Douglas Peucker Research Articles

Image analysis and the finite element method in the characterization of the influence of porosity parameters on the mechanical properties of porous EVA/PMMA polymer blends

Poly(ethylene-co-vinyl acetate) (EVA) and poly(methyl methacrylate) (PMMA) are two incompatible polymers. Compatibilization of EVA/PMMA polymer blends with EVA-g-PMMA decreased the pore diameter and improved the mechanical properties. Image analysis of SEM micrographs enabled statistical analysis of the porosity parameters, including data about their positions. Positioning of the pores on the image was achieved by implementation of Voronoi diagrams and Delaunay triangulation by Python libraries. Hausdorff dimension (DHf) analysis showed improved complexity of the material network obtained by compatibilization. The coordinates of pore contours were processed by the Ramer–Douglas–Peucker algorithm (RDP) to establish the models by the finite element method (FEM). This process is iterative and enables a parametric study of the problem so that the type of pore geometry responsible for the observed mechanical behavior could be revealed. The FEM revealed the regions of the material that bear the load and enabled the extraction of parts of the material that diminish the porosity but do not participate in load carrying. A parametric study showed a decrease of 22% in the maximal shear stress when the porosity increased 10%, suggesting the importance of bulging of the material that played the role in decreasing the porosity of non-bearing material. A procedure for selecting the model with a regular pore shape and distribution based on the actual (irregular) porous microstructure is proposed.

Error-bounded and Number-bounded Approximate Spatial Query for Interactive Visualization

AbstractIn the big data era, an enormous amount of spatial and spatiotemporal data are generated every day. However, spatial query result sets that satisfy a query condition are very large, sometimes over hundreds or thousands of terabytes. Interactive visualization of big geospatial data calls for continuous query requests, and large query results prevent visual efficiency. Furthermore, traditional methods based on random sampling or line simplification are not suitable for spatial data visualization with bounded errors and bound vertex numbers. In this paper, we propose a vertex sampling method—the Balanced Douglas Peucker (B-DP) algorithm—to build hierarchical structures, where the order and weights of vertices are preserved in binary trees. Then, we develop query processing algorithms with bounded errors and bounded numbers, where the vertices are retrieved by binary trees’ breadth-first-searching (BFS) with a maximum-error-first (MEF) queue. Finally, we conduct an experimental study with OpenStreetMap (OSM) data to determine the effectiveness of our query method in interactive visualization. The results show that the proposed approach can markedly reduce the query results’ size and maintain high accuracy, and its performance is robust against the data volume.

A method for simplifying ship trajectory based on improved Douglas–Peucker algorithm

Automatic identification system (AIS) can provide massive ship trajectory data that is valuable for mining information in water traffic. However, large sizes lead to difficulties in storing, querying, and processing the aforementioned data. In the present study, to better compress ship trajectory data regarding compression time and efficiency, a method based on the improved Douglas–Peucker (DP) algorithm is presented. In the process of compression, the proposed method considers the shape of vessel trajectory derived from course information of track points. Parallel experiments are conducted based on AIS data gathered over the duration of a month in the Chinese Zhou Shan islands. The results indicate that this method can effectively compress ship trajectory information. Additionally, when compared with the traditional DP algorithm, this method can significantly reduce the compression time and exhibits better performance at high compression strengths. Also, the proposed method outperforms other existing trajectory compression algorithms in term of compression time.

A Hierarchical unsupervised method for power line classification from airborne LiDAR data

ABSTRACTThe automatic classification of power lines from airborne light detection and ranging (LiDAR) data is a crucial task for power supply management. The methods for power line classification can be either supervised or unsupervised. Supervised methods might achieve high accuracy for small areas, but it is time consuming to collect training data over areas of different conditions and complexity. Therefore, unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand. In this paper, we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points (including the power line corridor direction detection based on a layered Hough transform, connectivity analysis, and Douglas–Peucker simplification algorithm), followed by the extraction of contextual linear and angular features for each candidate laser points, and finally by setting the feature threshold values to identify the power line points. We tested the method over both forest and urban areas and found that the precision, recall and quality rates were up to 96.7%, 88.8% and 78.3%, respectively, for the test datasets and were higher than the ones from a previously developed supervised classification method. Overall, our approach has the advantages of achieving relatively high accuracy and being relatively fast.

