Showing papers on "Line segment published in 2014"

Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains

Abstract: We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.

Outdoor Place Recognition in Urban Environments Using Straight Lines

Abstract: In this paper, we propose a visual place recognition algorithm which uses only straight line features in challenging outdoor environments. Compared to point features used in most existing place recognition methods, line features are easily found in man-made environments and more robust to environmental changes such as illumination, viewing direction, or occlusion. Candidate matches are found using a vocabulary tree and their geometric consistency is verified by a motion estimation algorithm using line segments. The proposed algorithm operates in real-time, and it is tested with a challenging real-world dataset with more than 10,000 database images acquired in urban driving scenarios.

Image processing apparatus, image processing method and program

Abstract: PROBLEM TO BE SOLVED: To improve a processing speed while properly acquiring a combination of major colors to be a distribution object of an intermediate color.SOLUTION: An image processing apparatus includes: a major color extraction section for extracting major colors including a foreground color and a background color of an image on the basis of a result obtained by associating the color information of each pixel of the image with an area of a color space; a line segment calculation section for calculating a line segment in which an intermediate color showing a color other than the major colors is present on a line between arbitrary two major colors in the color space among the major colors extracted by the major color extraction section; a determination section for acquiring, in each intermediate color in the image, two major colors in which a distance between an intermediate color and a line segment is the smallest in the color space to determine which the intermediate color belongs to between the two major color; and a generation section for generating an image obtained by replacing each intermediate color in the image with the major color determined by the determination section.

Scale-invariant line descriptors for wide baseline matching

Abstract: In this paper we propose a method to add scale-invariance to line descriptors for wide baseline matching purposes. While finding point correspondences among different views is a well-studied problem, there still remain difficult cases where it performs poorly, such as textureless scenes, ambiguities and extreme transformations. For these cases using line segment correspondences is a valuable addition for finding sufficient matches. Our general method for adding scale-invariance to line segment descriptors consist of 5 basic rules. We apply these rules to enhance both the line descriptor described by Bay et al. [1] and the mean-standard deviation line descriptor (MSLD) proposed by Wang et al. [14]. Moreover, we examine the effect of the line descriptors when combined with the topological filtering method proposed by Bay et al. and the recent proposed graph matching strategy from K-VLD [6]. We validate the method using standard point correspondence benchmarks and more challenging new ones. Adding scale-invariance increases the accuracy when confronted with big scale changes and increases the number of inliers in the general case, both resulting in smaller calibration errors by means of RANSAC-like techniques and epipolar estimations.

Improving Sparse 3D Models for Man-Made Environments Using Line-Based 3D Reconstruction

Abstract: Traditional Structure-from-Motion (SfM) approaches work well for richly textured scenes with a high number of distinctive feature points. Since man-made environments often contain texture less objects, the resulting point cloud suffers from a low density in corresponding scene parts. The missing 3D information heavily affects all kinds of subsequent post-processing tasks (e.g. Meshing), and significantly decreases the visual appearance of the resulting 3D model. We propose a novel 3D reconstruction approach, which uses the output of conventional SfM pipelines to generate additional complementary 3D information, by exploiting line segments. We use appearance-less epipolar guided line matching to create a potentially large set of 3D line hypotheses, which are then verified using a global graph clustering procedure. We show that our proposed method outperforms the current state-of-the-art in terms of runtime and accuracy, as well as visual appearance of the resulting reconstructions.

TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 2: Line Segments Linking and Merging

Abstract: Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and obtain vectors similar to what an analyst would manually extract. The objective of this study is the implementation, evaluation and comparison of Hough transform, segment merging and polynomial fitting methods towards automated tectonic lineament mapping. For this purpose we developed a new MATLAB-based toolbox (TecLines). The proposed toolbox capabilities were validated using a synthetic Digital Elevation Model (DEM) and tested along in the Andarab fault zone (Afghanistan) where specific fault structures are known. In this study, we used filters in both frequency and spatial domains and the tensor voting framework to produce binary edge maps. We used the Hough transform to extract linear image discontinuities. We used B-spline as a polynomial curve fitting method to eliminate artificial line segments that are out of interest and to link discontinuous segments with similar trends. We performed statistical analyses in order to compare the final image discontinuities maps with existing references map.

