Research And Implementation Of Three-dimensional Reconstruction From Perspective Views

The goal of Computer Vision is to make decisions about all kinds of objects in the world from the sensed images. These images fulfill numerous information about their properties in which the three dimentional structure is the most noteworthy, so Three-Dimensional Reconstruction from Perspective Views is an important and hot point since the birth of computer vision. The research topic has been partly resolved well and brought a new decipline - multiply views geometry - of Computer Vision. There are just four key technologies in respect to this research topic, including camera calibration, feature extraction and matching, motion estimation, and structure computation. Based on the previous research, this thesis has done some work followed from five aspects:(1)The Quantum-Behaved Particle Swarm Optimization has been firstly applied to camera calibration, in chapter 3, in order to improve the accuracy and overcome the drawbacks of traditional optimization algorithm. Firstly, this method uses the traditional linear method to achieve the initial value, and then optimizes the initial value with QPSO. Experimental data shows that camera calibration based on QPSO has less average back-projection error than a pixel and is an effective and reliable method. Experiment also shows that this approach has lower error than the one based on PSO.(2)The thesis points out that if there are four corresponding pairs of points between two planes, there will be existing one non-singular linear transformation, ie homography. This is the famous Mobius theorem in projective geometry. Section 2.5 proves the Mobius theorem from the basic definition of projective geometry and gives the homography calculation method.(3)Motion Estimation is the core issue in Three-Dimensional Reconstruction from Perspective Views, which is to calculate the relative positions among cameras from multiple images taken of one object from different viewpoints. One outstanging method has firstly appeared in Hartley[27][28] for Estimating Motion from Essential Matrix. Chapter 5 does some deep work on this method and brings out a new and easy proof to it.(4)The general systems of Three-Dimensional Reconstruction often use corner as feature points for matching, but the rate of matching is prone to be higher. To improve the accuracy of matching, Chapter 7 constructs one experimental system which uses extreme points in scale space as feature points and SIFT to extract and match these points. The experiment demonstrates the feasibility and value of this method.(5)The fourth chapter addresses one instinctive and simple unified framework which unifies a lot of algorithms, such as SIFT, SURF and HARRI, and directs the reseach for extracting the invariant feature points.