Showing papers on "Line segment published in 2015"

Line segment extraction for large scale unorganized point clouds

Abstract: Line segment detection in images is already a well-investigated topic, although it has received considerably less attention in 3D point clouds. Benefiting from current LiDAR devices, large-scale point clouds are becoming increasingly common. Most human-made objects have flat surfaces. Line segments that occur where pairs of planes intersect give important information regarding the geometric content of point clouds, which is especially useful for automatic building reconstruction and segmentation. This paper proposes a novel method that is capable of accurately extracting plane intersection line segments from large-scale raw scan points. The 3D line-support region, namely, a point set near a straight linear structure, is extracted simultaneously. The 3D line-support region is fitted by our Line-Segment-Half-Planes (LSHP) structure, which provides a geometric constraint for a line segment, making the line segment more reliable and accurate. We demonstrate our method on the point clouds of large-scale, complex, real-world scenes acquired by LiDAR devices. We also demonstrate the application of 3D line-support regions and their LSHP structures on urban scene abstraction.

Dual-Feature Warping-Based Motion Model Estimation

Abstract: To break down the geometry assumptions of traditional motion models (e.g., homography, affine), warping-based motion model recently becomes very popular and is adopted in many latest applications (e.g., image stitching, video stabilization). With high degrees of freedom, the accuracy of model heavily relies on data-terms (keypoint correspondences). In some low-texture environments (e.g., indoor) where keypoint feature is insufficient or unreliable, the warping model is often erroneously estimated. In this paper we propose a simple and effective approach by considering both keypoint and line segment correspondences as data-term. Line segment is a prominent feature in artificial environments and it can supply sufficient geometrical and structural information of scenes, which not only helps guild to a correct warp in low-texture condition, but also prevents the undesired distortion induced by warping. The combination aims to complement each other and benefit for a wider range of scenes. Our method is general and can be ported to many existing applications. Experiments demonstrate that using dual-feature yields more robust and accurate result especially for those low-texture images.

Accurate and Robust Line Segment Extraction Using Minimum Entropy With Hough Transform

Abstract: The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment’s length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment’s normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment’s midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances.

CannyLines: A parameter-free line segment detector

Abstract: 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.

Automatic Optical-to-SAR Image Registration by Iterative Line Extraction and Voronoi Integrated Spectral Point Matching

Abstract: Automatic optical-to-SAR image registration is considered as a challenging problem because of the inconsistency of radiometric and geometric properties. Feature-based methods have proven to be effective; however, common features are difficult to extract and match, and the robustness of those methods strongly depends on feature extraction results. In this paper, a new method based on iterative line extraction and Voronoi integrated spectral point matching is developed. The core idea consists of three aspects: 1) An iterative procedure that combines line segment extraction and line intersections matching is proposed to avoid registration failure caused by poor feature extraction. 2) A multilevel strategy of coarse-to-fine registration is presented. The coarse registration aims to preserve main linear structures while reducing data redundancy, thus providing robust feature matching results for fine registration. 3) Voronoi diagram is introduced into spectral point matching to further enhance the matching accuracy between two sets of line intersection. Experimental results show that the proposed method improves the matching performance. Compared with previous methods, the proposed algorithm can effectively and robustly generate sufficient reliable point pairs and provide accurate registration.

An Efficient Approach for Automatic Rectangular Building Extraction From Very High Resolution Optical Satellite Imagery

Abstract: This letter presents a new approach for rapid automatic building extraction from very high resolution (VHR) optical satellite imagery. The proposed method conducts building extraction based on distinctive image primitives such as lines and line intersections. The optimized framework consists of three stages: First, a developed edge-preserving bilateral filter is adopted to reduce noise and enhance building edge contrast for preprocessing. Second, a state-of-the-art line segment detector called EDLines is introduced for the real-time accurate extraction of building line segments. Finally, we present a graph search-based perceptual grouping approach to hierarchically group previously detected line segments into candidate rectangular buildings. The recursive process was improved through the efficient examination of geometrical information with line linking and closed contour search, in order to obtain more reasonable omission and commission rate in building contour grouping. Extensive experiments performed on VHR optical QuickBird imageries justify the effectiveness and robustness of the proposed linear-time procedure with an overall accuracy of 80.9% and completeness of 87.3%. This method does not require user intervention and thereby has the potential to be adopted in online applications and industrial use in the near future.