Power line detection using a circle based search with UAV images

Abstract: A new method for power line detection based on computer graphics algorithms is presented. The algorithm uses geometric relationships that are inherent to the circle symmetry. The method detects line segments that are linked in a posterior stage. For the detection, we use Canny and Steerable Filters. We developed two tests for validating the proposed approach. The first one uses synthetic images and the second one real power line images taken from UAVs. The results show that this method is not only efficient for line detection, but it also takes compared with state of the art algorithms a short computing time without the use of a GPU approach.

Pylon line spatial correlation assisted transmission line detection

Abstract: A transmission line is one of the most hazardous objects to low altitude flying aircraft. Due to its extremely tiny size and unsalient visual features, transmission line detection (TLD) is a well-recognized problem. In this paper, a novel TLD method is proposed with the assistance of the spatial correlation between pylon and line for TLD. First, a unidirectional spatial mapping is built up to describe the pylon line spatial correlation. Then, the proposed pylon line spatial correlation and other line features are integrated into a Bayesian framework, which is trained in advance and used to estimate the probability of one line segment belonging to a transmission line. Compared with three other line-based TLD methods, the experimental results demonstrate that the proposed method can obtain better detection performance with higher detection rates and much lower false alarm rates.

Automatic Line Segment Registration Using Gaussian Mixture Model and Expectation-Maximization Algorithm

Abstract: Line segment registration (LSR) for image pairs is a challenging task but plays an important role in remote sensing and photogrammetry. This paper proposes a line segment registration method using Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) algorithm. Comparing to the conventional registration methods which consider the local appearance of points or line segments, the proposed method of LSR uses only the spatial relations between the line segments detected from an image pair, and it does not require the corresponding line segments sharing the same start points and end points. Although the proposed method is not confined to the transformation model between the image pair, the affine model, which is a simple and fast registration model and widely used in remote sensing, is taken to verify the proposed method. Various images including aerial images, satellite images and GIS data are used to test the algorithm, and test results show that the method is robust to different conditions, including rotation, noise and illumination. The results of the proposed method are compared with those of other line segment matching methods, and it is shown that the proposed method is superior in matching precision and performs better in less-texture or no-texture case.

Automatic comic page segmentation based on polygon detection

Abstract: Comic page segmentation aims to automatically decompose scanned comic images into storyboards (frames), which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel method for comic page segmentation by finding the quadrilateral enclosing box of each storyboard. We first acquire the edge image of the input comic image, and then extract line segments with a heuristic line segment detection algorithm. We perform line clustering to further merge the overlapped line segments and remove the redundancy line segments. Finally, we perform another round of line clustering and post-processing to compose the obtained line segments into complete quadrilateral enclosing boxes of the storyboards. The proposed method is tested on 2,237 comic images from 12 different printed comic series, and the experimental results demonstrate that our method is effective for comic image segmentation and outperforms the existing methods.

Algebraic-Trigonometric Pythagorean-Hodograph curves and their use for Hermite interpolation

Abstract: In this article we define a new class of Pythagorean-Hodograph curves built-upon a six-dimensional mixed algebraic-trigonometric space, we show their fundamental properties and compare them with their well-known quintic polynomial counterpart. A complex representation for these curves is introduced and constructive approaches are provided to solve different application problems, such as interpolating C 1 Hermite data and constructing spirals as G 2 transition elements between a line segment and a circle, as well as between a pair of external circles.

Planar Structure Matching under Projective Uncertainty for Geolocation

Abstract: Image based geolocation aims to answer the question: where was this ground photograph taken? We present an approach to geolocalating a single image based on matching human delineated line segments in the ground image to automatically detected line segments in ortho images. Our approach is based on distance transform matching. By observing that the uncertainty of line segments is non-linearly amplified by projective transformations, we develop an uncertainty based representation and incorporate it into a geometric matching framework. We show that our approach is able to rule out a considerable portion of false candidate regions even in a database composed of geographic areas with similar visual appearances.

System and method of retail image analysis

Abstract: The present disclosure provides a method of analyzing a retail image, the method comprising: obtaining a retail image representative of a flank side of a shelving module including at least one shelf; detecting in the retail image at least one line segment corresponding to a flank edge of the at least one shelf; determining a dimension of the line segment in the retail image; and rating the retail image based on the dimension of the line segment.

