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In view of the matching problem between objects and mirrors in images, the RNFA(Relative Number of False Alarms)edge chain detection method is introduced to obtain richer line segments. An improved LBD(Line Band Descriptor) algorithm is proposed for constructing local invariant feature descriptors, and initial matching pairs are obtained by comparing local invariant feature descriptors. The screening of the global projection angle is adopted and the false matches in the initial matching pairs are eliminated fitting the projection center line. After the selection of the global projection angle and the fitting of the projection median are completed, the screening of the local invariant feature descriptor threshold is relaxed to obtain more matching pairs and improve the recall rate. The experimental results of image set simulation show that the designed algorithm can better identify line segments in the weaker texture regions and obtain more matching pairs on the basis of the guaranteed performance of the original algorithm, which can improve the correct matching rate by about 5% and achieve a recall rate of over 90%. [ABSTRACT FROM AUTHOR]

Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint. [ABSTRACT FROM AUTHOR]

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we suggest calculating the endpoint error in LSM rather than the direction error in considering the error band and segment length. We propose a point-pair representation, and transform the LSM problem to be an intuitive point-pair alignment problem with a mixture model. The point-pair representation is more robust on the segment length than the linear equation representation used in RANSAC, which significantly preserves short line segments. Then, based on the point-pair representation, we propose a novel Directed Endpoint Drift method to solve the inherent fracture problem of line segments by allowing the endpoints to move along the line to complement the fracture. Compared with the recent learning-based method, the proposed method nearly doubles the number of correct matches on the remote sensing dataset. Especially, the recall is improved by 10% to 30% for both the short segments and the slightly fractured segments, compared with the popular RANSAC and MAGSAC++ methods. The code is available at https://github.com/shenliang16/DEpD. [ABSTRACT FROM AUTHOR]

Many methods have been proposed to extract line segment (LS) correspondences for images with viewpoint variations. However, the matching performance on images with viewpoint variations is still limited due to the influence of discontinuous parallax and poor textures on images. Existing line segment matching (LSM) methods rarely consider the coexistence of these two situations. This study attempts to address this problem by proposing an adaptive feature region (FR)-based LSM method based on the observation that different FRs are suitable for different image scenes under image viewpoint change. In the proposed method, LSs are paired, and two types of FRs, symmetric FR centered on the line-pair intersection and asymmetric FR with the intersection as a vertex, are constructed for every line pair. The asymmetric FR of a line pair is a subregion of its symmetric FR. And the complement region of asymmetric FR in symmetric FR is called complement FR. To choose a suitable FR for the matching of each line pair adaptively, the asymmetric FR-based feature descriptor similarity and epipolar line-based constraints are combined to determine candidate line pair matches firstly. Then, the candidate matches with similar complement FR-based feature descriptors are considered initial matches and used to construct a topological constraint to check other candidates. Finally, we estimate an adaptive influence region-based local homography for each matched line pair to constrain the matching of unmatched individual LSs. The experimental results on the test images with viewpoint changes show that our approach outperforms the state-of-the-art methods in terms of Recall, matching precision (MP), and F-Measure (reflecting the overall matching performance of Recall and MP). Especially, it improves the average Recall and average F-Measure of the best method among the comparison methods by 45.20% and 25.83%, respectively.

Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint.

Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods. [ABSTRACT FROM AUTHOR]

Line segment matching is important in applications that require recovering the 3D structure of objects (e.g., manmade objects in street-level scenarios). However, differentiating between true and false line matches is generally difficult without strong geometric constraints for line segments. Hence, additional constraints are forced to be used, sacrificing many true line matches. This study proposes a robust line segment matching method based on global projective transformation modeling. We develop a non-parametric motion regression formulation with a specially designed direct linear transformation-based cost function that reformulates the piecewise smoothly varying projective transformations as a global continuous model from highly noisy point matches. The resultant model can effectively approximate the real underlying image transformation and derive high-quality point matches. We apply the computed model and high-quality point matches to a point-correspondence-based line matching pipeline, which provides sufficient strict geometric constraints for first generating the pair-to-pair matches and then distilling the line-to-line matches. Extensive experiments conducted on two challenging line matching datasets show that the proposed method can obtain considerable correct line segment matches, outperforming the comparison methods in mean F-score by at least 15.5% on the benchmark dataset and 16.9% on the local dataset. Code is available at https://github.com/geovsion/SLEM. [ABSTRACT FROM AUTHOR]

Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (LJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes. [ABSTRACT FROM AUTHOR]

Line segment matching for wide-baseline images is challenging due to the significant viewpoint differences. In this study, we propose a hierarchical line segment matching method based on viewpoint robust local structure and geometric constraints. In our approach, line segments are paired and classified into three types representing heuristically those with different level of matchability: structured line pairs (S-LPs), unstructured line pairs (U-LPs), and individual line segments (I-LSs). Accordingly, we design a hierarchical matching framework that consists of three matching layers respectively corresponding to the above three types: in the first layer, robust local structures are constructed for S-LPs. We match the S-LPs by measuring local structure similarity (LSS). In the second layer, we build a topological descriptor-based constraint based on the S-LP matches and combine it with epipolar geometry-based constraints to select candidate matches for U-LPs, and then use LSS to further refine the matches. In the third layer, we estimate local homography for I-LSs to build constraints, to extract matches by exploiting the implicit region information of line pair matches. A pair of pre- and postprocessing algorithms, namely line segment merging and match reassignment, are performed before and after the matching procedure to overcome the negative effect of line segment fragmentation on the matching. Experimental results demonstrate that the proposed method performs better than state-of-the-art methods (with the largest improvement of 124.17% in terms of F-Measure over the best one among the compared methods). [ABSTRACT FROM AUTHOR]

Line matching plays an essential role in Structure from Motion (SFM) and Simultaneous Localization and Mapping (SLAM), especially in low-texture scenes, where feature points are hard to be detected. In this paper, we present a new method by combining Convolutional Neural Networks and Graph Convolutional Networks to match line segments in pairs of images. We design a graph-based method to predict the assignment matrix of two feature sets with solving a relaxed optimal transport problem. In contrast to handcrafted line matching algorithms, our approach learns the line segment features and performs matching simultaneously through end-to-end weakly supervised training. The experiment results show that our method outperforms the state-of-the-art techniques and is robust to various image transformations. Besides, the generalization experiment illustrates that our method has good generalization ability without fine-tuning. The code of our work is available at https://github.com/mameng1/GraphLineMatching. [ABSTRACT FROM AUTHOR]

High-accuracy localization systems for non-line-of-sight (NLOS) environments have been a popular focus of research on indoor positioning. In particular, the use of ultra-wideband (UWB) technology for ranging is emerging as an effective method of indoor localization due to its high accuracy. To improve the overall performance of UWB systems, combining UWB technology with an inertial navigation system (INS) is a commonly used indoor positioning approach. However, UWB measurements are affected by outliers under the NLOS propagation of radio signals. In addition, INS tend to suffer from error accumulation without constraints imposed by observations from other sensors. To address these problems, an adaptive robust extended Kalman filter algorithm based on tightly coupled UWB/INS/floor map integration is proposed, with which the noise in the UWB observations can be adaptively adjusted. The integrated system performs NLOS identification based on a map line segment matching algorithm; then, abnormal observations are abandoned, or their weights are reduced to prevent the error accumulation of the INS from being incorrectly constrained. Finally, an evaluation based on experimental data shows the significant enhancement achieved with the proposed algorithm based on the integration of UWB technology, an INS and a floor map, which can achieve a 2-D positioning accuracy that corresponds to a root mean square error of 0.27 m. Thus, substantial improvements can be achieved for long-term operation in NLOS environments using the proposed integration scheme. [ABSTRACT FROM AUTHOR]

Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.