A Novel Airport Detection Method via Line Segment Classification and Texture Classification

Abstract: Airports are one of the most important traffic facilities; thus, airport detection is of great significance in economic and military construction. This letter proposes a novel method for airport detection, with the entire algorithm based on line segment classification and texture classification. First, a fast line segment detector is applied to extract the line segments in images and compute the features of these line segments. Then, the line segments are discriminated by a trained runway line classifier, and the regions of interest (ROIs) are extracted from the line segments, which are classified as runway lines. Finally, whether the ROI is actually an airport is determined by analyzing the classification results of the image blocks. This method is unique in terms of the computing of line segment features and line segment classification. Experimental results demonstrate the effectiveness and robustness of the proposed method.

Curve fitting and optimal interpolation for CNC machining under confined error using quadratic B-splines

Abstract: In CNC machining, fitting the polyline machining tool path with parametric curves can be used for smooth tool path generation and data compression In this paper, an optimization problem is solved to find a quadratic B-spline curve whose Hausdorff distance to the given polyline tool path is within a given precision Furthermore, adopting time parameter for the fitting curve, we combine the usual two stages of tool path generation and optimal velocity planning to derive a one-step solution for the CNC optimal interpolation problem of polyline tool paths Compared with the traditional decoupled model of curve fitting and velocity planning, experimental results show that our method generates a smoother path with minimal machining time The explicit Hausdorff distance of a line segment and a quadratic curve is givenG01 codes can be fitted by quadratic B-splines with confined errorWe combine the tool path generating and optimal velocity planning in one stepWe simulate the manufacture process with our proposed method

A new method based on hough transform for quick line and circle detection

Abstract: Hough transform (HT) is a typical method to detect or segment geometry objects from images. In this paper, we study the principle of Hough Transform and its mathematical expressions, and try to use a new approach based on Hough transform for quick line and circle detection in image processing. Our method accurately detected some simple graphics, such as straight line of different direction, circles of different detection, thickness and different number. The results show that our method is less memory consumption and calculated fast, which could be applied for line detection and segmentation in 3D ultrasonic image.

Urban road localization by using multiple layer map matching and line segment matching

Abstract: In recent years, automated vehicle researches move on to the next stage, that is, auto-driving experiments on public roads. This study focuses on how to realize accurate localization based on the use of Lidar data and precise map. On different roads such as urban roads and expressways, the observed information of surrounding is significantly different. For example, on the urban roads, many buildings can be observed around the upper part of the vehicle. Such observation realizes accurate map matching. On the other hand, the upper part has no specific observation on the expressway. Therefore, it is necessary to observe the lower part for the map matching. To adapt the situation changes, we propose a localization method based on self-adaptive multi-layered scan matching and road line segment matching. The main idea is to effectively match the features observed from different heights and to improve the results by applying the line segment matching in certain scenes. Localization experiments show the ability to estimate accurate vehicle pose in urban driving.

Real time rectangular document detection on mobile devices

Abstract: In this paper we propose an algorithm for real-time rectangular document borders detection in mobile device based applications. The proposed algorithm is based on combinatorial assembly of possible quadrangle candidates from a set of line segments and projective document reconstruction using the known focal length. Fast Hough Transform is used for line detection. 1D modification of edge detector is proposed for the algorithm.

Descriptor free visual indoor localization with line segments

Abstract: We present a novel view on the indoor visual localization problem, where we avoid the use of interest points and associated descriptors, which are the basic building blocks of most standard methods. Instead, localization is cast as an alignment problem of the edges of the query image to a 3D model consisting of line segments. The proposed strategy is effective in low-textured indoor environments and in very wide baseline setups as it overcomes the dependency of image descriptors on textures, as well as their limited invariance to view point changes. The basic features of our method, which are prevalent indoors, are line segments. As we will show, they allow for defining an efficient Chamfer distance-based aligning cost, computed through integral contour images, incorporated into a first-best-search strategy. Experiments confirm the effectiveness of the method in terms of both, accuracy and computational complexity.

