Automatic Gap Identification towards Efficient Contour Line Reconstruction in Topographic Maps

Shape depiction in non-photorealistic rendering of 3D objects has mainly been concerned with the extraction of contour lines, which are generally detected by tracking the discontinuities of a given set of shape features varying on the surface and/or the picture plane. In this paper, we investigate another approach: the depiction of shape through shading. This technique is often used in scientific illustration, comics, cartoon animation and various other artwork. A common method consists in indirectly adapting light positions to reveal shape features; but it quickly becomes impractical when the complexity of the object augments. In contrast, our approach is to directly extract a set of shape cues that are easily manipulated by a user and re-introduced during shading. The main problem raised by such an approach is that shape cues must be identified in a continuous way in image space, as opposed to line-based techniques. Our solution is a novel view-dependent shape descriptor called Apparent Relief, which carries pertinent continuous shape cues for every pixel of an image. It consists of a combination of object- and imagespace attributes. Such an approach provides appealing properties: it is simple to manipulate by a user, may be applied to a vast range of styles, and naturally brings levels-of-detail functionalities. It is also simple to implement, and works in real-time on modern graphics hardware.

A distributed approach to contour line extraction in a multi-modal field using wireless sensor networks is proposed in this work. In contrast with previous work on edge or boundary estimation, which is primarily concerned with the region of a certain phenomenon, contour lines offer more detailed information about the underlying phenomenon. To effectively utilize the limited resources of sensors, a cluster-based data fusion technique is adopted so that the phenomenon of interest can be monitored in the base station (BS) without demanding excessive raw data transmission. Simulation results are given to demonstrate the efficiency of the proposed approach. Furthermore, some analysis is conducted to understand the impact of the sensor density and noise on the performance of the proposed approach.

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.

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.

Recently, intelligent transport system (ITS) is actively developed. There are big development areas in it. Within those areas, the research on detection and recognition of the road sign is one of the most important development areas. In this paper, we show the process improvement in order to extract the contour line and recognize the road sign shape from it. We have proposed the new extraction method of the contour line and internal areas from the road sign instantaneously by using the self organization map (SOM)

As assyriologists have to handle tremendous amounts of important documents of ancient history written in cuneiform script, like the epic of Gilgamesh, we are developing an efficent system to support their daily tasks. Previous projects demonstrated the application of holography and early close-range 3D scanners for this task. Based on experiences from our previous projects in archaeology, we are focusing on processing the vast amounts of data of high resolution 3D models from todays close-range 3D scanners like the Breuckmann smartSCAN-3D-HE. The presented method exploits the high-resolution of the 3D models to extract the impressed characters as well as other features like fingerprints. Previous work typically used rendering techniques from computer graphics to visualize the characters, which then had to be processed manually. More recent approaches use methods from differential geometry for detection and extraction of coarse contour lines. These methods are computationally fast, and well-established for industrial application, but cannot cover the variations of human handwriting in form of the – wedge shaped – cuneiform script. To overcome the variations in size of the wedges, we choose a multiscale approach using integrating geometry. A transformation invariant function is achieved by calculating the volumes of multiple concentric spheres intersecting the volume below the 3D model's surface at each point. Due to this multiscale approach, this function is represented by the so-called feature vector. By classifying these feature vectors using auto-correlation, our system – called GigaMesh – can automatically extract characters, requiring only one parameter: the approximated line (wedge) width in mm. Results are shown for cuneiform tablets from the collections of the Assyriologie Heidelberg as well as from the Uruk-Warka Sammlung. Finally an outlook regarding character (en)coding and integration into related projects like the Cuneiform Digital Library Initiative (CDLI) is given.

The paper proposes a new extraction algorithm for contour lines and feature points from profile images for automatic personal identification. First the authors input a side view of a human head within a dark background under uniform lighting conditions by a CCD camera, then transform the input to a color difference signal image. After enhancing edges of the image using Sobel operators they binarize the luminance level of each pixel and portray a black profile in a white background. Next they differentiate the black profile, binarize the differentiated results, and depict a contour line using a thinning operator. Finally they encode the extracted contour line using Freeman's chain code and, using the encoded data, calculate the digital curvature in concave sections of the contour line and determine the concave feature points. Next they draw straight lines connecting the adjacent feature points and specify the convex feature points on the contour line which are farthest from the straight lines. They can obtain feature points of the profile automatically and reliably.

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.

3D Reconstruction of Underground Cable Wells with Automatic Extraction of Point Cloud Contour Lines

A digital elevation model and a topographic map of an archaeological site in Syria were created from a field topographic survey with a handheld laser range finder and data post‐processing using a notebook personal computer with Geographical Information System software. The method enabled rapid on‐site topographical mapping even under technical and political restrictions in the country. In an area of about 1 km2, coordinates of random points on the land surface were measured, and a DEM with a 10 m interval grid was generated from the point cloud using Kriging interpolation. The DEM enables various topographic representations to be constructed, such as contour lines, cross sections and slope distribution maps. The DEM‐derived contour lines with a 1 m interval were combined with vector data showing roads and buildings to provide a topographic map. Topographic cross sections created from the DEM generally agree with sections surveyed with a hand level and a measurement tape. The obtained DEM and map are useful for preliminary field research.

