Contour Line Extraction Research Articles

RGB pixel information fusion segmentation of dense point cloud data for transmission and distribution 3D digital corridors

Abstract Point cloud segmentation, as a key link in 3D point cloud data processing, can realize power transmission and distribution positioning, object identification, classification, and feature extraction, which helps to refine power grid management. In this paper, firstly, dense point cloud transmission and distribution 3D digital corridor modeling is carried out. Alignment splicing and noise reduction are carried out after obtaining the original dense point cloud. Contour line extraction, geometric modeling, and texture mapping are realized after processing the data to ultimately realize the transmission and distribution of 3D digitization. Then, the conversion formula for the pixel coordinate system and world coordinate system is derived to extract features from point clouds. Finally, a distance-based feature fusion method is designed to extract spatial features from point clouds and use the joint attention layer to segment them by fusing RGB pixel information. The original dense point cloud of a transmission and distribution digital corridor is segmented using the model presented in this paper for application after testing the dataset. It is found that the under-segmentation ratio of this paper’s algorithm is 0.96%, 3.44%, and 2.87% for the three scenarios of regular, irregular, and multi-targets, respectively, which is much lower than that of RANSAC+ECS with FCM + ECS. The intersection and concatenation ratios of this paper’s algorithm for the scenarios of irregular geometry as well as multi-target objects are 91.49% and 89.56%. It is much higher than 64.31% and 72.17% for RANSAC + ECS and 76.85% and 60.91% for FCM + ECS, which illustrates that this paper’s algorithm has a significant advantage in performance. In this study, the target point cloud can be segmented with high accuracy from the dense point cloud of a 3D model of power transmission and distribution with a large amount of data, effectively avoiding the phenomenon of under-segmentation and over-segmentation and contributing to the accurate control of power grid data.

Semantic Extraction of Roof Contour Lines From Airborne LiDAR Building Point Clouds Based on Multidirectional Equal-Width Banding

Semantic Extraction of Roof Contour Lines From Airborne LiDAR Building Point Clouds Based on Multidirectional Equal-Width Banding

Light Source Optimization of 3D Inner Contour Measurement System Based on Ring Beam Method

Ring beam method is a simple and efficient method for measuring three-dimensional internal contour. The whole three-dimensional inner contour is stitched from the contour lines of cross-sectional images collected during scanning. The contour image with uniform and moderate light intensity and fine ring band is beneficial to the calculation of contour line extraction algorithm. In practice, due to the quality of the ring beam and the modulation effect of the sample surface microstructure, the light band of the contour image has local widening, overexposure and speckle phenomena, which directly affect the contour extraction effect. The ring beam generation system is optimized here, which makes the whole system have better size adaptability and convenient adjustment. In addition, experiments results show that the measurement repeatability of the cross-sectional profile obtained after the light source optimization has been effectively improved.

Building 3-D Reconstruction With a Small Data Stack Using SAR Tomography

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well established for the 3-D reconstruction of urban buildings. Many methods have been proposed in the literature for TomoSAR inversion. These methods usually require fairly large data stacks (more than 20 images) for reliable reconstruction. Hence, they cannot be applied to the 3-D reconstruction of urban buildings using Gaofen-3 (GF-3) data directly, because there are few images available on average in each city. This article proposes a novel workflow for the 3-D reconstruction of high-rise buildings using small data stacks. In this workflow, we combine the methods of contour line extraction (CLE) and reference-elevation multilooking relaxation (RM-RELAX) for the 3-D reconstruction of high-rise buildings. The CLE method extracts contour lines of buildings relying on a data-driven approach and does not need to rely on any external data, which can provide prior knowledge for subsequent processing. The RM-RELAX method is an extension of RELAX, which can obtain more precise 3-D inversion with multilooking and reject outliers with the reference elevation (which can be obtained by the prior knowledge of contour lines) of each pixel. The applicability of this workflow is demonstrated by using simulated data and real data with six SAR images acquired by the GF-3 satellite over an area in Beijing, China.