Bias Correction in Monthly Records of Satellite Soil Moisture Using Nonuniform CDFs

It is important to eliminate systematic biases in the field of soil moisture data assimilation. One simple method for bias removal is to match cumulative distribution functions (CDFs) of modeled soil moisture data to satellite soil moisture data. Traditional methods approximate numerical CDFs using 12 or 20 uniformly spaced samples. In this paper, we applied the Douglas–Peucker curve approximation algorithm to approximate the CDFs and found that three nonuniformly spaced samples can achieve the same reduction in standard deviation. Meanwhile, the matching results are always closely related to the temporal and spatial availability of soil moisture observed by automatic soil moisture station (ASM). We also applied the new nonuniformly spaced sampling method to a shorter time series. Instead of processing a whole year of data at once, we divided it into 12 datasets and used three nonuniformly spaced samples to approximate the model data’s CDF for each month. The matching results demonstrate that NU-CDF3 reduced the SD, improved R, and reduced the RMSD in over 70% of the stations, when compared with U-CDF12. Additionally, the SD and RMSD have been reduced by over 4% with R improved by more than 9%.

Reconstruction of Medical Images Using Artificial Bee Colony Algorithm

The goal of this study is to assess the efficiency of Artificial Bee Colony (ABC) algorithm in finding the optimal solution of curve fitting problem specifically for medical images. Data of Computed Tomography (CT) images from two different patients were collected. The procedure of curve fitting for medical images include conversion of Digital and Communications in Medicine (DICOM) images to binary images, boundary and corner point detection, parameterization, and curve reconstruction by using ABC algorithm. Then, Sum Square Error (SSE) was used to calculate the distance of the fitted Cubic Bezier curve with the boundary of the original images. Based on the calculation and parameter tuning that had been done, the smallest error of both skulls is57.5754and28.8628, respectively. The finding of this study illustrated that the proposed method had efficiently produced fitted Bezier curve that resemble the original medical images. In addition, the used of Douglas Peucker algorithm helps to improve the performance of the proposed method since computational time can be minimized. This study had shown that the proposed method can be used as an alternative method in order to reconstruct or redesigned the medical images since it produces a small error. For future work, we are planning to explore and applied the ABC algorithm to reconstruct the missing part of the skull since it can reduce the time taken to produce the skull implant as well as reducing the cost of producing it.

A new approach to simplifying polygonal and linear features using superpixel segmentation

ABSTRACTOne important classical research area in automated cartographic generalization is simplification. Over the past few decades, numerous scholars have proposed various methods for polygon and line simplification, most of which have focused on vector data. However, with the rapid development of computer vision technology, unstructured image analysis and processing has provided a plethora of information, as well as new challenges. Therefore, in this article, we propose a new method for simplifying polygonal and linear features: a superpixel segmentation (SUSS) method specially designed for image data. In this method, polygonal boundaries are first divided by a superpixel algorithm called simple linear iterative clustering. Then, three types of curves – convex, concave, and flat – are globally simplified by comparing and selecting superpixels. Finally, uneven local features are removed by Fourier descriptors. In addition, the proposed SUSS method is extended for linear features, and it maintains topological relationships. To demonstrate the effectiveness of this approach, we use contours and water area data to perform experiments. Compared with the classic Douglas–Peucker and Wang and Muller algorithms, the proposed method is able to properly simplify the curves of polygonal and linear features while maintaining their essential shapes, and it maintains a steady change in area for large-scale applications while effectively avoiding self-intersection issues. Compared with the typical smoothing and Raposo algorithms, the proposed SUSS method can simplify lines at different scales and guarantee effective smoothing while maintaining displacement.

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.

An Efficient Visualization Method for Polygonal Data with Dynamic Simplification

Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we present an efficient polygonal data visualization method by organizing the simplification, tessellation and rendering operations into a single mesh generalization process. First, based on the sweep line method, we propose a topology embedded trapezoidal mesh data structure to organize the tessellated polygons. Second, we introduce horizontal and vertical generalization operations to simplify the trapezoidal meshes. Finally, we define a heuristic testing algorithm to efficiently preserve the topological consistency. The method is tested using three OpenStreetMap datasets and compared with the Douglas Peucker algorithm and the Binary Line Generalization tree-based method. The results show that the proposed method improves the rendering efficiency by a factor of six. Efficiency-sensitive mapping applications such as emergency mapping could benefit from this method, which would significantly improve their visualization performances.

