Line Extraction Method Research Articles

Application of image processing technology in cable detection of underwater robot

Manual inspection of underwater cables has many problems, such as heavy labor intensity, high operation difficulty and low work efficiency. It is more effective to use underwater camera to capture images of underwater cables than manual inspection. The addition of subsea detection and construction equipment raises bottom sediment in the water and causes the water to become turbid and cloudy, it also reduces the contrast of the underwater image and adds irrelevant background information. Therefore, a method of cable location in turbid waters based on image processing is proposed. The method firstly applies the dark channel prior dehazing algorithm. An adaptive histogram equalization algorithm is used to improve the light distribution of underwater image after defogging. Then, we use the local adaptive dynamic threshold segmentation method to achieve the rough segmentation, and the fine segmentation of cable image is carried out by selecting each connected domain region. Finally, the cable pixel coordinates are obtained by the method of center line extraction and frequency domain transformation. Experimental result shows that the proposed algorithm has good effects in terms of clarity.

A new section line extraction method of ring forgings based on normal vector and L1-median

Ring forgings are widely used in industrial fields. It is an important way that the key dimensions of ring forgings are obtained by laser scanning to ensure the processing quality. It is difficult to extract high-precision section line of ring forgings from laser point clouds with a lot of noise. This paper presents a new method for extracting section line from raw point cloud of ring forging. An optimization algorithm is created based on the normal vector and L1-median, which is used to iteratively shrink the sampling points for obtaining the section line of ring forgings. L1-median has the characteristics of noise immunity, adding normal vector can get more detailed features. The extraction of high-precision section line of ring forgings is realized. Taking the second-order ring forging and the third-order ring forging as the experimental object, experiments are carried out to verify the effectiveness of the model.

Combining Ultraviolet Photodissociation and Two-Dimensional Mass Spectrometry: A Contemporary Approach for Characterizing Singly Charged Agrochemicals.

Ultraviolet photodissociation (UVPD) has been shown to produce extensive structurally informative data for a variety of chemically diverse compounds. Herein, we demonstrate the performance of the 193 nm UVPD fragmentation technique on structural/moiety characterization of 14 singly charged agrochemicals. Two-dimensional mass spectrometry (2DMS) using infrared multiphoton dissociation (IRMPD) and electron-induced dissociation (EID) have previously been applied to a select range of singly charged pesticides. The ≥80% moiety coverage achieved for the majority of the species by the UVPD and 2D-UVPD methods was on par with and, in some cases, superior to the data obtained by other fragmentation techniques in previous studies, demonstrating that UVPD is viable for these types of species. A three-dimensional (3D) peak picking method was implemented to extract the data from the 2DMS spectrum, overcoming the limitations of the line extraction method used in previous studies, successfully separating precursor specific fragments with milli-Dalton accuracy. Whole spectrum internal calibration combined with 3D peak picking obtained sub-part-per-million (ppm) to part-per-billion (ppb) mass accuracies across the entire 2DMS spectrum.

Tree Line Identification and Dynamics under Climate Change in Wuyishan National Park Based on Landsat Images

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers.

A Ridge-Valley Line Extraction Method on Hole Filling of Groove Point Cloud

Among several teaching methods of welding robot, the one based on 3D point cloud path extraction is a much more intuitive approach. However, due to reflectivity, corrosion and insufficient illumination, etc., the point cloud on the workpiece is squeezed into a small hole that hinders the path planning. Thus, this paper puts forward a hole segmentation and filling algorithm on the basis of ridge-valley line extraction. Firstly, the discrete curvature of the point cloud surface is estimated by Weingarten mapping properties, and the ridge and valley feature points are identified. Then, an improved connection algorithm is used to connect the ridge and valley points, and the feature lines obtained by fitting divide the holes into simple sub-holes. Finally, after the rough filling of each sub-hole with triangulation and subdivision algorithm, the positions of the newly added vertices are adjusted to the RBF (Radial Basis Function) implicit surfaces to complete the hole filling with sharp and curved features preserved. The experimental results show that this method can restore the sharp and curved features well on different groove point clouds.

