Showing papers on "Line segment published in 2018"

A Novel Linelet-Based Representation for Line Segment Detection

Abstract: This paper proposes a method for line segment detection in digital images We propose a novel linelet-based representation to model intrinsic properties of line segments in rasterized image space Based on this, line segment detection, validation, and aggregation frameworks are constructed For a numerical evaluation on real images, we propose a new benchmark dataset of real images with annotated lines called YorkUrban-LineSegment The results show that the proposed method outperforms state-of-the-art methods numerically and visually To our best knowledge, this is the first report of numerical evaluation of line segment detection on real images

Airport Detection in Large-Scale SAR Images via Line Segment Grouping and Saliency Analysis

Abstract: The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods locate airports by connecting pairs of line segments or directly applying saliency analysis to an entire SAR image. These methods are either time-consuming or can easily result in false detection. Considering these issues, a method using line segment grouping and saliency analysis is proposed in this letter. First, line segments are obtained via an improved line segment detector (LSD). After line segment grouping, airport support regions are extracted. Then, selective nonmaximum suppression is proposed to obtain potential airport regions. Finally, airport regions are located by false alarm control and saliency analysis. Experiments on large-scale SAR images prove that our proposed algorithm has a better performance and higher efficiency in airport detection compared with traditional methods.

Near Time-Optimal S -Curve Velocity Planning for Multiple Line Segments Under Axis Constraints

Abstract: To drive the machine tool along a curve, a commercial computer-aid design and computer-aided manufacturing system usually breaks the curve into a set of line segments. This paper provides a systematic way for generating smooth and fast machining velocity planning for contouring line segments in a machine tool. It is especially useful for mass manufacturing. In this paper, the particle swarm optimization algorithm is employed to optimize the design parameters. The algorithm allows for as many motion limits as possible, not only the limits along the path, but those on the axes, as well. It can automatically determine the existence of acceleration, constant speed, and deceleration zones of the S -curve velocity profile. Also, it can determine the optimal corner speed with a given corner error. The computation time of the algorithm is fast so that it allows for online operation. Simulation and experimental results demonstrate that machine tools using the proposed algorithm go fast where possible and slow down where needed, dramatically increasing the productivity.

Learning Attraction Field Representation for Robust Line Segment Detection.

Abstract: This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three components: (i) A region-partition map in which every pixel is assigned to one and only one line segment; (ii) An attraction field map in which every pixel in a partition region is encoded by its 2D projection vector w.r.t. the associated line segment; and (iii) A squeeze module which squashes the attraction field to a line segment map that almost perfectly recovers the input one. By leveraging the duality, we learn ConvNets to compute the attraction field maps for raw in-put images, followed by the squeeze module for LSD, in an end-to-end manner. Our method rigorously addresses several challenges in LSD such as local ambiguity and class imbalance. Our method also harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution. In experiments, our method is tested on the WireFrame dataset and the YorkUrban dataset with state-of-the-art performance obtained. Especially, we advance the performance by 4.5 percents on the WireFrame dataset. Our method is also fast with 6.6~10.4 FPS, outperforming most of existing line segment detectors.

Arc-support Line Segments Revisited: An Efficient and High-quality Ellipse Detection

Abstract: Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images remains a challenge. In this paper, we propose a valuable industry-oriented ellipse detector by arc-support line segments, which simultaneously reaches high detection accuracy and efficiency. To simplify the complicated curves in an image while retaining the general properties including convexity and polarity, the arc-support line segments are extracted, which grounds the successful detection of ellipses. The arc-support groups are formed by iteratively and robustly linking the arc-support line segments that latently belong to a common ellipse. Afterward, two complementary approaches, namely, locally selecting the arc-support group with higher saliency and globally searching all the valid paired groups, are adopted to fit the initial ellipses in a fast way. Then, the ellipse candidate set can be formulated by hierarchical clustering of 5D parameter space of initial ellipses. Finally, the salient ellipse candidates are selected and refined as detections subject to the stringent and effective verification. Extensive experiments on three public datasets are implemented and our method achieves the best F-measure scores compared to the state-of-the-art methods. The source code is available at this https URL.