Computer-based convex polygon field unmanned aerial vehicle spraying operation route planning method

Abstract: The invention discloses a computer-based convex polygon field unmanned aerial vehicle spraying operation route planning method. The method includes the steps: firstly, solving a linear equation of each edge of a polygon; secondly, finding out the longest edge of the polygon; thirdly, respectively solving the distance from each endpoint of a field boundary to the longest point, and finding out the point farthest from the longest edge; fourthly, selecting a group of equidistant parallel lines parallel to the longest edge between the longest edge and the farthest point; fifthly, solving two intersections of each parallel line and the field boundary and storing result sets into corresponding groups; sixthly, connecting the two intersections of each parallel line and the polygon into a line segment, and then cutting off the parts, located outside the field operation area, of the group of parallel lines so as to obtain a group of parallel line segments located in the field operation area; finally, planning out a route. The method is simple algorithm and good in universality, efficiency of unmanned helicopters for executing spraying operation to irregular fields can be improved, and energy consumption of flight is lowered.

Robustness to lighting variations: An RGB-D indoor visual odometry using line segments

Abstract: Large lighting variation challenges all visual odometry methods, even with RGB-D cameras. Here we propose a line segment-based RGB-D indoor odometry algorithm robust to lighting variation. We know line segments are abundant indoors and less sensitive to lighting change than point features. However, depth data are often noisy, corrupted or even missing for line segments which are often found on object boundaries where significant depth discontinuities occur. Our algorithm samples depth data along line segments, and uses a random sample consensus approach to identify correct depth and estimate 3D line segments. We analyze 3D line segment uncertainties and estimate camera motion by minimizing the Mahalanobis distance. In experiments we compare our method with two state-of-the-art methods including a keypoint-based approach and a dense visual odometry algorithm, under both constant and varying lighting. Our method demonstrates superior robustness to lighting change by outperforming the competing methods on 6 out of 8 long indoor sequences under varying lighting. Meanwhile our method also achieves improved accuracy even under constant lighting when tested using public data.

Optimal clustering of a pair of irregular objects

Abstract: Cutting and packing problems arise in many fields of applications and theory. When dealing with irregular objects, an important subproblem is the identification of the optimal clustering of two objects. Within this paper we consider a container (rectangle, circle, convex polygon) of variable sizes and two irregular objects bounded by circular arcs and/or line segments, that can be continuously translated and rotated. In addition minimal allowable distances between objects and between each object and the frontier of a container, may be imposed. The objects should be arranged within a container such that a given objective will reach its minimal value. We consider a polynomial function as the objective, which depends on the variable parameters associated with the objects and the container. The paper presents a universal mathematical model and a solution strategy which are based on the concept of phi-functions and provide new benchmark instances of finding the containing region that has either minimal area, perimeter or homothetic coefficient of a given container, as well as finding the convex polygonal hull (or its approximation) of a pair of objects.

Line matching based on planar homography for stereo aerial images

Abstract: We propose an efficient line matching algorithm for a pair of calibrated aerial photogrammetric images, which makes use of sparse 3D points triangulated from 2D point feature correspondences to guide line matching based on planar homography. Two different strategies are applied in the proposed line matching algorithm for two different cases. When three or more points can be found coplanar with the line segment to be matched, the points are used to fit a plane and obtain an accurate planar homography. When one or two points can be found, the approximate terrain plane parallel to the line segment is utilized to compute an approximate planar homography. Six pairs of rural or urban aerial images are used to demonstrate the efficiency and validity of the proposed algorithm. Compared with line matching based on 2D point feature correspondences, the proposed method can increase the number of correctly matched line segments. In addition, compared with most line matching methods that do not use 2D point feature correspondences, the proposed method has better efficiency, although it obtains fewer matches. The C/C++ source code for the proposed algorithm is available at http://services.eng.uts.edu.au/∼sdhuang/research.htm .