Bending diagnosis of rice seedling lines and guidance line extraction of automatic weeding equipment in paddy field

Mechanical weeding is an efficient weeding method, which is of considerable significance to the paddy field ecosystem. However, traditional mechanical weeding methods can cause seedling damages due to the bending phenomenon of the seedling lines. Introducing computer vision and control technology to traditional mechanical weeding methods can help the system diagnose the bending phenomenon and avoid crushing the seedlings. In this paper, we propose a deep-learning-based method of seedling line bending diagnosis and guidance line extraction. To prove the proposed method effective in the mechanical weeding system, we choose the Faster Region-based Convolutional Network (R-CNN) and Single Shot MultiBox Detector (SSD) as the representative models of the single-phase method and the two-phase method. With a novel dataset of rice seedling images established, we compare and analyze the confidence and real-time performance of the trained models. The experimental results show that the Faster R-CNN model is better in terms of accuracy, yet the SSD model has more advantages in the speed. Comprehensively considering the system requiring and model performances, the SSD model is a better choice in the automatic rice avoidance system.

基于机载LiDAR点云数据的电力线自动提取方法

基于机载LiDAR点云数据的电力线自动提取方法

AUTOMATIC EXTRACTION OF POWER LINES FROM UAV LIDAR POINT CLOUDS USING A NOVEL SPATIAL FEATURE

Abstract. UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.

Three-dimensional (3D) reconstruction of structures and landscapes: A new point-and-line fusion method

The technique of three-dimensional (3D) reconstruction is widely used to develop infrastructure and landscape models to manage cities and assets better. Accurately reconstructing 3D structures (e.g., planes or lines) is a core step in rebuilding a model, especially within a built environment, where piece-wise planar/linear structures predominately prevail. As high-resolution images of large areas have become increasingly accessible, this paper develops an improved 3D reconstruction pipeline using the combination of point and line features. By introducing a dense reconstruction algorithm, which is an improved patch based stereo matching algorithm, this paper presents a robust approach that can be used to overcome the inaccuracies, integrity and reconstruction inefficiencies associated with point clouds. A 3D line extraction method is added to reconstruct accurate edges of buildings. The experimental results demonstrate that the proposed method visually improves the reconstruction effect of a 3D structure and a model's visualization.

Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features

Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas.

A three-step synthetic extraction algorithm for transmission lines

The combination of vision and unmanned aerial vehicle is an economic and effective solution for transmission line inspection. The key in the process of transmission line inspection is real-time extraction and recognition of transmission lines regardless of the complicated background. The horizontal and vertical disturbances are numerous in the complicated natural background. At the same time, transmission line crossing and different shooting angles of unmanned aerial vehicle lead to the phenomenon of overlapping lines, which makes it more difficult to extract and track transmission lines. Therefore, a three-step synthetic extraction algorithm for transmission lines is proposed in the article. Firstly, in view of the edge extraction problem of the image, a complementary edge feature extraction algorithm combining the Sobel operator with the Log operator is proposed to solve the problems such as discontinuous and uneven edges and low-detection efficiency caused by the general Canny operator for edge detection. Secondly, in view of the line feature extraction, a coarse-fine directional optimization algorithm is proposed. The horizontal and vertical interferences of the extraction of transmission lines under complex background and the problem of line crossing in the image are eliminated. The algorithm has higher detection effect and efficiency than Hough transform. Finally, a similarity determination mechanism is proposed to merge the adjacent lines, which avoids the loss of the tracking line caused by overlapping lines in the image. A quadrotor unmanned aerial vehicle is used to carry out the experiment of transmission line detection in the article. The experimental results demonstrate that the three-step method of line extraction is more effective and efficient than the current algorithms.

Surfaces of Revolution (SORs) Reconstruction Using a Self-Adaptive Generatrix Line Extraction Method from Point Clouds

This paper presents an automatic reconstruction algorithm of surfaces of revolution (SORs) with a self-adaptive method for generatrix line extraction from point clouds. The proposed method does not need to calculate the normal of point clouds, which can greatly improve the efficiency and accuracy of SORs reconstruction. Firstly, the rotation axis of a SOR is automatically extracted by a minimum relative deviation among the three axial directions for both tall-thin and short-wide SORs. Secondly, the projection profile of a SOR is extracted by the triangulated irregular network (TIN) model and random sample consensus (RANSAC) algorithm. Thirdly, the point set of a generatrix line of a SOR is determined by searching for the extremum of coordinate Z, together with overflow points processing, and further determines the type of generatrix line by the smaller RMS errors between linear fitting and quadratic curve fitting. In order to validate the efficiency and accuracy of the proposed method, two kinds of SORs, simple SORs with a straight generatrix line and complex SORs with a curved generatrix line are selected for comparison analysis in the paper. The results demonstrate that the proposed method is robust and can reconstruct SORs with a higher accuracy and efficiency based on the point clouds.