Fusing Georeferenced and Stereoscopic Image Data for 3D Building Façade Reconstruction

3D building façade reconstruction has become a very popular topic in various applications related to restoration and preservation of architectural structures as well as urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. We present an approach that enables the relation of stereoscopic images with tacheometry data. The proposed multimodal fusing scheme results in an accurate 3D realistic façade reconstruction and provides a fast and low cost solution. In the first stage of the proposed approach a 2D skeleton of the building is extracted from the viewed scene using Active Contour and Hough line extraction. The next stage of our method utilizes depth information, extracted from a stereoscopic layout, to infer the structural details of inner façade structures, such as windows or doors. In the final stage, the structural information extracted from the image data is integrated with georeferenced point datasets. The final output of our method is a georeferenced 3D model of the structure’s façade, which can be further refined with the use of image-driven texture information.

ISAR Image Recognition Algorithm and Neural Network Implementation

Abstract The image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.

ISAR geometry, signal model, and image processing algorithms

Inverse synthetic aperture radar (ISAR) technique provides high-resolution images of observed moving objects. However, shadow-occluded effects and noise, which results in lack of fractions of the object geometry, often corrupt ISAR images. To facilitate the image analysis and to solve the target identification problem in most practical applications additional image-processing procedures are strongly required. The aim of the present research is based on ISAR geometry and signal modelling to develop procedures improving quality of images and extract the silhouette contour line of the ISAR image, the main geometric parameter in the target recognition process. The problem is solved in two main stages. First, ISAR geometric parameters are described and expressions for linear frequency modulated signal modelling and imaging are derived. Second, image-processing algorithms for ISAR image improving and contour line extraction are developed. All stages of image-processing and reconstructing algorithms are illustrated by results of numerical experiments.

The Reconnection of Contour Lines from Scanned Color Images of Topographical Maps Based on GPU Implementation

This paper presents a method for the reconnection of contour lines from scanned color images of topographical maps based on graphics processing unit (GPU) implementation. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. First, an effective method is presented for contour line reconnection from scanned topographical maps based on CPU. This method considers both the distance and direction between the two broken points of the contour lines. It gets better performance and has high connection rate, but the time complexity of the algorithm is nonlinear with the increasing size of topographical map. Second, the advantage of the massively parallel computing capability of GPU with the compute unified device architecture is taken to improve the algorithm. Finally, a better performance has been achieved based on the open source computer vision library. The experimental results show that the GPU implementation with loop-based patterns achieves a speedup of 1360× and the identical result compared with the implementation on CPU.

Identification of Contour Lines from Average-Quality Scanned Topographic Maps

Contour line is the main linear feature on topographic maps. Extraction of contour lines is tedious and time-consuming process, but is still an interesting problem. This paper presents a novel method for extracting contour lines from average-quality scanned topographic maps. First, it uses spatial fuzzyc-means algorithm (sFCM) to solve color aliasing and false color problems by taking into consideration both color and spatial information of topographic maps during color segmentation. In order to improve the categorizing rate, upper and lower cut-sets are introduced into sFCM. Second, to deal with the problem of thick lines, node segments are removed before gaps are repaired. Third, different methods are used to repair contour lines gaps according to the causes, which improves the break points matching accuracy. The performance of the method is tested on several topographic maps comparing with other methods, and the results show that the method can avoid misleading results caused by distortion and wrong branches at intersecting regions when using thinning algorithms and have more accurate and higher quality extraction results.

Fisiografia da micro bacia do Reibeirão Coqueiro - Jataizinho, PR, obtida por meio de técnicas de geoprocessamento

This study applies geoprocessing in morphometric characterization of the Stream Coqueiro watershed - Jataizinho, Stateof PR,through the Geographic Information System software - IDRISI Selva, aim ingat the preservation, rationalization of their use and environment al recovery. The water shed hasanarea of 1171,07ha and is located between the UTM coordinates, longitude 503037 to 510757m W and latitudes 7426,813 to 7429722m S. The cartographic base use dwere the cartographic mapsof Assam andUrai (PR), scale 1: 50000 (IBGE, 1991) in the extraction of contour lines, hydrography and topography, Geographic Information System environment - Idrisi Selva, to determine the morphometric in dices. The results showed that low levels of drainage densitya ssociate dwith the presence of permeable rock, facilitate the infiltration of the soil water run off and decreasing therisk of erosion and environment al degradation, as well as the low value of the shape factors upported by the circularity index indicates that the water shed tendstobe more elongate dwith les ssusceptibility to floods more pronounced. The environment al parametercoe fficient ofroughnessal low edto classify the water shed for vocation with forest and reforestation.