A line-based approach for precise extraction of road and curb region from mobile mapping data

Abstract: . Planar structure detection from point clouds is important process in many applications such as maintenance of infrastructure facility including roads and curbs because most artificial structures consists of planar surfaces. The Mobile Mapping System can obtain a large amount of points with traveling at a standard speed. However, in the case that the high-end laser scanning system is equipped, the distribution density of points is uneven. In the point-based method, this situation causes the problem to the method of calculating geometric information using neighborhood points. In this paper, we propose a line-based region growing method in order to detect planar structures with precise boundary from point clouds with uneven distribution density of points. The precise boundary of a planar structure is maintained by appropriately creating line segments from the input clouds. We adapt the definition of neighborhood and the estimation of the normal vector to the line-based region growing. The evaluation by comparing our result with manually extracted points shows that more than 98% of curb points are detected. And, about 90% of the boundary points between a road and a curb are detected with less than 0.005 meters of the distance error.

Recognition of Airport Runways in FLIR Images Based on Knowledge

Abstract: Airport runway recognition technology would play an important role in developing intelligent weapon systems in the future. In this letter, a method of automatically finding runways in forward looking infrared (FLIR) images is proposed based on the knowledge of vision. First, the line segments in the images are extracted by a fast line segment detector (LSD) and an improved line segment linking method. Then, the regions of interest (ROI) of runways are detected using some constraint rules based on the direction, gradient, and width of line segment pairs. Afterward, an ROI length backtracking technique based on texture distribution is presented to retrieve the complete ROI. Finally, using runway regional self-similarity and contextual information, several decision criteria are formulated to accurately recognize the runway. Experimental results on the FLIR images with different imaging ranges show that the proposed algorithm is robust and has a good real-time performance.

Planar Structures from Line Correspondences in a Manhattan World - eScholarship

Abstract: Planar Structures from Line Correspondences in a Manhattan World Chelhwon Kim 1 , Roberto Manduchi 2 Electrical Engineering Department Computer Engineering Department University of California, Santa Cruz Santa Cruz, CA, US Abstract. Traditional structure from motion is hard in indoor environ- ments with only a few detectable point features. These environments, however, have other useful characteristics: they often contain severable visible lines, and their layout typically conforms to a Manhattan world geometry. We introduce a new algorithm to cluster visible lines in a Man- hattan world, seen from two different viewpoints, into coplanar bundles. This algorithm is based on the notion of “characteristic line”, which is an invariant of a set of parallel coplanar lines. Finding coplanar sets of lines becomes a problem of clustering characteristic lines, which can be accomplished using a modified mean shift procedure. The algorithm is computationally light and produces good results in real world situations. Introduction This paper addresses the problem of reconstructing the scene geometry from pictures taken from different viewpoints. Structure from motion (SFM) has a long history in computer vision [1, 2], and SFM (or visual SLAM) algorithms have been ported on mobile phones [3, 4]. Traditional SFM relies on the ability of detecting and matching across views a substantial number of point features. Unfortunately, robust point detection and matching in indoor environments can be challenging, as the density of detectable points (e.g. corners) may be low. At the same time, indoor environments are typically characterized by (1) the presence of multiple line segments (due to plane intersections and other linear structures), and (2)“Manhattan world” layouts, with a relatively small number of planes at mutually orthogonal orientations. This paper introduces a new algorithm for the detection and localization of planar structures and relative camera pose in a Manhattan world, using line matches from two images taken from different viewpoints. As in previous ap- proaches [5–7], the orientation (but not the position) of the two cameras with respect to the environment is computed using vanishing lines and inertial sensors (available in all new smartphones). The main novelty of our algorithm is in the criterion used to check whether groups of lines matched in the two images may be coplanar. Specifically, we introduce a new invariant feature (~n -characteristic line) of the image of a bundle of coplanar parallel lines, and show how this

Structure from Motion with Line Segments under Relaxed Endpoint Constraints

Abstract: We present a novel SfM pipeline which estimates motion and wiry 3D structure from imaged line segments across multiple views. Though the position of lines can be determined more accurately than point features, the issue of detecting stable endpoints diverted most research focus away to point-based methods. In our approach, we tackle this problem by utilizing relaxed constraints on the endpoint positions both during matching as well as in the 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. Experiments on long sequences show that our line-based SfM pipeline advantageously complements point-based methods, giving more meaningful 3D representation for indoor scenarios.