A line segment extraction algorithm using laser data based on seeded region growing

Abstract: This article presents a novel line segment extraction algorithm using two-dimensional (2D) laser data, which is composed of four main procedures: seed-segment detection, region growing, overlap reg...

Accurate Outline Extraction of Individual Building From Very High-Resolution Optical Images

Abstract: This letter presents a novel approach for extracting accurate outlines of individual buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of building roofs and outperforms the state-of-the-art method.

Robust Lane-Detection Method for Low-Speed Environments.

Abstract: Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.

Coarse-Fine-Stitched: A Robust Maritime Horizon Line Detection Method for Unmanned Surface Vehicle Applications.

Abstract: The horizon line has numerous applications for an unmanned surface vehicles (USV), such as autonomous navigation, attitude estimation, obstacle detection and target tracking. However, maritime horizon line detection is quite a challenging problem. The pixel points of the horizon line features are far fewer than the pixel points of the entire image, on the one hand. Conversely, the detection results might be impacted negatively by the complex maritime environment, waves, light changing, and partial occlusions due to maritime vessels or islands, for example. To solve these problems, a robust horizon line detection method named coarse-fine-stitched (CFS) is proposed in this paper. First, in the coarse step of CFS, a line segment detection approach using gradient features is applied to build a line candidate pool, which probably contains many false detection results. Then, hybrid feature filtering is designed to pick the horizon line segments from the pool in the fine step. Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.

Adaptive Obstacle Detection for Mobile Robots in Urban Environments Using Downward-Looking 2D LiDAR.

Abstract: Environment perception is important for collision-free motion planning of outdoor mobile robots. This paper presents an adaptive obstacle detection method for outdoor mobile robots using a single downward-looking LiDAR sensor. The method begins by extracting line segments from the raw sensor data, and then estimates the height and the vector of the scanned road surface at each moment. Subsequently, the segments are divided into either road ground or obstacles based on the average height of each line segment and the deviation between the line segment and the road vector estimated from the previous measurements. A series of experiments have been conducted in several scenarios, including normal scenes and complex scenes. The experimental results show that the proposed approach can accurately detect obstacles on roads and could effectively deal with the different heights of obstacles in urban road environments.

A locally optimal transition method with analytical calculation of transition length for computer numerical control machining of short line segments

Abstract: Line segments, or G01 codes, generated by computer-aided manufacturing softwares are the most widely used toolpath format for computer numerical control systems. The linear toolpath normally consists of thousands of short line segments due to the high-accuracy requirement of the machined parts. Due to the tangential and the curvature discontinuities at the junction of two segments, the feedrates at the start and the end points of line segment have to be slowed down. In order to increase the feedrates along short line segments, a locally optimal transition method is proposed, which uses a two-step strategy to generate a blended toolpath composed of cubic Bezier curves and line segments. In the first step, the optimal proportional coefficient is represented as the function of the angle between two adjacent line segments, which can be employed to minimize the curvature variation energy of the cubic Bezier curve. In the second step, the local optimization model with the aim to minimize the sum of two curvatur...

Incremental 3D Line Segment Extraction from Semi-dense SLAM

Abstract: Although semi-dense Simultaneous Localization and Mapping (SLAM) has been becoming more popular over the last few years, there is a lack of efficient methods for representing and processing their large scale point clouds. In this paper, we propose using 3D line segments to simplify the point clouds generated by semi-dense SLAM. Specifically, we present a novel incremental approach for 3D line segment extraction. This approach reduces a 3D line segment fitting problem into two 2D line segment fitting problems and takes advantage of both images and depth maps. In our method, 3D line segments are fitted incrementally along detected edge segments via minimizing fitting errors on two planes. By clustering the detected line segments, the resulting 3D representation of the scene achieves a good balance between compactness and completeness. Our experimental results show that the 3D line segments generated by our method are highly accurate. As an application, we demonstrate that these line segments greatly improve the quality of 3D surface reconstruction compared to a feature point based baseline.

