Researches On Self-Calibration Techniques Of Camera-projector System And Its Applications

One of the important tasks in computer vision and computer graphics is how to create a vivid virtual world. And camera-projector self-calibration method is the key technique, which is using 2D images of the objects to get 3D information and to build 3D models. Recently, self-calibration method is becoming more and more popular, and has various applications in the field of machine vision, virtual reality, augment reality, 3D reconstruction, image measurement and medical image processing. In practice, to acquire the parameters using traditional method by hand, is time consuming and is not flexible. And the precision always fails to meet the requirements of applications when using the parameters provided by the manufacturers. Therefore, we need a simple and flexible method to calibrate the parameters with precise results. Thus it is clear that camera-projector self-calibration method is a very important part of computer vision, and is the basis for doing other researches in computer vision.The main object of this work is to study the techniques related to self-calibration method, including correspondence between different images, estimation of fundamental matrix, estimation of intrinsic and extrinsic parameters, and optimization of parameters. All these techniques are important parts in self-calibration method. On the basis of these studies, we propose a self-calibration method by converting a space constraint into intrinsic parameter space. And this method is applied to our camera-projector system, to improving the calibration process. The main contribution of this work includes the following parts: First, all related concepts and theories are considered and explained in detail in this work, including mathematical model of camera, multi-view and epipolar geometry. These are the basis for subsequent researches on self-calibration techniques.Second, we study the method for finding correspondence and estimating fundamental matrix. Finding correspondence is the key step in self-calibration method. To getting reliable correspondences between different images, we use the active geometric method based on structured light. Experimental result shows that, high precise correspondences can be found using this method. Meanwhile, we discuss the method for estimating fundamental matrix, and how to increase the robustness with RANSAC algorithm. Related experiments are given at the end of the chapter.Third, techniques of extrinsic parameters estimation and 3D reconstruction are brought to evaluate the position and orientation of camera in space and to build the 3D model. Besides these, Optimization method of bundle adjustment is mentioned here. Estimation method based on essential matrix decomposition is studied and realized. Visualized experimental results are given by building the optimized 3D model with estimated value.Finally, basic theories of self-calibration method based on essential matrix is discussed. And a new method by converting space constraint into intrinsic parameter space is proposed. Compare to the traditional method, we can evaluate the intrinsic and extrinsic parameters with only a few moves of the projector. We substitute this method for the traditional one in our camera-projector system to simplify the calibration process. After the evaluation and optimization, experimental verification and result analysis are given. The results show the effectiveness of our approach.