3D Surface Reconstruction from Point-and-Line Cloud

Abstract: We present a method for reconstructing 3D surface as triangular meshes from imagery. The surface reconstruction requires 3D point cloud for composing vertices of triangle meshes. A standard approach uses incremental structure from motion (SfM) to obtain camera poses and sparse 3D point cloud that are given based on 2D key-point matching. As the 3D surface directly reconstructed from the sparse 3D point cloud often lack detail of objects, multiple-view stereo (MVS) is commonly used to generate dense 3D point cloud. A known problem with the densification is that MVS generates many small patches even for planar flat objects that degrade the quality of surface model. Using dense 3D point cloud also requires high memory capacity for visualization. In this work, we propose to reconstruct 3D surface using sparse 3D point cloud generated by SfM and 3D line segments (3D line cloud) computed from multiple views since these two elements can complement well for representing man-made structures. The proposed method extends the tetrahedra-carving method as it can use 3D point-and-line cloud under the global optimization framework. We demonstrate that the proposed method can efficiently produce surface models whose quality are at least as good as the baseline method using dense 3D point cloud.

Place recognition based on matching of planar surfaces and line segments

Abstract: This paper considers the potential of using three-dimensional 3D planar surfaces and line segments detected in depth images for place recognition. A place recognition method is presented that is based on matching sets of surface and line features extracted from depth images provided by a 3D camera to features of the same type contained in a previously created environment model. The considered environment model consists of a set of local models representing particular locations in the modeled environment. Each local model consists of planar surface segments and line segments representing the edges of objects in the environment. The presented method is designed for indoor and urban environments. A computationally efficient pose hypothesis generation approach is proposed that ranks the features according to their potential contribution to the pose information, thereby reducing the time needed for obtaining accurate pose estimation. Furthermore, a robust probabilistic method for selecting the best pose hypothesis is proposed that allows matching of partially overlapping point clouds with gross outliers. The proposed approach is experimentally tested on a benchmark dataset containing depth images acquired in the indoor environment with changes in lighting conditions and the presence of moving objects. A comparison of the proposed method to FAB-MAP and DLoopDetector is reported.

Image processing apparatus, image processing method and program

Abstract: To specify a contour to be detected even when there are a plurality of candidates of the contour on the periphery of a photographing target, an image processing apparatus comprises: a determining unit which detects a plurality of candidate points being the candidates of the contour of a subject based on distance image information of the subject in an image, and determines an inspection-target area in the image based on the detected candidate points; and a specifying unit which detects line segments existing in the inspection-target area determined by the determining unit, based on luminance information of the inspection-target area, and specifies the line segment being the contour of the subject based on the candidate point from the detected line segments

An optimal estimate for electric fields on the shortest line segment between two spherical insulators in three dimensions

Abstract: We consider a gradient estimate for a conductivity problem whose inclusions are two neighboring insulators in three dimensions. When inclusions with an extreme conductivity (insulators or perfect conductors) are closely located, the gradient can be concentrated in between inclusions and then becomes arbitrarily large as the distance between inclusions approaches zero. The gradient estimate in between insulators in three dimensions has been regarded as a challenging problem, while the optimal blow-up rates in terms of the distance were successfully obtained for the other extreme conductivity problems in two and three dimensions, and are attained on the shortest line segment between inclusions. In this paper, we establish upper and lower bounds of gradients on the shortest line segment between two insulating unit spheres in three dimensions. These bounds present the optimal blow-up rate of gradient on the line segment which is substantially different from the rates in the other problems.

Generating Moving Regions from Snapshots of Complex Regions

Abstract: Moving regions are a form of spatiotemporal data in which a region changes in shape and/or position over time. In many fields, moving regions representing real-world phenomena are collected using sensors that take temporally encoded snapshots of regions. We provide a novel algorithm that creates a moving region between any two complex regions. The proposed algorithm has worst-case time bounds of O;(n;2), but can use approximation techniques to achieve O(nlgn) in practice, space bounds of O;(n;), and output size bounded by O;(n;) (where n; is the number of line segments that define the boundaries of the regions).