A local smoothing interpolation method for short line segments to realize continuous motion of tool axis acceleration

Abstract: In traditional processing, a large number of G01 blocks are adopted to discretize free surface or curve for NC machining. But, the continuity of G01 line segments is only C0, which may lead to discontinuity of axis acceleration, resulting in the frequent fluctuation of tool motion at the junctions in high-speed machining, deteriorating the quality of work piece, and reducing processing efficiency. To solve this problem, a local smoothing interpolation method is proposed in this paper. At first, the analytic relationship between the continuity of the trajectory and the continuity of the axes motion is first systematically described by formula. Based on this relationship, a local smoothing algorithm and a feed-rate scheduling method are proposed to generate a C2 continuous tool path motion with axis-acceleration continuity. The local smoothing algorithm smoothes the corners of G01 blocks by the cubic B-spline according to the cornering error tolerance specified by the user. After the feed rate at critical points of smoothed tool path was determined by a modified bidirectional scanning algorithm by considering constrains of chord error and kinematic property, an iterative S-shape feed rate scheduling is employed to minimize residual distance caused by round of time while ensuring the continuity of feed rate and acceleration. Then, a look-ahead interpolation strategy combined with smoothing algorithm and feed-rate scheduling as mentioned is proposed for real-time interpolation of short line segments. At last, simulations are conducted to verify the effectiveness of the proposed methods. Compared with the traditional G01 interpolation, it can significantly improve the processing efficiency and shorten the processing time within error tolerance.

Splitting method for elliptic equations with line sources.

Abstract: In this paper, we study the mathematical structure and numerical approximation of elliptic problems posed in a (3D) domain $\Omega$ when the right-hand side is a (1D) line source $\Lambda$. The analysis and approximation of such problems is known to be non-standard as the line source causes the solution to be singular. Our main result is a splitting theorem for the solution; we show that the solution admits a split into an explicit, low regularity term capturing the singularity, and a high-regularity correction term $w$ being the solution of a suitable elliptic equation. The splitting theorem states the mathematical structure of the solution; in particular, we find that the solution has anisotropic regularity. More precisely, the solution fails to belong to $H^1$ in the neighbourhood of $\Lambda$, but exhibits piecewise $H^2$-regularity parallel to $\Lambda$. The splitting theorem can further be used to formulate a numerical method in which the solution is approximated via its correction function $w$. This approach has several benefits. Firstly, it recasts the problem as a 3D elliptic problem with a 3D right-hand side belonging to $L^2$, a problem for which the discretizations and solvers are readily available. Secondly, it makes the numerical approximation independent of the discretization of $\Lambda$; thirdly, it improves the approximation properties of the numerical method. We consider here the Galerkin finite element method, and show that the singularity subtraction then recovers optimal convergence rates on uniform meshes, i.e., without needing to refine the mesh around each line segment. The numerical method presented in this paper is therefore well-suited for applications involving a large number of line segments. We illustrate this by treating a dataset (consisting of $\sim 3000$ line segments) describing the vascular system of the brain.

Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes.

Abstract: In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features-point and line segments-have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.

Manuscript Text Line Detection and Segmentation Using Second-Order Derivatives

Abstract: In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.

Fast and Resource-Efficient Hardware Implementation of Modified Line Segment Detector