The Application of Douglas-Peucker Algorithm in Collaborative System for Power Grid Operation Mode Calculation

The collaborative system for power grid operation mode calculation implements data management of multi-level paralleling operated dispatching departments indifferent places and joint operation mode calculation, which is based on the calculated data of power grid operation modes. The transient stability analysis monitors the components of electrical variation curve through the network transmission to the web page. When more users and more curves reach a certain extent, the overall simulation time is longer and the curve is stuck because of the limitation of the transmission bandwidth and the refresh rate of the web page. This paper, The Douglas Peucker algorithm is used in system to solve the data transmission of large amount of data delay problem and achieve the transmission of the large amount of data, the overall simulation time of curve in the web page display is decreased, the curves are refreshed smoothly.

Simplifying GPS Trajectory Data with Enhanced Spatial-Temporal Constraints

Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may be used to simplify GPS trajectories. However, traditional algorithms focus on geometric characteristics of linear features. Trajectory data may record information beyond location. Examples include time and elevation, and inferred information such as speed, transportation mode, and activities. Effective trajectory simplification should preserve these characteristics in addition to location and orientation of spatial-temporal movement. This paper proposes an Enhanced Douglas–Peucker (EDP) algorithm that implements a set of Enhanced Spatial-Temporal Constraints (ESTC) when simplifying trajectory data. These constraints ensure that the essential properties of a trajectory be preserved through preserving critical points. Further, this study argues that speed profile can uniquely identify a trajectory and thus it can be used to evaluate the effectiveness of a trajectory simplification. The proposed ESTC-EDP simplification method is applied to two examples of GPS trajectory. The results of trajectory simplification are reported and compared with that from traditional DP algorithm. The effectiveness of simplification is evaluated.

Edge-based approach to estimate the drift of a helicopter during flight

The Institute of Flight Systems at the German Aerospace Center (DLR) site in Braunschweig Germany has set its goal into making helicopter flying as safe as possible. The new DLR research project “Rettungshubschrauber 2030” addresses the topic of aiding helicopter rescue missions. Research will be conducted to increase the safety of these missions as well as to enable the conduct of missions in circumstances where nowadays a helicopter would not be allowed to operate. One aspect of this research is to increase or maintain the situational awareness of the pilot by processing data from camera images. The paper will focus on the field of visual odometry. It examines, if an edge-based approach for extracting features is a possible alternative or addition to established feature extractors. In the paper, two algorithms for edge extraction will be compared: an algorithm that is based on Hough transform and an algorithm that is based on the Douglas–Peucker method. They are tuned for working with images of rather low resolution and are tested on their ability to detect a sufficient amount of features in camera images as well as on their computational complexity. Then, their ability to detect the drift of a helicopter is assessed using recorded data from flight tests with the advanced control technology/flying helicopter simulator (ACT/FHS) of the DLR. Their performance will be tested on the basis of reference data from the ACT/FHS which have been recorded by the use of a highly accurate INS/DGPS system. The evaluation of the algorithms yields the result that both achieve the proposed accuracy regarding the velocity estimation with the Hough-based method achieving better accuracy while taking significantly more computational time.

A segmental HMM based trajectory classification using genetic algorithm

Trajectory classification techniques face various challenges due to varying length and lack of the presence of clear boundaries among the trajectory classes. To overcome such challenges, a trajectory shrinking framework using Adaptive Multi-Kernel based Shrinkage (AMKS) can be used. However, such a strategy often results in over-shrinking of trajectories leading to poor classification. To improve classification performance, we introduce two additional kernels that are based on convex hull and Ramer–Douglas–Peucker (RDP) algorithm. Next, we propose a supervised trajectory classification approach using a combination of global and Segmental Hidden Markov Model (HMM) based classifiers. In the first stage, HMM is used globally for classification of trajectory to provide state-wise distribution of trajectory segments. In the second stage, state-wise trajectory segments are classified and combined with global recognition performance to improve the classification results. Combination of Global HMM and Segmental HMM is performed using a genetic algorithm (GA) based framework in the final stage. We have conducted experiments over two publicly available datasets, popularly known as T15 and MIT. We have achieved 94.80% and 96.75% of accuracies on T15 and MIT datasets, respectively. We also analyzed the robustness of the proposed framework by adding Gaussian noise. To show the effectiveness of the system, we have performed recognition of on-line signature using proposed Segmental HMM based combination model. In SVC2004 signature dataset, it outperforms traditional HMM-based systems.

