As the vital procedure for exploiting line segments extracted from images for solving computer vision problems, Line Segment Matching (LSM) has received growing attentions from researcher in recent years, and a considerable number of methods have been proposed. However, no one has attempted to solve two major problems in this area. The first is how to evaluate different methods in an unbiased way. All proposed methods were evaluated using images and line segment detectors selected by the authors themselves, making the conclusions based on the somewhat biased experiments less convincing. The second problem is that there is no reliably automatic way to access the correctness of obtained line segment matches, which can often be up to hundreds in quantity. Checking them one by one by visual inspection is the only reliable, but very tedious and error-prone way. In this paper, we target to solve the two problems. We introduce a benchmark which provides the ground truth matches among 30 pairs of line segment sets extracted from 15 representative image pairs using two state-of-the-art line segment detectors. With the benchmark, we evaluated some of the existing LSM methods.

This paper presents an method that matches points and line segments jointly on wide-baseline stereo images. In both two images to be matched, line segments are extracted and those spatially adjacent ones are intersected to generate V-junctions. To match V-junctions from the two images, we extract for each of them an affine and scale invariant local region and describe it with SIFT. The putative V-junction matches obtained from evaluating their description vectors are refined subsequently by the epipolar line constraint and topological distribution constraint among neighbor V-junctions. Since once a pair of V-junctions are matched, the two pairs of line segments forming them are matched accordingly. A part of line segments from the two images are therefore matched along with V-junction matches. To get more line segment matches, we further match those left unmatched line segments by the local homographies estimated from their adjacent V-junction matches. Experiments verify the robustness of the proposed method and its superiority to both some famous point and line segment matching methods on wide-baseline stereo images. In addition, we also show the proposed method can make it easier for 3D line segment reconstruction.

Fisheye image rectification and estimation of intrinsic parameters for real scenes have been addressed in the literature by using line information on the distorted images. In this paper, we propose an easily implemented fisheye image rectification algorithm with line constrains in the undistorted perspective image plane. A novel Multi-Label Energy Optimization (MLEO) method is adopted to merge short circular arcs sharing the same or the approximately same circular parameters and select long circular arcs for camera rectification. Further we propose an efficient method to estimate intrinsic parameters of the fisheye camera by automatically selecting three properly arranged long circular arcs from previously obtained circular arcs in the calibration procedure. Experimental results on a number of real images and simulated data show that the proposed method can achieve good results and outperforms the existing approaches and the commercial software in most cases.

In this paper, we present a robust line segment detection algorithm to efficiently detect the line segments from an input image. Firstly a parameter-free Canny operator, named as CannyPF, is proposed to robustly extract the edge map from an input image by adaptively setting the low and high thresholds for the traditional Canny operator. Secondly, both efficient edge linking and splitting techniques are proposed to collect collinear point clusters directly from the edge map, which are used to fit the initial line segments based on the least-square fitting method. Thirdly, longer and more complete line segments are produced via efficient extending and merging. Finally, all the detected line segments are validated due to the Helmholtz principle [1, 2] in which both the gradient orientation and magnitude information are considered. Experimental results on a set of representative images illustrate that our proposed line segment detector, named as CannyLines, can extract more meaningful line segments than two popularly used line segment detectors, LSD [3] and ED-Lines [4], especially on the man-made scenes.