This paper proposes a line segment matching method by performing line mapping and unmapping based on point correspondences. The goal of this paper is to improve the accuracy and the robustness of line segment matching for two views, which will be conducive for generating a full single-line structure for image sequences. In this paper, to improve the quantity and quality of line matches, the topological adjacency of a point-line is first introduced for two goals: to find and filter the candidate line segment by resorting to KD-tree data-index structure efficiently in the corresponding image and to leverage the candidate line segment to improve the performance of planar homography. In addition, the shift threshold parameter is theoretically analyzed, and trials are validated to determine the matching degree. Line mapping and unmapping are then used for image sequences to reduce missing matches. The extensive experimental results validate and demonstrate that our method is both more accurate and robust than existing line matching methods for two views under the circumstance of a higher recall rate. In addition, our method contributes in finding full line segments for image sequences using line mapping, with higher completeness of the 3D line model than that obtained by the state-of-the-art methods.

This paper presents a novel method for matching line segments in images based on pair-wise geometric constraints and matching redundancy. In this study, pairs of line segments satisfying angle and distance constraints are used as matching primitives. To ensure that each extracted line segment is paired with another line segment, the search region of each line segment is gradually grown until it is paired. Initial pair-to-pair correspondences between two images are established using four pair-wise constraints; next, line-to-line correspondences are obtained. To effectively solve the matching conflict in the results, a method of recording the result of line pair matching based on a double-layer matrix is proposed. Based on the double-layer matrix, an effective checking method for the line matching results based on the collinearity constraint and matching redundancy is presented. It fully utilizes redundancy information and considers the collinearity of fragmented line segments. Further, it can effectively separate correct and incorrect matches from the one-to-many, many-to-one, and many-to-many matching results. The proposed method was tested on 12 image pairs from a benchmark of matched-lines, and compared with other state-of-the-art methods. The results demonstrate the superiority of the proposed method due to its higher accuracy and greater recall in challenging cases. [ABSTRACT FROM AUTHOR]

Line segment matching in two or multiple views is helpful to 3D reconstruction and pattern recognition. To fully utilize the geometry constraint of different features for line segment matching, a novel graph-based algorithm denoted as GLSM (Graph-based Line Segment Matching) is proposed in this paper, which includes: (1) the employment of three geometry types, i.e., homography, epipolar, and trifocal tensor, to constrain line and point candidates across views; (2) the method of unifying different geometry constraints into a line-point association graph for two or multiple views; and (3) a set of procedures for ranking, assigning, and clustering with the line-point association graph. The experimental results indicate that GLSM can obtain sufficient matches with a satisfactory accuracy in both two and multiple views. Moreover, GLSM can be employed with large image datasets. The implementation of GLSM will be available soon at https://skyearth.org/research/. [ABSTRACT FROM AUTHOR]

Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (LJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes.

Three-dimensional reconstruction of line and plane structures from two images is a major task in urban building modeling. However, traditional line segment (LS) matching methods frequently produce inaccurate few LS matches, and further lead to unreliable sparse 3D line-plane reconstruction. To address these issues, this paper presents an effective line-plane reconstruction method based on angle regularization. The proposed method first performs LS matching by learning the angles between planes using convolutional neural networks (CNNs). Angle regularization is used to correct unreliable LS matches and infer progressively potential 3D LSs for unmatched ones. Then, the resulting 3D LSs and planes are globally regularized by incorporating geometric constraints, image features, and plane and angle regularity terms under a unified optimization framework. Experiments on several standard datasets demonstrate that our method has clear advantages over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We present a novel structure from motion pipeline, which estimates motion and wiry 3D structure from imaged line segments across multiple views. Although the position and orientation of line segments can be determined more accurately than point features, the instability of their endpoints and the fact that lines are not constrained by epipolar geometry diverted most research focus away to point-based methods. In our approach, we tackle the problem of instable endpoints by utilizing relaxed constraints on their positions, both during matching and as well in the following bundle adjustment stage. Furthermore, we gain efficiency in estimating trifocal image relations by decoupling rotation and translation. To this end, a novel linear solver for relative translation estimation given rotations from five line correspondences in three views is introduced. Extensive experiments on long image sequences show that our line-based structure from motion pipeline advantageously complements point-based methods, giving more meaningful 3D representation for indoor scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces a new system for reconstructing 3D scenes from Line Segments (LS) on images. A new LS matching algorithm and a novel 3D LS reconstruction algorithm are incorporated into the system. Two coplanar cues that indicates image LSs are coplanar in physical (3D) space are extensively exploited in both algorithms: (1) adjacent image LSs are coplanar in space in a high possibility; (2) the projections of coplanar 3D LSs in two images are related by the same planar homography. Based on these two cues, we efficiently match LSs from two images firstly in pairs through matching the V-junctions formed by adjacent LSs, and secondly in individuals by exploiting local homographies. We extract for each V-junction a scale and affine invariant local region to match V-junctions from two images. The local homographies estimated from V-junction matches are used to match LSs in individuals. To get 3D LSs from the obtained LS matches, we propose to first estimate space planes from clustered LS matches and then back-project image LSs onto the space planes. Markov Random Field (MRF) is introduced to help more reliable LS match clustering. Experiments shows our LS matching algorithm significantly improves the efficiency of state-of-the-art methods while achieves comparable matching performance, and our 3D LS reconstruction algorithm generates more complete and detailed 3D scene models using much fewer images. [ABSTRACT FROM AUTHOR]