A fast and robust ellipse-detection method based on sorted merging.

Abstract: A fast and robust ellipse-detection method based on sorted merging is proposed in this paper. This method first represents the edge bitmap approximately with a set of line segments and then gradually merges the line segments into elliptical arcs and ellipses. To achieve high accuracy, a sorted merging strategy is proposed: the merging degrees of line segments/elliptical arcs are estimated, and line segments/elliptical arcs are merged in descending order of the merging degrees, which significantly improves the merging accuracy. During the merging process, multiple properties of ellipses are utilized to filter line segment/elliptical arc pairs, making the method very efficient. In addition, an ellipse-fitting method is proposed that restricts the maximum ratio of the semimajor axis and the semiminor axis, further improving the merging accuracy. Experimental results indicate that the proposed method is robust to outliers, noise, and partial occlusion and is fast enough for real-time applications.

Planar Structures from Line Correspondences in a Manhattan World

Abstract: Traditional structure from motion is hard in indoor environments with only a few detectable point features These environments, however, have other useful characteristics: they often contain severable visible lines, and their layout typically conforms to a Manhattan world geometry We introduce a new algorithm to cluster visible lines in a Manhattan world, seen from two different viewpoints, into coplanar bundles This algorithm is based on the notion of “characteristic line”, which is an invariant of a set of parallel coplanar lines Finding coplanar sets of lines becomes a problem of clustering characteristic lines, which can be accomplished using a modified mean shift procedure The algorithm is computationally light and produces good results in real world situations

Robust Line Matching Based on Ray-Point-Ray Structure Descriptor

Abstract: In this paper, we propose a novel two-view line matching method through converting matching line segments extracted from two uncalibrated images to matching the introduced Ray-Point-Ray (RPR) structures. The method first recovers the partial connectivity of line segments through sufficiently exploiting the gradient map. To efficiently matching line segments, we introduce the Ray-Point-Ray (RPR) structure consisting of a joint point and two rays (line segments) connected to the point. Two sets of RPRs are constructed from the connected line segments extracted from two images. These RPRs are then described with the proposed SIFT-like descriptor for efficient initial matching to recover the fundamental matrix. Based on initial RPR matches and the recovered fundamental matrix, we propose a match propagation scheme consisting of two stages to refine and find more RPR matches. The first stage is to propagate matches among those initially formed RPRs, while the second stage is to propagate matches among newly formed RPRs constructed by intersecting unmatched line segments with those matched ones. In both stages, candidate matches are evaluated by comprehensively considering their descriptors, the epipolar line constraint, and the topological consistency with neighbor point matches. Experimental results demonstrate the good performance of the proposed method as well as its superiority to the state-of-the-art methods.