Extraction of Cartographic Contour Lines Using Digital Terrain Model (DTM)

The French National Mapping Agency (IGN) plans to renew all the topographic maps at 1∶25 000 and 1∶50 000 scale. The New Base Map project has been launched in order to produce a flow line for topographic maps from a vector geographic database, and decrease costs of topographic map production. The project aims also to automate the extraction of contour lines, and take advantage of the high-resolution digital terrain model (DTM) data recently available in the agency. These data come from automatic correlation, or they are produced using new sensors (lidar or radar). The high-resolution DTM is a breakthrough to extract cartographic elements such as the cartographic contour lines, the points of mountain pass and the summits. In a precedent paper, we presented a research for producing cartographic contour lines from DTM. Since then, the method has been developed and implemented at the IGN in order to have a production tool for the contour lines. The first part of this paper provides technical details regarding the extraction of contour lines with the problems resolved during the implementation. The second part will introduce our method for extracting the mountain pass and summits from high-resolution DTM. The final section will include the results and summarize the findings.L’Institut Géographique National français a prévu une rénovation complète de ses cartes au 1:25 000 et au 1:50 000. Le projet Nouvelle Carte de Base a été initié pour produire une ligne de production pour la production de cartes topographiques à partir de bases de données vecteur (le RGE) en diminuant les couts de production de ces cartes. Le projet a entre autre pour but d’automatiser l’extraction des courbes de niveau et d’utiliser au mieux le MNT Haute Résolution disponible depuis peu à l’IGN. Ces données récentes sont produites par corrélation automatique ou à partir de nouveaux capteurs (lidar ou radar). Le MNT Haute Résolution est conçu pour pouvoir en extraire des éléments cartographiques tels que des courbes de niveau, des points d’altitude dans les cols et les sommets. Dans un papier précédant nous avons présenté la méthode globale pour produire des courbes de niveau à partir de MNT. La méthode a été conçue et implémentée à l’IGN pour une utilisation dans la chaîne de production. Dans la première partie de ce papier nous présentons les détails techniques de l’extraction de courbes de niveau comprenant la résolution de problèmes rencontrés pendant l’implémentation. Dans la seconde partie nous présentons notre méthode d’extraction de cols et de sommets à partir d’un MNT Haute Résolution. La dernière partie présente et commente les résultats obtenus.

Contour line and geographic feature extraction from USGS color topographical paper maps

This paper presents a method for the extraction of contour lines and other geographic information from scanned color images of topographical maps. Although topographic maps are available from many suppliers, this work focuses on United States Geological Survey (USGS) maps. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. These difficulties render simple approaches such as clustering ineffective. The proposed method overcomes these difficulties using a multistep process. First, a color key set, designed to comprehend color aliasing and false colors, is generated using an eigenvector line-fitting technique in RGB space. Next, area features, representing vegetation and bodies of water, are extracted using RGB color histogram analysis in order to simplify the next stage. Then, linear features corresponding to roads and rivers including contours, are extracted using a valley seeking algorithm operating on a transformed version of the original map. Finally, an A* search algorithm is used to link valleys together to form linear features and to close the gaps caused by intersecting features. The performance of the algorithm is tested on a number of USGS topographic map samples.

Layering-based breakpoint handling in contour line extraction

This paper deals with the automatic connection of contour lines extracted from a scanned brown geographical map. For the variety of topographical elements contained on a map, the factors causing the interruption of contour line are also multiform, which make the connection task very difficult. On the basis of separating those elements always making the contours break and regarding them as referent layers, a layering-based method is presented. The purpose is to take into account property information (like inclination and configuration) of contour lines when they come across other different symbols, such as gully, cliff, dry land and elevation annotation etc. In this paper, the authors propose that it should be far more effective and direct to adopt different algorithmic operators to different factors than using single one operator to all.

EXTRACTING CONTOUR LINES UTILIZING FEATURES OF A RASTER IMAGE TOPOGRAPHIC MAP

It is a difficult task to automatically extract contour lines from a raster image topographic map and give each contour line its inherent elevation. In this paper, the authors propose an automatic contour line extraction algorithm based on the continuity among the contour lines. All of the points on a contour line indicate the same elevation, the difference of elevations between every neighboring contour line is constant, and most wide contour lines have numeral arrays pointing out their inherent elevations. By employing the information of these features, the shapes and elevations of contour lines can be extracted and recognized. Usefulness of the proposed algorithm is demonstrated with several practical maps.