A neutral model for the simulation of linear networks in territories

A landscape matrix is the support of biotic and abiotic flows, and in that sense, requires increased interest from ecological modellers. This matrix is partly composed of linear elements, such as roads and field borders, that delimit land uses and are the result of socio-economic drivers. The geometrical properties of these elements could affect flows of water bodies, fauna, and flora. A large amount of research on landscape matrix simulation has been conducted using neutral models, but the efforts have been limited to 1km2 and have been principally devoted to field borders. However, simulating largest territories in neutral models requires consideration of supplementary elements, such as road networks. Furthermore, the sinuosities of the linear elements in territories have rarely been considered per se. We proposed a hierarchical model based on successive imbrication of deformed networks, with the deformation being realized on the basis of a reverse Douglas–Peucker algorithm. We first isolated the hierarchical levels of the landscape and analyzed their relative deformations. Then we constructed the hierarchical model and we tested it on a real territory in the Mediterranean zone. Its structural realism was tested against other common neutral models using the pattern-oriented modelling approach. The hierarchical model was the only neutral model able to represent simultaneously the variabilities of three patterns having implications in ecological processes: polyline lengths, sinuosities and polygon areas. Possible improvements of the model to address non-stationary processes and its potential for implementing geoprospective scenarios are discussed.

Cutting tool wear detection using multiclass logical analysis of data

ABSTRACTThis article presents a new tool wear multiclass detection method. Based on the experimental data, tool wear classes are defined using the Douglas–Peucker algorithm. Logical analysis of data (LAD) is then used as machine learning, pattern recognition technique for double objectives of detecting the present tool wear class based on the recent sensors' readings of the time-dependent machining variables, and deriving new information about the intercorrelation between the tool wear and the machining variables, by doing pattern analysis. LAD is a data-driven technique which relies on combinatorial optimization and pattern recognition. The accuracy of LAD is compared to that of an artificial neural network (ANN) technique, since ANN is the most familiar machine learning technique. The proposed method is applied to experimental data those are gathered under various machining conditions. The results show that the proposed method detects the tool wear class correctly and with high accuracy.

Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty

This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas–Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment.

Shape-adaptive geometric simplification of heterogeneous line datasets

ABSTRACTLine generalization is an essential data processing operation in geographic information systems and cartography. Many point reduction and simplification algorithms have been developed for this purpose. During the last three decades, several attempts have been made to develop approaches of generalization that adapt to the geometric properties of the processed lines. They typically incorporate line segmentation based on quantitative descriptions of the line shape. Little attention has been paid to the cases in which heterogeneous lines of different geometric character are mixed in one dataset. One common example is administrative borders, which often contain natural and artificial, smooth and sharp, schematic and non-schematic, and regular and irregular shapes. The tuning of the simplification algorithm based on a quantitative description of the shape would be ineffective here, since different algorithms must be applied to lines of different characters. In this article, we present a general method and generalization model for the simplification of such datasets. The properties of schematism, smoothness and regularity are used to differentiate various line characters. Three line characters, including irregular non-schematic, irregular schematic and regular orthogonal schematic, were selected for the current study. The developed generalization model consists of preprocessing, processing and postprocessing stages. Line segmentation based on the detection of different characters is performed during the preprocessing stage. Then, Li–Openshaw, Douglas–Peucker and orthogonal simplification are applied to the appropriate segments in the processing stage. Postprocessing enables the addition of extra regularity to the simplified shape. A visual and quantitative assessment of the results is provided and demonstrates the effectiveness of the developed approach in comparison with the global application of a single simplification algorithm.

DOTS: An online and near-optimal trajectory simplification algorithm

Abstract The last decade witnessed an increasing number of location acquisition equipments such as mobile phone, smart watch etc. Trajectory data is collected with such a high speed that the location based services (LBS) meet challenge to process and take advantage of that big data. Good trajectory simplification (TS) algorithm thus plays an important role for both LBS providers and users as it significantly reduces processing and response time by minimizing the trajectory size with acceptable precision loss. State of the art TS algorithms work in batch mode and are not suitable for streaming data. The online solutions, on the other hand, usually use some heuristics which won’t hold the optimality. This paper proposed a Directed acyclic graph based Online Trajectory Simplification (DOTS) method which solves the problem by optimization. Time complexity of DOTS is O(N2/M). A cascaded version of DOTS with time complexity of O(N) is also proposed. To our best knowledge, this is the first time that an optimal and online TS method is proposed. We find both the normal and cascaded DOTS outperform current TS methods like Douglas–Peucker (Douglas and Peucker, 1973), SQUISH (Muckell et al., 2011) etc. with pretty big margin.

PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

Abstract. Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.