effectively manage the problem of noise. In many practical applications, structure from motion algorithms fail to work properly because of the noise in the optical flow values. Most structure from motion algorithms implicitly assume that the noise is identically distributed and that the noise is white. Both assumptions are false. Some points can be track more easily than others and some points can be tracked more easily in a particular direction. The accuracy of each optical flow value can be quantified using an optical flow probability distribution. By using optical flow probability distributions in place of optical flow estimates in a structure from motion algorithm, a better understanding of the noise is developed and a more accurate solution is obtained. Two different methods of calculating the optical flow probability distributions are presented. The first calculates non-Gaussian probability distributions and the second calculates Gaussian probability distributions. Three different methods for calculating structure from motion are presented that use these probability distributions. The first method works on two frames and can handle any kind of noise. The second method works on two frames and is restricted to only Gaussian noise. The final method works on multiple frames and uses Gaussian noise. A simulation was created to directly compare the performance of methods that use optical flow probability distributions and methods that do not. The simulation results show that those methods which use the probability distributions better estimate the camera motion and the structure of the scene.
College and Department
Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering
BYU ScholarsArchive Citation
Merrell, Paul Clark, "Structure from Motion Using Optical Flow Probability Distributions" (2005). All Theses and Dissertations. 281.
structure from motion, optical flow, computer vision, machine vision, robotic vision, 3D reconstruction, uncertainty model