Fast contour line extraction algorithm with selective thresholding according to line continuity

AbstractThis paper describes a new contour line extraction algorithm which makes better contour line continuation and accelerates processing speed.When obtaining a contour line from an original gray‐scale image, the contour line extraction algorithm generally requires the following conditions: extracted contour lines representing a good feature of the original image; less invalid data such as noise; contour line width is one pixel thickness; and better contour connectedness. Many contour line extraction algorithms have been proposed. However, usually the user has employed the contour line extraction algorithm selecting by experience and a sixth sense. In the conventional method, differential operators are carried out on an original image with thresholding at a given value. Afterward, a thinning operation is carried out to obtain a one‐pixel thickness contour line. However, this process takes considerable computing time because the thinning process needs recursive processing.The new algorithm presented here carries out the process which performs the edge detection and thinning operation simultaneously eliminating the extra step. The process is effectively integrated in two ways: one is maximum point extraction in the differential image; and the other is the thresholding by introducing the selection of threshold values by using observance of the contour line connectedness in the differential image.As a result, the proposed algorithm is 10 times faster than the conventional methods in processing speed, and provides almost the same quality in contour line image.

Extraction of contour lines by identification of neighbor relationships on a voronoi line graph

AbstractThe studies on automatic extraction of contour lines have attracted interest in building a map information system as an automatic composition of input data. However, many attempts reported so far considered only the local connection relationship of contour lines and did not use global information contained in the whole topographic map. Therefore, they could not cope with extraction of contour lines having various cut forms.The method proposed in this paper can deal with the interconnection of contour lines which are disconnected within a region enclosed by a curved contour line and also the interconnection of contour lines disconnected in the same region, which were difficult in the existing methods. This was because its processing is based on global information of contour lines contained in a topographic map.As an example of the global information, the neighbor relationship built by a Voronoi diagram is used. The proposed method is divided into the following five steps: construction of a Voronoi diagram; identification of neighbor relationships of line segments; determination of height relation of line segments; extraction of contour lines; and extraction of pairs of endpoints to be interconnected. Then, while comparing and verifying them with the features of contour lines, disconnected contour lines are identified and extracted. This paper reports the procedures for processing and then considers the effect of the proposed method by experimental results.

An approach to computational complexity: nondiscrete biomolecular computing

Discrete molecular computing devices based on digital device simulation and nondiscrete biomolecular computing devices based on the overall principles of information processing that use molecular-level mechanisms to create simple biomolecular systems are described. It is argued that discrete molecular computing only extends the choice of materials available in modern semiconductor device technology, but does not provide an opportunity to use physical mechanisms of information processing different from those available in conventional materials and devices. Nondiscrete biomolecular information processing devices effectively solve some problems with high computational complexity-for example, image processing operations such as contour enhancement and line extraction. The information processing potential of biomolecular systems is reviewed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

立体形状の高速認識方式

This paper comprehensively presents a method to recognize 3-dimensional objects within one second. Slit light data from a range finder and gray scale image from a TV-camera are used jointly. First, by subtracting the background image from the background with slit light image, an image containing only, the slit light is obtained. ±0.3 pixel accuracy in fixing the slit line position in the image is obtained by using a statistical method. The extracting time is 200 ms. Then syntax analysis for labeling objects and extraction of contour lines from the gray scale image line is reported. The threshold for processing the gray scale image is decided automatically by making use of the slit light. At last, the method to reconstruct 3-dimensional data from 2-dimensional data is presented. The coefficients between picture coordinates (I, J) and a spacial coordinates (x, y, z) are calculated using a least squares method. Totally, 400 ms for all execution time and ±0.2 mm in accuracy, are obtained.

A coding method of gray‐valued pictures using density contour lines

AbstractFirst, the concept of density contour lines is introduced for gray‐valued pictures. Its application for pattern recognition has already been reported. However, in this paper it is considered for description and restoration of gray‐valued pictures via the extraction of density contour lines as well as for coding gray‐valued pictures by assigning priority to the density contour lines. Furthermore, a method for sequential restoration of pictures from general information is described.In this paper, sequential restoration from the main contour' lines and experimental restoration of gray‐valued pictures using only a part of the density contour lines are demonstrated. These results illustrate a new coding method of gray‐valued pictures which facilitates the coding of the high information content of original pictures by only about 20% of the contour lines. Finally, the effectivity of the present method is shown for the fast search of pictures.