Straight Line Correspondences Research Articles

A New Straight Line Matching Technique by Integration of Vision-based Image Processing

In this paper, a new algorithm for a new straight line matching by integration of vision based image processing is being proposed. The corner detector and Hough Line Transform were employed respectively the feature extraction of corners and edges from the left and right images. After process of corner detection has been already extracted the corner points in the left and right images, each corner use the computation of a cross-correlation which is based on matching score for putative corner correspondences. This algorithm uses the corner pairs from reference points of started-end corner. The matching comparison between corner pairs and Hough Line are able to generate a new straight on the edge of both left and right images. From corner correspondences in the image pairs are going to compare with a new straight line to get the result of new straight line correspondences. This method is simple and easy to apply to the 3D recognition of geometric shapes and is able to reduce the number of false matches in a probabilistic manner which the result arise from incomplete Hough Lines. The results have shown the new straight line correspondences that are effective, complete and could be used in a 3D recognition process.

Matching of straight line segments from aerial stereo images of urban areas

Reliable extraction of corresponding straight lines in overlapping images can be used for different purposes such as 3D object extraction, image registration, automated triangulation, etc. In this study, a new approach for the matching of straight line features from stereo aerial images is presented. Initial correspondences between stereo images are generated using a pair-wise stereo matching approach, which involves a total of seven relational constraints. The final straight line correspondences between the stereo images are established in a line-to-line matching stage. The optimal settings for the parameters guiding the matching phase are determined after analysing the probability density functions (PDFs). The proposed approach is tested on 30 image patches of two different urban areas, and as a result, very successful and promising stereo line matching performances are achieved. Besides, the comparison of the results of the proposed approach with the results of one of the state-of-the-art stereo matching approaches proves the superiority and potential of the proposed approach.

Complex correspondences in straight line stereo matching

Partial occlusion and fragmented lines will result in the various cases of straight line correspondences, such as one-to-one, one-to-many or many-to-many ones. However, the complex correspondences, such as one-to-many and many-to-many ones, are usually ignored or cannot be established completely in the existing methods. Here, the essence of the complex correspondences will be analyzed. Based on the two characteristics of a straight line, which are introduced by regarding a straight line as a set of collinear points, the compatibility between the complex correspondences and the uniqueness constraint of point correspondence is proved and a new uniqueness constraint of correspondence for matching lines is proposed. Based on the analysis of the complex correspondences, a new concept of line feature group is defined to describe a set of integral correspondences among straight line features from different images and then a new algorithm for establishing all the correspondences completely is described simply. The experimental results with real stereo images illustrate that the complex correspondences among straight lines are actual cases and can be established effectively.

A model-based method for indoor mobile robot localization using monocular vision and straight-line correspondences

A model-based method for indoor mobile robot localization is presented herein; this method relies on monocular vision and uses straight-line correspondences. A classical four-step approach has been adopted (i.e. image acquisition, image feature extraction, image and model feature matching, and camera pose computing). These four steps will be discussed with special focus placed on the critical matching problem. An efficient and simple method for searching image and model feature correspondences, which has been designed for indoor mobile robot self-location, will be highlighted: this is a three-stage method based on the interpretation tree search approach. During the first stage, the correspondence space is reduced by virtue of splitting the navigable space into view-invariant regions. In making use of the specificity of the mobile robotics frame of reference, the global interpretation tree is divided into two sub-trees; two low-order geometric constraints are then defined and applied directly on 2D–3D correspondences in order to improve pruning and search efficiency. During the last stage, the pose is calculated for each matching hypothesis and the best one is selected according to a defined error function. Test results illustrate the performance of this approach.

Three-dimensional motion determination from real scene images using straight line correspondences

An experiment in determining (3D) vehicle motion in an outdoor scene using line correspondences over an image sequence is presented. The motivation of this experiment is to test how well algorithms of motion estimation work on real life outdoor scenes. Using images taken by a well calibrated camera, the experiment includes straight line extraction from intensity images, straight line matching and motion estimation using line correspondences. Many corrections and adjustments are performed on the raw image data before they are used for motion estimation, which include film distortion correction, lens distortion correction and camera alignment. The results of this experiment indicate that for the type of vehicle motion under consideration, our algorithm can obtain reasonably accurate estimates for rotation. The experiment also reveals that there is a big gap between simulation tests and real scene tests for algorithms of motion estimation.

Determining straight line correspondences from intensity images

This paper presents an algorithm for straight edge extraction from intensity images and an algorithm for straight line matching. In the first part of the paper, straight edge extraction is described. Image data are first processed by the operation of edge support focusing to remove unnecessary image details. An edge support is formed in this process. Then, straight edges are extracted from line support regions, which are segmented from the edge support. In the second part of this paper, we describe straight line matching using a matching function, which characterizes the similarity of edge lines of two images and is based on not only the geometrical relations of the lines but also the information from the intensity images. A technique of two-step matching is applied to reduce the cost of computation. The motivation behind this paper is our work on motion estimation from line correspondences of sequential images where straight edge extraction and matching is an essential first step. The output data of the algorithm can also be used for calibration of stereo camera setups. The results of experiments using indoor and outdoor scene images are shown.

Interpretation of straight line correspondences using angular relations

Formal analysis of the structure-from-motion problem has produced computational schemes that are often unstable and sensitive to image measurement errors. This is in part due to the fact that the formal analysis is mainly concerned, as it should be, with the most general cases, treating special cases with marginal interest. However, special cases abound in man-made environments; their occurrence, if ascertained, simplifies drastically the task of 3D interpretation. After examining the problem of interpreting general configurations of lines in space, this paper proposes a number of simple and efficient computational units that are tuned to the occurrence of special line configurations. The process of propagation, which allows interpretation to spread over the image, is also advocated. Such computational units and processes can be organized in a rule-based framework where the occurrence of special configurations can either be tested directly, or hypothesized and used until an inconsistency is demonstrated.

A linear algorithm for motion estimation using straight line correspondences

This paper presents a linear algorithm for determining 3D motion parameters of a rigid object based on straight line correspondences. The algorithm requires a minimum number of thirteen line correspondences over three frames. It includes three steps: first, three intermediate matrices are computed; then, several candidate solutions of the rotation matrices and translation vectors are obtained from the intermediate matrices; finally, motion parameters are uniquely determined by the physical constraints of 3D rotations and translations. Some simulation results are also given.

Estimation of rigid body motion using straight line correspondences

An algorithm for the estimation of rigid body motion using straight line correspondences is presented in this paper. In the case of pure translation, we present a linear algorithm using 5 line correspondences over 3 frames. In the case of general motion, it is found that the rotation and the translation parts are separable. The rotation part can be computed by the iterative solution of nonlinear equations based on 6 or more line correspondences over 3 frames. After the rotation is found, the translation part is determined just as in the pure translation case. For the special case of constant rotation, the convergence range of the iterative method is wide enough so that global search can be used to estimate the rotation matrix. However, for the case of variable rotation, global search appears computationally infeasible at present.