Abstract: Lines are significant features enclosing high-level information in an image. The line segment Detector (LSD) Algorithm with low error rate is a widely used method to extract lines in images effectively and accurately. However, the algorithm on PC performs too costly both in time and resources for the real-time video processing. This paper provides a fast and resource-efficient hardware implementation solution for a modified LSD algorithm on Field Programmable Gate Arrays (FPGA) for real-time line detection. The task-level pipeline structures are exploited fully in a stream process mapped to the hardware architecture free of frame buffer. Our proposed hardware implementation processes in a stream-in–stream-out manner with little consumption of the on-chip block RAM to store intermediate values. We first employ hardware Gaussian filter and adjust Canny edge detection to obtain an edge map at single-pixel width. Then, a novel structure of region growing model based on dynamic rooted tree is used to detect line segment regions accurately with a latency of only a few rows of pixels. The low cost in time, on-chip resources, and power consumption makes our proposed algorithm suitable for portable real-time streaming video processing applications using line segment features, such as Lane departure warning systems. It can also be applied in real-time machine vision systems that use line segments information for further recognition or stereo correspondence and many others. The proposed algorithm is synthesized and tested on XC7Z020 FPGA with high reliability, accuracy speed, and low cost in both resources and energy.

Towards an Optimal Method for Dynamic Planar Point Location

Abstract: We describe a fully dynamic linear-space data structure for point location in connected planar subdivisions, or more generally vertical ray shooting among nonintersecting line segments, that suppor...

Spectral Clustering of Straight-Line Segments for Roof Plane Extraction From Airborne LiDAR Point Clouds

Abstract: This letter presents a novel approach to automated extraction of roof planes from airborne light detection and ranging data based on spectral clustering of straight-line segments. The straight-line segments are derived from laser scan lines, and 3-D line geometry analysis is employed to identify coplanar line segments so as to avoid skew lines in plane estimation. Spectral analysis reveals the spectrum of the adjacency matrix formed by the straight-line segments. Spectral clustering is then performed in feature space where the clusters are more prominent, resulting in a more robust extraction of roof planes. The proposed approach has been tested on ISPRS benchmark data sets, with the results showing high quality in terms of completeness, correctness, and geometrical accuracy, thus confirming that the proposed approach can extract roof planes both accurately and efficiently.

Methods and systems for detecting, recognizing, and localizing pallets

Abstract: Example implementations may relate methods and systems for detecting, recognizing, and localizing pallets. For instance, a computing system may receive sensor data representing aspects of an environment, and identify a set of edge points in the sensor data. The computing system may further determine a set of line segments from the set of edge points where each line segment may fit to a subset of the set of edge points. Additionally, the computing system may also filter the set of line segments to exclude line segments that have a length outside a height range and a width range associated with dimensions of a pallet template, and identify, from the filtered set of line segments, a subset of line segments that align with the pallet template. Based on the identified subset of line segments, the computing system may determine a pose of a pallet in the environment.

Stereo Visual-Inertial SLAM With Points and Lines

Abstract: Visual–inertial SLAM systems achieve highly accurate estimation of camera motion and 3-D representation of the environment. Most of the existing methods rely on points by feature matching or direct image alignment using photo-consistency constraints. The performance of these methods usually decreases when facing low textured environments. In addition, lines are also very common in man-made environments and provide geometrical structure information of the environment. In this paper, we increase the robustness of visual–inertial SLAM system to handle these situations by using both points and lines. Our method, implemented based on ORB-SLAM2, makes the combination of points, lines, and IMU measurements in an effective way by selecting keyframes very carefully and handling the outlier lines efficiently. The cost function of bundle adjustment is formed by point, line reprojection errors, and IMU residual errors. We derive the Jacobian matrices of line reprojection errors with respect to the 3-D endpoints of line segments and camera motion. Loop closure detection is decided by both point and line features using the bag-of-words approach. Our method is evaluated on the public EuRoc dataset and compared with the state-of-the-art visual–inertial fusion methods. Experimental results show that our method achieves the highest accuracy on most of testing sequences, especially in some challengeable situations such as low textured and illumination changing environments.

Effective Road Lane Detection and Tracking Method Using Line Segment Detector

Abstract: In this paper, we present an effective lane detection and tracking system using a fusion of Line Segment Detector (LSD) and Kalman filter. We employ line segment as low-level features to detect lane markings on structured road. Firstly, we segment the road surface region through adaptive method, and then a different grayscale method is used to achieve strong contrast than the previous studies. Secondly, we apply LSD algorithm on them and remove incorrect line segments in an adaptive way. Finally, two Kalman filters are used to track the virtual boundary points, which also be applied to predict the next processed region of interests (ROIs). Meanwhile, real-time performance can be better achieved. Experiments are conducted on several open datasets which are collected in real-world scenarios, such as Caltech dataset. The results demonstrate the efficiency and the robustness of our proposed method.