While navigating in a typical traffic scene, with a drastic drift or sudden jump in its Global Positioning System (GPS) position, the localization based on such an initial position is unable to extract precise overlapping data from the prior map in order to match the current data, thus rendering the localization as unfeasible. In this paper, we first propose a new method to estimate an initial position by matching the infrared reflectivity maps. The maps consist of a highly precise prior map, built with the offline simultaneous localization and mapping (SLAM) technique, and a smooth current map, built with the integral over velocities. Considering the attributes of the maps, we first propose to exploit the stable, rich line segments to match the lidar maps. To evaluate the consistency of the candidate line pairs in both maps, we propose to adopt the local appearance, pairwise geometric attribute and structural likelihood to construct an affinity graph, as well as employ a spectral algorithm to solve the graph efficiently. The initial position is obtained according to the relationship between the vehicle's current position and matched lines. Experiments on the campus with a GPS error of dozens of metres show that our algorithm can provide an accurate initial value with average longitudinal and lateral errors being 1.68m and 1.04m, respectively. [ABSTRACT FROM AUTHOR]

Computer screens are rich in discontinuous-tone content besides traditional continuous-tone content. Thus, new video coding tools coherently united into traditional hybrid coding tools are needed to effectively compress screen contents. This paper proposes a Pseudo-2D-matching (P2M) coder based enhancement to High Efficiency Video Coding for screen contents. An input Largest Coding Unit (LCU) is simultaneously fed into a P2M coder and a traditional hybrid coder. The coder minimizing rate-distortion is selected as the final coder for the LCU. The P2M coder breaks an LCU into many horizontal or vertical line segments and searches matching line segments in the searching-window consisting of previously coded pixels. Since the searching-window is large (a few kilobytes to megabytes) to increase the chance to find a good matching, a hash-table is used to speedup the search. In the P2M coder, three matching modes are used: 1) vertically scanned string matching; 2) horizontally scanned 2D-shape-preserved matching; and 3) vertically scanned 2D-shape-preserved matching. All three matching modes are used to code an LCU and the mode minimizing rate-distortion is selected in the P2M coder. The P2M based enhancement achieves significant Bitrate-distortion rate (BD-rate) and subjective visual quality improvement over traditional hybrid coding for screen contents. [ABSTRACT FROM AUTHOR]

Abstract: In the paper we propose a novel line segment matching method over multiple views based on sparse representation with geometric configuration constraint. The significant idea of the paper is that we transfer the issue of line correspondence into a sparsity based line recognition. At first, line segments are detected by a LSD (line segment detector) and clustered according to spatial proximity to form completed lines. For each point within a line, SIFT is extracted to represent the attribute of point and PHOG is also considered to describe the appearance of the patch centered at the point. SIFT and PHOG are simply concatenated as a single feature vector and then all these point features are put together by a max pooling function to form a distinctive line signature. Then, all line features extracted from training images are trained into a dictionary using sparse coding. Lines with the same similarity may fall together in the high-dimensional feature space. Finally, line segments in a test view are matched to their counterparts in other views by seeking maximal pulses from the coefficient vector. Under our framework, line segments are trained once and matched across all other views. Experimental results have validated the effectiveness of the approach for planar structured scenes under various transformations and degradation, such as viewpoint change, illumination, blur and compression corruption. [Copyright &y& Elsevier]