Descriptor generation method and apparatus

Abstract: The invention provides a descriptor generation method and apparatus. The method comprises the steps of: extracting the profile of an input image, and extracting at least one line segment according to the profile; determining a main direction of a detected characteristic point according to the direction of the line segment; rotating a neighborhood of the characteristic point according to the main direction; and describing the characteristic point according to the rotated neighborhood so as to generate a descriptor of the characteristic point. In the process, the determination of the main direction is mainly dependent on the direction of the line segment of the neighborhood of the characteristic point, and in a multimodal image, the line segment is more stable relative to the point, so that the descriptor of the characteristic point corresponding to each image has repeatability, the accuracy of the descriptor is improved, and further the registration accuracy of the multimodal image is improved.

Shortest Paths for Line Segments

Abstract: We study the problem of shortest paths for a line segment in the plane. As a measure of the distance traversed by a path, we take the average curve length of the orbits of prescribed points on the line segment. This problem is nontrivial even in free space (i.e., in the absence of obstacles). We characterize all shortest paths of the line segment moving in free space under the measured 2, the average orbit length of the two endpoints. The problem ofd 2 optimal motion has been solved by Gurevich and also by Dubovitskij, who calls it Ulam's problem. Unlike previous solutions, our basic tool is Cauchy's surface-area formula. This new approach is relatively elementary, and yields new insights.

New results on stabbing segments with a polygon

Abstract: We consider a natural variation of the concept of stabbing a set of segments with a simple polygon: a segment s is stabbed by a simple polygon P if at least one endpoint of s is contained in P, and a segment set S is stabbed by P if P stabs every element of S. Given a segment set S, we study the problem of finding a simple polygon P stabbing S in a way that some measure of P (such as area or perimeter) is optimized. We show that if the elements of S are pairwise disjoint, the problem can be solved in polynomial time. In particular, this solves an open problem posed by Loffler and van Kreveld [Algorithmica 56(2), 236-269 (2010)] [16] about finding a maximum perimeter convex hull for a set of imprecise points modeled as line segments. Our algorithm can also be extended to work for a more general problem, in which instead of segments, the set S consists of a collection of point sets with pairwise disjoint convex hulls. We also prove that for general segments our stabbing problem is NP-hard.

Lane line detection method

Abstract: The invention discloses a lane line detection method, and belongs to the technical field of image processing. The lane line detection method comprises the steps that first a pre-processed image is horizontally divided into K edge image blocks, the height scale of the edge image block at the bottom end and the entire image is [1/4,1/3], and two or more pairs of straight lines which are closest to a vanishing line are determined under the determined vanishing line to be used as candidate line pairs of the current edge image block; then the weights of the candidate line pairs are calculated, the straight line with the maximum weight of the edge image blocks of the candidate line pairs is taken as the only lane line segment pair of the current edge image block based on the weights of the candidate line pairs; at last, the lane line of a current frame image is output based on the end points of the lane line segment pairs of all the edge image blocks. The method is used for detecting lane lines, the method is not sensitive to initialization parameters, the robustness of detection is high, and a good detecting effect can be achieved under the bad conditions of dark light, lane line loss, shadows and the like.

Detecting Point Pattern of Multiple Line Segments Using Hough Transformation

Abstract: Surface defects in manufacturing often exhibit particular spatial patterns. These patterns contain valuable information about the manufacturing process and can help to identify potential root causes. In this paper, we present a new method to detect the point patterns that consist of multiple line segments. The basic idea is that by using the Hough transformation, we convert the point pattern detection problem into a simple point matching problem. Compared with the existing point pattern matching methods, the proposed method does not require training data and is relatively easy to implement and compute. The details of the detection algorithm are presented and the parameter selection and performance evaluation of this method are investigated. Case studies are presented to validate the effectiveness of this method.