Semi-Global 3D Line Modeling for Incremental Structure-from-Motion

Abstract: Recovering 3D information from a single moving camera is a widely studied field in the area of computer vision (e.g. [1]). Most of these Structurefrom-Motion (SfM) approaches are based on so-called interest points (e.g. corners) in images, which can be accurately matched using powerful descriptors like SIFT [7]. Hence the output is usually a sparse 3D point cloud along with the camera poses for all successfully integrated images. While previous methods were only able to perform pose estimation and 3D reconstruction in an offline way, there are now more and more incremental SfM approaches available (e.g. [4]). Since conventional SfM approaches are based on interest points, the distribution of the obtained 3D points is usually not uniform throughout the whole reconstruction. This is due to the fact that such interest points are usually located on highly textured areas, but not on homogeneous regions or along edges. Since the result of SfM pipelines is often used as basis to generate a more dense result or for localization and navigation tasks, it would be beneficial to generate additional complementary 3D information in an efficient way. From a SfM point of view, using line segments is especially interesting for urban and indoor environments, where linear structures frequently occur. While interest points are located mostly on richly textured image locations, line segments usually mark the boundaries of objects. Hence, incorporating such features in an online SfM pipeline to create 3D line segments naturally leads to a more complete 3D representation of the underlying scene, which is beneficial for all kinds of subsequent applications. We propose a novel approach which generates 3D line models in a semi-global way directly on-the-fly, based solely on the output of a conventional incremental SfM pipeline. The goal of our method is to generate additional complementary 3D information to improve the sparse 3D representation of the scene. In this approach, we consider the SfM pipeline as a black box and do not interfere with the pose estimation procedure. We show that 3D line reconstructions can be obtained very efficiently by using purely geometric constraints, or by additionally incorporating appearance and collinearity information. Our approach enables accurate 3D reconstruction of texture-less as well as textured man-made objects, including complex structures such as wiry objects. Figure 1 shows a reconstruction result obtained by an incremental SfM system [4], followed by a surface generation method [5], with and without the usage of additional 3D line segments obtained by our proposed method. As we can see, additional 3D information significantly improves the completeness and overall appearance of the resulting reconstructions. For more technical details, we kindly refer to the full paper.

Image mosaic method based on SIFT features of line segment.

Abstract: This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

Systems and methods for processing image data associated with line detection

Abstract: In a particular embodiment, a method includes receiving line segment data at a processing core. The line segment data is associated with multiple candidate line segments associated with a first group of pixels of an image. The line segment data includes an angle value and/or a distance value for each of the multiple candidate line segments. The method further includes identifying, at the processing core, a set of line segments of the multiple candidate line segments by comparing angle values and/or distance values associated with the multiple candidate line segments. The method also includes determining, at the processing core, a representative line segment based on the set of line segments of the multiple candidate line segments. The method further includes storing, by the processing core, line segment information based on the representative line segment.

A Contrario Line Segment Detection

Abstract: The reliable detection of low-level image structures is an old and still challenging problem in computer vision. Thisbook leads a detailed tour through the LSD algorithm, a line segment detector designed to be fully automatic. Based on the a contrario framework, the algorithm works efficiently without the need of any parameter tuning. The design criteria are thoroughly explained and the algorithm's good and bad results are illustrated on real and synthetic images. The issues involved, as well as the strategies used, are common to many geometrical structure detection problems and some possible extensions are discussed.

Change detection using curve alignment

Abstract: Aspects of the disclosure relate to determining whether a feature of map information. For example, data identifying an object detected in a vehicle's environment and including location coordinates is received. This information is used to identify a corresponding feature from pre-stored map information based on a map location of the corresponding feature. The corresponding feature is defined as a curve and associated with a tag identifying a type of the corresponding object. A tolerance constraint is identified based on the tag. The curve is divided into two or more line segments. Each line segment has a first position. The first position of a line segment is changed in order to determine a second position based on the location coordinates and the tolerance constraint. A value is determined based on a comparison of the first position to the second position. This value indicates a likelihood that the corresponding feature has changed.

Line Matching and Pose Estimation for Unconstrained Model-to-Image Alignment

Abstract: This paper has two contributions in the context of line based camera pose estimation, 1) We propose a purely geometric approach to establish correspondence between 3D line segments in a given model and 2D line segments detected in an image, 2) We eliminate a degenerate case due to the type of rotation representation in arguably the best line based pose estimation method currently available. For establishing line correspondences we perform exhaustive search on the space of camera pose values till we obtain a pose (position and rotation) which is geometrically consistent with the given set of 2D, 3D lines. For this highly complex search we design a strategy which performs precomputations on the 3D model using separate set of constraints on position and rotation values. During runtime, the set of different rotation values are ranked independently and combined with each position values in the order of their ranking. Then successive geometric constraints which are much simpler when compared to computing reprojection error are used to eliminate incorrect pose values. We show that the ranking of rotation values reduces the number of trials needed by a huge factor and the simple geometric constraints avoid the need for computing the reprojection error in most cases. Though the execution time for the current MATLAB implementation is far from real time requirement, our method can be accelerated significantly by exploiting simplicity and parallelizability of the operations we employ. For eliminating the degenerate case in the state of art pose estimation method, we reformulate the rotation representation. We use unit quaternions instead of CGR parameters used by the method.