This paper addresses the problem of contour lines extraction from scanned topographic maps. A novel method called "local window segmentation" is developed and used to overcome the difficulty of the gaps and thick lines. Experiments and evaluations on a number of well- and common-conditioned topographic maps are provided.

The representation of the Earth’ topography in a 3D digital format is a vital constituent in a wide range of geographic, geomatics, environmental, and engineering activities. However, the accuracy of such representations could define its applicability and validity for a specific utilization. This chapter presents an analysis of four open-source Global Digital Elevation Models (GDEMs) and compares them on two topographic profiles (nearly flat, and hills regions) for mapping and geomatics applications in two countries. The main goal is to determine whether GDEMs-based heights, contour intervals, slopes, and topographic profiles are accurate for all topographic map scales, which is a significant problem in mapping operations. For the purpose of illustrating flat and moderate-topography patterns, two case studies—the Nile delta in Egypt and Makkah city in Saudi Arabia—have been used. The investigated GDEMs include the most-recent released models: ASTER v.3, ACE 2, SRTMGL1 v. 3, and NASADEM_HGT v.1 released in 2019 and 2020 with spatial resolutions of 1 and 3 arc seconds. There are 540 accurate Ground Control Points (GCP) available in the Nile delta and 175 in Makkah. It has been determined based on the available datasets in two study locations that  the accuracy of investigated GDEMs over known checkpoints range from ±2.5 and ±5.1 meters in the Nile delta region, while it varies between ±5.1 and ±8.0 meters in the Makkah area. This suggests that the use of GDEMs in topographic mapping varies greatly between spatial areas that are flat and those that are hilly. Therefore, it is advised against utilizing GDEMs to create topographic maps at scales of 1: 25,000 or greater in flat areas and 1: 50,000 or greater in hilly areas. Additionally, the achieved results demonstrated that throughout cross sections up to 30 kilometers in length, no GDEM-based slope matches the real slopes from known GCP. This leads to the conclusion that GDEMs are not the appropriate heights' source for topographic mapping at medium and large map scales, and could not be utilized for topographic profiling in precise engineering and geomatics applications. Such findings should be vitally taken into account in mapping and engineering applications not just in Egypt and Saudi Arabia but worldwide as well.

In response to the requirement, the manufacturing processes have evolved from mass production technique, through its implementation on reconfigurable systems. The most difficult part of reconfigurable manufacturing systems (RMSs) is an understanding of the barriers related to their relative interdependencies. This article presents a conceptual framework of various barriers associated with RMSs along with their interdependencies. Using graph theory and matrix methods, the barriers of RMSs presented in the form of a single numerical index. The proposed model is quite versatile from the point of view that it provides an opportunity to integrate new barriers which could impact on manufacturing processes. In the present work, an attempt has been made to develop a mathematical model of these barriers using graph theory and matrix methods. An index of barriers in RMS is proposed which evaluates the inhibiting power of these barriers.

In topographic maps, It is difficult to separate the linear elements, including contour lines, roads, latitude and longitude lines from complicated background due to the pixels with aliasing and false colors, and there exists some background in the result images extracted by the existing methods, especially when the color and energy of linear elements and background are similar in some particular maps, or the maps have low contrast contour lines. To solve these problems, this paper introduces the idea of seed spreading, and puts forward a novel method for separating linear elements. In this method, all the seeds carry the color information of the pixels and the energy information in the negative grayscale images, and they can search other pixels as their brothers to be combined into seed groups according to the color and energy similarity. The seeds have good perception of the environment around them, and the shapes of the seed groups are variable. Furthermore, the seeds are determined as linear elements by analyzing the color and energy differences between the seed groups and the areas around them. The experimental results show that our method can distinguish linear elements from the background more accurately than the previous methods.

The use of regional sinkhole maps, or closed-contour depicted sinkholes from 7.5-minute topographic maps, for deriving sinkhole susceptibility maps may be problematical. In Virginia, the 1:24,000-scale topographic maps for the Valley and Ridge physiographic province karst utilize either 6- or 12-m (20- or 40-ft) contour intervals. Detailed field mapping of sinkholes reveals that the actual number of sinkholes is an order of magnitude greater than the number depicted on a 6-m (20-ft) contour topographic map. The majority of the newly-formed Virginia sinkholes, examined over twenty years, were triggered by human influences to local drainage or by induced groundwater fluctuation. In folded and faulted carbonate-rock terrains, sinkhole distributions are influenced by site-specific lithologic configuration, bedrock deformation, and local hydrology. The processes of sinkhole formation are attuned to water-rock-soil relationships. Sinkhole susceptibility mapping should not be based on sinkhole patterns reflective of previous and existing land-use conditions if it will be used to plan for future development and land-use changes that will modify hydrological conditions. Process driven, karst is an evolving terrain that commonly has been characterized by subsidence, but the most significant and pervasive hazard is the susceptibility of karst groundwater to contamination.