Feature Line Extraction from Point Clouds Based on Geometric Structure of Point Space

In order to improve the accuracy and rapidity of feature line extraction from point clouds, the work proposed a feature line extraction method based on geometric structure of point space. Firstly, a spatial grid dynamic division method is designed to locate the feature region of the model. A new feature points detection operator based on the linear intercept ratio is proposed according to the geometric information of points. Then, the feature points are refined by the Laplacian operator. Finally, the refined feature points are connected into the characteristic curve by the improved method of polyline growth. Compared with the feature points detection method based on surface variation (MSSV) or the angle of normal vector (SM-PD), the proposed method has low rate of error recognition with the increased noise intensity. Meanwhile, the computation time is 224.42 ms for the standard Armadillo model, less than 530.23 ms of the MSSV and 350.75 ms of the SM-PD. The experimental results show that the proposed method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.

Feature Line Extraction of Stone Tools Based on Mahalanobis Distance Metric

Point-cloud-based technique plays a very significant role in 3D model restoration. In the archaeological application of stone tools, the scale drawing, which is hand-drawn from measured stone tools, is traditionally used. In the scale drawing creation, a base drawing which consists outline and ridge lines is initially drawn from geometric features of shape. After that other lines are extracted from knowledge of making stone tools and are added to the base drawing. It requires special knowledge to extract feature lines from stone tools so that scale drawing is time-consuming. Therefore, if the base drawing is automatically extracted, the working hours are reduced. To overcome this issue, this paper proposes a feature line extraction method using the Mahalanobis distance metric. First, the points on outline are extracted from a point cloud. Then, the surface variation is calculated with a various number of neighbors and thus the potential feature points are detected by the analysis of its surface variation. After that, the potential feature points are thinned towards the highest variation points by using Laplacian smoothing. Then, the thinned feature points are shrunk to the potential feature points. Finally, a feature line is extracted by connecting the nearest thinned feature points locating in the Mahalanobis distance field. To verify our method, the extracted feature lines are compared to the ground truth of base drawing drawn by archaeological illustrators. Our method is applied to stone tools, and we confirm the effectiveness of our method.

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

This paper proposes a 3D autonomous navigation line extraction method for field roads in hilly regions based on a low-cost binocular vision system. Accurate guide path detection of field roads is a prerequisite for the automatic driving of agricultural machines. First, considering the lack of lane lines, blurred boundaries, and complex surroundings of field roads in hilly regions, a modified image processing method was established to strengthen shadow identification and information fusion to better distinguish the road area from its surroundings. Second, based on nonobvious shape characteristics and small differences in the gray values of the field roads inside the image, the centroid points of the road area as its statistical feature was extracted and smoothed and then used as the geometric primitives of stereo matching. Finally, an epipolar constraint and a homography matrix were applied for accurate matching and 3D reconstruction to obtain the autonomous navigation line of the field roads. Experiments on the automatic driving of a carrier on field roads showed that on straight roads, multicurvature complex roads and undulating roads, the mean deviations between the actual midline of the road and the automatically traveled trajectory were 0.031 m, 0.069 m, and 0.105 m, respectively, with maximum deviations of 0.133, 0.195 m, and 0.216 m, respectively. These test results demonstrate that the proposed method is feasible for road identification and 3D navigation line acquisition.

