Registration Of2D Light Intensity And Range Images And Its Applications

Image registration is a basic problem in image processing, which finds it's applications invarious fields like robot localization, remote sensing, medical image analysis, image mosaicing,target recognition and localization, quality control, navigation and guidance. Despite comprehensiveresearch spanning over thirty years, to register images precisely and to register images in thepresence of large deformations are still important research topics.In this dissertation, image registration methods which require high precision and to registerimages in the presence of large deformations are studied. The involved images include twodimensional light intensity images and range images. The accomplished works can be summarizedas follows:1. A shape-based template matching method with high precision is investigated. Subpixeledge detecting algorithm based on facet model is adopted to locate the feature points in shape modeland ICP algorithm is used to refine matching result. Comparisons with Halcon's method showingthat the proposed method has comparable precision.2. Application of the shape-based template matching method for circle detection in highlyvariable environment is investigated. A robust method for circle detection is proposed. To begin with,the shape-based template matching method is adopted to locate the circle roughly. Then, an annulararea containing the circle's contour can be defined with the rough location obtained by the matchingmethod and the size of the circle given in advance. So we can determine the double-threshold forCanny edge detector in the local annular area which is easy. To get subpixel location for Canny edgepoints, a facet model based method is used. Furthermore, to kick out the outliers, a de-noisingalgorithm based on gradient direction is developed. Finally, RANSAC algorithm is used to estimatethe circle parameters. Experimental results demonstrate that the method proposed outperformHalcon's which is based on Hough transform.3. Image registration based on log-polar transform and feature points is explored. Aiming atthe problems of Zokai's method, a registration method based on log-polar transform and Harris corner is proposed. Correlation between log-polar transforms of two circular windows whose centersare two Harris corners is calculated by NCC (Normalized Cross Correlation), which is considered asthe similarity of the two corners. But NCC is computationally expensive. On the one hand, onedimensional projection is utilized to speed up the process of NCC. On the other hand, sum table isused to optimize the calculation of the denominator of NCC and Jensen inequality is used toconstruct a termination condition of NCC. Experimental results show that the proposed methodoutperform David Lowe's which is based on SIFT (Scale Invariance Feature Transform) features. Toregister images in the presence of larger deformations, Harris-Affine features are extracted from theimages. Harris-Affine features' elliptical regions are normalized to circular regions, and thenlog-polar transform and NCC are used to calculate the similarity of the two features. Experimentalresults show that the method obtained much more correct match pairs than Mikolajczyk's method.4. Registration method of2D range images and its application in pose estimation for mobilerobot is studied. To overcome the problem of local extrema existing in iterative closest point (ICP)algorithm when severe occlusions occur, the closest point (CP) rule is modified and dual closestpoint (DCP) rule is proposed. DCP rule contains twice CP correspondences so that computationcomplexity is doubled. To decrease the computation complexity, iterative dual closest point based onclustering (IDCP BoC) is proposed. Scan range points are divided into clusters and then a procedureof reducing the number of points is conducted. The reduced data set is used for iterative computationbefore the error of two consecutive iterations' residual errors less than a preset threshold to speed upthe algorithm after that the data set which is not reduced is used to guarantee the accuracy.Experimental results show that IDCP BoC can avoid the problem of local extrema effectively.