Highlights: [•] Multi-scale line detection strategy is applied. [•] A novel Line Band Descriptor is developed to depict the local appearance of line. [•] A graph is build which combines appearance similarities and geometric consistencies. [•] The graph matching problem is solved by a spectral technique. [ABSTRACT FROM AUTHOR]

Land-based mobile mapping system ( MMS) technology provides an innovative way to update geospatial information, characterised by rapid data collection and direct georeferencing. This paper presents a new method for automatically updating large scale maps using sequential MMS imagery. A major component of the technique is the extraction of vertical lines from such imagery through an improved Hough transform and vanishing point classification. These lines are stereomatched using an epipolar constraint and random sample consensus ( RANSAC) strategy, with object space positions obtained by photogrammetric intersection. Buildings are recognised based on these vertical lines in conjunction with basic polygon templates, allowing maps to be updated. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: By extending the previously proposed geometric branch-and-bound algorithm with bounded alignment for point pattern matching, the paper presents the development and evaluation of a new and fast algorithm for image registration based on line segments. Using synthetically generated data sets with randomly distributed line segments and hard test cases with highly symmetric line patterns, as well as real remote sensing images, the developed algorithm is shown to be computationally fast, highly robust, capable of handling severely corrupted data sets with considerable line segment position errors as well as significant fragmented and spurious line segments in the images to be matched. [Copyright &y& Elsevier]

A new iteration scheme is proposed to solve the line segment matching problem in stereo vision analysis. A match function which directly reflects the requirements of the epipolar and disparity constraints is proposed for line segment matching. The information contained in the match function is used to determine line segment correspondences indirectly. After a match network is established according to the match function values, a new iteration algorithm is employed to tune the strengths of the match links in the match network so that the match network can converge to a stable state. No explicit compatibility coefficient need be defined for computing the support function values in the iterations, resulting in a faster computation speed than those of conventional relaxation matching techniques. The inherent anti-symmetric characteristic of relaxation matching for the image correspondence problem is also avoided naturally. The experimental results show that the proposed iteration scheme is effective and suitable for matching line segments even when images are complicated. [ABSTRACT FROM AUTHOR]

A line segment based approach for 3D motion estimation and tracking of multiple objects from a monocular image sequence is presented. Objects are described by means of 3D line segments, and their presence in the scene is associated with the detection of 2D line segments on the image plane. A change detection algorithm is applied to detect moving objects on the image plane and a Hough-based algorithm is used to individuate 2D line segments. 3D parameters of each line segment are estimated, at each time instant, by means of an extended Kalman filter (EKF), whose observations are the displacements of 2D line segment endpoints on the image plane. Results on both synthetic and real scenes are presented. [ABSTRACT FROM AUTHOR]

• Matching line segments for stereo images can be employed relying only on the geometry constraint. • The association graph constructed with the epipolar constraint is invariant to the projective transformation. • Employing the random walk on the association graph enables ranking the candidate. • The proposed algorithm outperformed the state of the art methods in the experiments, especially in the scenes of the wide baseline, the steep viewpoint change, and the dense line segment. This paper presents a novel method for matching line segments between stereo images. Given the fundamental matrix, the local homography can be over determined with pairwise line segment candidates. We exploit this constraint to initialize the candidate and construct the novel homography graph. Because the constraint between the node is based on the epipolar geometry, the homography graph is invariant to the local projective transformation. We employ the reweighted random walk on the graph to rank the candidate, then, we propose the constrained-greedy algorithm to obtain the reliable match. To the best of our knowledge, this is the first study to embed the epipolar geometry into the graph matching theory for the line segment matching. When evaluated on the 32 image patches, our method outperformed the state of the art methods, especially in the scenes of the wide baseline, steep viewpoint changes and dense line segments. The proposed algorithm is available at https://github.com/weidong-whu/line-match-RRW. [ABSTRACT FROM AUTHOR]