Gestaltic Grouping of Line Segments

Abstract: Using simple grouping rules in Gestalt theory, one may detect higher level features (geometric structures) in an image from elementary features. By recursive grouping of already detected geometric structures a bottom-up pyramid could be built that extracts increasingly complex geometric features from the input image. Taking advantage of the (recent) advances in reliable line segment detectors, in this paper, we propose three feature detectors along with their corresponding detailed algorithms that constitute one step up in this pyramid. For any digital image, our unsupervised algorithm computes three classic Gestalts from the set of predetected line segments: good continuations, non-local alignments, and bars. The methodology is based on a common stochastic a contrario model yielding three simple detection formulas, characterized by their number of false alarms. This detection algorithm is illustrated on several digital images.

Real-Time Lane Detection-Based Line Segment Detection

Abstract: This paper introduces a robust algorithm for real-time lane detection using the lane markers in urban streets or highway roads. It is based on applying Region of Interest (ROI) on the input image of the road from a calibrated camera in the front of the car, generating the top view of the image using Inverse Perspective Mapping (IPM), applying the core algorithm Line Segment Detection (LSD) which is followed by post-processing steps. Applying curve fitting to the line segments to get the right and left lines or curves. Finally, to get the output stream inverse IPM is applied. The proposed algorithm can detect the road lanes discriminating dashed and solid road lanes, straight and curved road lanes overcoming the shadow effect challenge with real-time performance 70 frames per second.

Method and system for crop recognition and boundary delineation

Abstract: A computer-implemented method for determining farm boundary delineations within a target geographic area, comprising extracting data from pixels of a satellite image of the target geographic area, evaluating the data using a classification algorithm to generate one or more line segments between adjacent pixels, the one or more line segments being representative of a portion of a boundary delineation, connecting the one or more line segments to an adjacent line segment to form a boundary delineation defining at least one parcel of land within the target geographic area, and generating a boundary delineation map including the boundary delineation.

Robot synchronous positioning and map construction method and system

Abstract: The invention discloses a robot synchronous positioning and map construction method based on line segment features and line segment vanishing points, and the robot synchronous positioning and map construction method is applied to the positioning and map construction of a robot in an unknown environment. The robot synchronous positioning and map construction method comprises the steps of: extracting line segment features, intersection points and vanishing points from images, establishing line segment matching between the images according to the intersection points, estimating camera poses according to line segment matching results, and selecting a new keyframe; and inserting the keyframe into a map, calculating three-dimensional coordinates of the newly-added line segment features, performing cluster adjustment on local map, and removing outlier observation; and performing closed-loop detection and global map optimization based on the intersection points. The invention further discloses a synchronous positioning and map construction system. The robot synchronous positioning and map construction method and the robot synchronous positioning and map construction system can be appliedto the positioning and map construction of the robot in the unknown environment, and is particularly suitable for structured or semi-structured scenes, such as indoor environments, outdoor environments with buildings, and the like.

A precise information extraction algorithm for lane lines

Abstract: Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model (GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform (PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.

FSG: A statistical approach to line detection via fast segments grouping

Abstract: Line extraction is a preliminary step in various visual robotic tasks performed in low textured scenes such as city and indoor settings. Several efficient line segment detection algorithms such as LSD and EDLines have recently emerged. However, the state of the art segment grouping methods are not robust enough or not amenable for detecting lines in real-time. In this paper we present FSG, a fast and robust line detection algorithm. It is based on two independent components. A proposer that greedily cluster segments suggesting plausible line candidates and a probabilistic model that decides if a group of segments is an actual line. In the experiments we show that our procedure is more robust and faster than the best methods in the literature and achieves state-of-the art performance in a high level robot localization task such as vanishing points detection.