COASTLINE ZONE EXTRACTION USING LANDSAT-8 OLI IMAGERY, CASE STUDY: BODRUM PENINSULA, TURKEY

Abstract. Coastline extraction is a fundamental work for coastal resource management and coastal environmental protection. Today, by using digital image processing techniques, coastline extraction can be done with remote sensing imagery systems. In this study, Landsat 8 Operational Land Imagery (OLI) data have been the main data source due to free access and sufficient spatial resolution for coast line extraction. This research is focused on determining the coastline length and measuring land area by using Landsat 8 OLI satellite image for Bodrum Peninsula, Turkey. Three commonly used methods have been applied in order to determine sea-land boundary line and its length, and area of the study area. The Automatic Water Extraction Index (AWEI), Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification technique and on screen digitizing method was chosen for identification of coastal boundaries. Results of coastline length and land areas of Bodrum by using AWEI, ISODATA and on-screen digitizing are compared with each other. This study shows that with using optimal threshold value, AWEI can be used for coast line extraction method with coherently for Landsat 8 OLI satellite imagery. The overall results show that coastline extraction from satellite imagery can be done with sufficient accuracy using spectral water indices instead of time consuming on-screen digitizing.

Optimization of Line Extraction Based on an Affine Camera Model

Line feature constitutes important geometric structure information in image processing and is significant in visual navigation and 3-D structure contour extraction of 3-D objects. The current state-of-the-art line extraction methods exhibit fast extracting speed and good extracting results. However, most line extraction methods face a problem, wherein a long line is easily fragmented. Thus, a long line is broken into several short segments due to local small changes or disturbances. To obtain longer and more useful line segments without the aid of external information, this paper proposes an improved line extraction method called affine-lines, which is based on an affine camera model. First, the affine camera model is used to simulate affine projections from different viewing angles, and a sequence of affine simulated images is obtained via affine transformation matrices. Subsequently, line segments are extracted from the original image and each simulated image. Each set of line segments on the simulated image is back-projected on the original image based on its corresponding inversely affine transformation matrix. Finally, the line segments on the original image are used as references to sequentially purify and optimize subsequent lines. Several defined geometric constraints are used to eliminate pseudo lines and combine short lines. Thus, fragmented lines are connected, and short lines are converted into long lines. Given several sets of close-range and aerial images, experiments are performed and compared via a state-of-the-art line extraction algorithm, called the line segment detector (LSD). The results indicate that the proposed method significantly increases the lengths of the obtained line segments, significantly reduces the fragmentation effect, and obtains more useful line segments. The extracted improved line segments are applied to line matching for building structures, and promising results are obtained.

Power Line Extraction From Aerial Images Using Object-Based Markov Random Field With Anisotropic Weighted Penalty

The extraction of power line plays a key role in power line inspection by Unmanned Aerial Vehicles (UAVs). While it is challenging to extract power lines in aerial images because of the weak targets and the complex background. In this paper, a novel power line extraction method is proposed. First of all, we create a line segment candidate pool which contains power line segments and large amount of other line segments. Secondly, we construct the irregular graph model with these line segments as nodes. Then a novel object-based Markov random field with anisotropic weighted penalty (OMRF-AWP) method is proposed. It defines a new neighborhood system based on the irregular graph model and builds a new potential function by considering the region angle information. With the OMRF-AWP method, we can distinguish between the power line segments and other line segments. Finally, an envelope-based piecewise fitting (EPF) method is proposed to fit the power lines. Experimental results show that the proposed method has good performance in multiple scenes with complex background.

Extraction of Terrain Feature Lines from Elevation Contours Using a Directed Adjacent Relation Tree

The extraction of terrain feature lines is an important yet challenging problem in the processing and usage of contour lines. Most research has focused on identifying terrain feature points by using methods such as the Douglas–Peucker (D–P) algorithm and on determining contour adjacent relations for closed contour lines by using methods such as Delaunay triangulation and its dual Voronoi diagram, with few attempts made to address open contour lines and identify the direction of contour lines. Here, we propose a novel method for terrain feature line extraction based on adjacent relation trees. First, a contour adjacent relation tree is constructed. The tree is used to determine the directions of closed and open contour lines and thereby form a “directed adjacent relation tree” (DART). Terrain feature points are then extracted using the classic D–P algorithm, followed by the identification and connection of valley and ridge points via the aforementioned DART. Finally, integrity compensation for terrain feature lines is performed based on a slope extension algorithm. The effectiveness and accuracy of this method is verified by experimental results obtained from 1:10,000 topographic map data.

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings.

In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods.