Skeleton Line Research Articles

A single-pixel and non-redundant branching-based algorithm for nailfold capillary skeleton line extraction.

Static nailfold capillary parameters are important parameters that reflect the health of the human body. Disease onset or progression is often accompanied by changes in the physiological parameters of the nailfold. Hence, the physiological parameters of the nailfold are closely related to the study of disease, with their automated and high-precision measurements playing a crucial role in these studies. Currently, manually measured values of the nailfold's parameters are the gold standard; however, they are time consuming and labor intensive, making the development of automated measurement methods essential. Most automated measurement methods use skeleton lines; however, current skeleton-thinning algorithms have non-single pixels and redundant branches that lead to reduced measurement accuracy. This study proposes a single-pixel and non-redundant branching-based skeleton line extraction algorithm for nailfold capillaries, which is then applied to nailfold static parameter calculations to improve accuracy. The algorithm includes deletion and restoration templates combined with the depth-first search method to obtain single-pixel skeleton lines without redundant branches. These lines are applied to the static nailfold capillary parameter measurement method based on digital image processing to calculate the blood vessel diameter. The results show that the proposed method can obtain the single-pixel skeleton line without the redundant branches that are required for the parameter calculations and improve the accuracy of the nailfold capillary diameter measurement. Experiments showed that the root mean square errors (RMSEs) of the labeled apical diameter, arterial limb diameter, and venous limb diameter were 0.794, 0.756, and 0.830 µm, respectively, when the calculated results were compared with those of the manual calculations. According to the accuracy formula, the accuracy of the method in this study is 90%. We calculated the P values of the algorithmic and manual measurements to P<0.001 and found that the difference in the measurements of the proposed algorithm is statistically significant. Therefore, the method in this study has high sensitivity and specificity for the measurement of normal nailfold capillaries. The proposed algorithm could obtain single-pixel skeleton lines without redundant branches, thereby improving the nailfold static parameter measurement accuracy.

Integrating terrain structure characteristics into generative adversarial nets for hillshade generation

A hillshade is a visualization technique that represents three-dimensional terrain in a two-dimensional plane by illumination mapping. The digital relief shading promotes the visualization of the terrain efficiently using DEM data. However, compared with manual shading, the digital algorithm-based method still has a gap in the visual effect of illumination strategy and terrain generalization. The typical landform characteristics and micro-geomorphic properties usually are destroyed. The reason is that the complexity of illumination rules is hard to summarize for different terrain phenomena. In this study, namely the data-driven artificial intelligent method, an alternative strategy based on generative adversarial networks (GANs) is proposed rather than the rule-based method. The DEM and the terrain skeleton lines are input to the model and part of the manual relief shading of Swisstopo is used for the training samples. Through the GAN training and learning, the manual skill imbedded in the hillshade is discovered in the generation model. The results show that the proposed model performs better than the digital relief shading on various landforms in aesthetic visualization and geo-scientific representation. Compared to other models, including other convolution neural network (CNN) based methods, terrain structure is maintained more significantly through the proposed model.

A dataset of the 1 km-grid road network density in China (2019)

Road network density is a basic index of evaluating regional road development scale and road network development degree. The measurement and monitoring of road network density (as a comprehensive characterization index) plays an important role in the assessment of regional traffic and economic development, and provide reference for urban road management and control measures, which is of great significance to urban planning and municipal management. In this paper, based on the vector data of railway land, highway land, urban and village road land and rail transit in the Third National Land Survey, we extracted the road center line, using the km-grade network production software, to obtain a dataset of the 1km-grid road network density in China in 2019. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including road surface vector extraction, road skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for the study on the preparation for the national territory development plan, the evaluation of plan implementation, the calculation of status factors for regional transportation development, and accessibility calculation.

USV Path Planning in a Hybrid Map Using a Genetic Algorithm with a Feedback Mechanism

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

Method for selecting viewpoints of architectural heritage landscapes

The selection of viewpoints is a crucial aspect in conducting visual impact assessments of architectural heritage. To address this issue, a quantitative viewpoint selection method based on GIS spatial analysis is proposed. The first step involves defining the factors that influence the selection of viewpoints for architectural heritage. Next, the city’s historical architectural areas are divided into units based on courtyards. Subsequently, a candidate set of viewpoints is constructed by extracting the skeleton lines and characteristic points of sub-regions. Finally, following the principles of viewpoint selection, the method achieves the selection of viewpoints for the visual impact assessment of architectural heritage. The Forbidden City is chosen as the practical area for implementation, and the results demonstrate that the viewshed coverage range in the architectural heritage area is as high as 96%. It represents that this approach for visual impact assessments of architectural heritage is more grounded.

An Adaptive Simplification Method for Coastlines Using a Skeleton Line “Bridge” Double Direction Buffering Algorithm

Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.

Collaborative Methods of Resolving Road Graphic Conflicts Based on Cartographic Rules and Generalization Operations

The resolution of road graphic conflicts is a key aspect of map generalization, which involves both scale reduction and the symbolization of map features. This study proposes collaborative methods of road graphic conflict resolution considering different road characteristics. These methods consider both geometric and semantic characteristics, and they incorporate the bend characteristics of roads, the road symbol size, and road semantics. Constrained Delaunay triangulation skeleton lines are used to categorize road graphic conflicts, which are made up of four independent conflict types and four group conflict types. Based on their characteristics, three collaborative methods are designed to deal with the different types of road graphic conflicts: collaboration between deletion and the snake displacement model, collaboration between the snake displacement model and collinearity, and collaboration among simplification, smoothing, and the beam displacement model. Two types of independent conflicts can be processed using only one simple operation. This study summarizes the cartographic rules for resolving road graphic conflicts, and these are used along with geometric features to drive the collaborative methods or one simple operation presented here. The experimental results indicate that the method proposed in this study can effectively resolve road graphic conflicts.

2020年京津冀1 km格网沟渠密度数据集

As important ecological corridors in farmland ecosystems, ditches are not only important for collecting water, holding water as flowing channels, but also valuable for biodiversity conservation as various wetland ecological systems. They play an important role in purifying water quality and maintaining biodiversity. Ditch density is an important indicator of ditch structure in a certain area, directly revealing ditch configuration and farmland irrigation and drainage capacity. Taking the national land change survey data in 2020 as the data source, we extracted the skeleton lines of irrigation canals and ditches by using software, and calculated the density of 1km grid ditches in Beijing, Tianjin and Hebei in 2020 by using km grid production tools. This dataset can be used for research on rural planning, farmland irrigation and drainage convenience, farmland quality evaluation, farmland assets evaluation, etc.

Research of ESPI Stripe Skeleton Line Extraction Based on Improved Fast Parallel Algorithm

Stripe skeleton line method is one of the commonly used methods to extract the phase information of ESPI stripe maps, and the accuracy of the skeleton line extraction determines the accuracy of the stripe map phase information. In this paper, a fast parallel refinement algorithm for ESPI streak image is proposed for the commonly used ZS and OPTA fast parallel refinement algorithms, which improves the deletion template and retention template. It is experimentally verified that the algorithm can effectively reduce the burr bifurcation and fracture phenomenon of the refined image in ESPI skeleton line extraction, which has good practical value.

A dataset of 1 km grid drainage density in China (2019)

Drainage density is an outcome of the combined influence of watershed landform, climate, soil, and vegetation. As a comprehensive characterization index, the measurement and monitoring of drainage density is of great significance to environmental protection and water resources allocation in regional watersheds. This paper uses river water surface vector data from the third National Land Survey, extracting the river center line. Then, the kilometer grid network production software is employed to generate the dataset of 1 km grid drainage density in China. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including river surface vector extraction, river skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for national and regional hydrogeological analysis, soil erosion estimation, planning and layout of basic monitoring station network, etc.

Hybrid Method of Connection Evaluation and Framework Optimization for Building Surface Reconstruction

The three-dimensional (3D) reconstruction of buildings using photogrammetric point clouds is important for many applications, ranging from digital city construction to urban energy consumption analysis. However, problems such as building complexity and point cloud flaws may lead to incorrect modeling, which will affect subsequent steps such as texture mapping. This paper introduces a pipeline for building surface reconstruction from photogrammetric point clouds, employing a hybrid method that combines connection evaluation and framework optimization. Firstly, the plane segmentation method divides building point clouds into several pieces, which is complemented by a proposed candidate plane generation method aimed at removing redundancies and merging similarities. Secondly, the improved connection evaluation method detects potential skeleton lines from different planes. Subsequently, a framework optimization method is introduced to select suitable undirected polygonal boundaries from planes, forming the basis for plane primitives. Finally, by triangulating all plane primitives and filling holes, a building surface polygonal model is generated. Experiments conducted on various building examples provide both qualitative and quantitative evidence that the proposed hybrid method outperforms many existing methods, including traditional methods and deep learning methods. Notably, the proposed method successfully reconstructs the main building structures and intricate details, which can be further used to generate textural models and semantic models. Experimental results validate that the proposed method can be used for the surface reconstruction from photogrammetric point clouds of planar buildings.

Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry

Electronic speckle pattern interferometry (ESPI) is widely used in fields such as materials science, biomedical research, surface morphology analysis, and optical component inspection because of its high measurement accuracy, broad frequency range, and ease of measurement. Phase extraction is a critical stage in ESPI. However, conventional phase extraction methods exhibit problems such as low accuracy, slow processing speed, and poor generalization. With the continuous development of deep learning in image processing, the application of deep learning in phase extraction from electronic speckle interferometry images has become a critical topic of research. This paper reviews the principles and characteristics of ESPI and comprehensively analyzes the phase extraction processes for fringe patterns and wrapped phase maps. The application, advantages, and limitations of deep learning techniques in filtering, fringe skeleton line extraction, and phase unwrapping algorithms are discussed based on the representation of measurement results. Finally, this paper provides a perspective on future trends, such as the construction of physical models for electronic speckle interferometry, improvement and optimization of deep learning models, and quantitative evaluation of phase extraction quality, in this field.

Iterative Approach to the Area Collapse Algorithm for 2D Geometric Objects Representing Long Engineering Structures

Nowadays the amount of gathered raw data emphasizes the importance of further data processing done by skilled engineers aided by computer algorithms. Researchers develop new algorithms for the automated determination of geometrical features, such as symmetry and main axes, skeleton lines, etc. This paper presented a new algorithm to compute an unbranched axis. It was based on the Curve of Minimal Radii (CMR) algorithm, and it overcomes its significant limitations depending on the shape of the input data. To define the accuracy of the results the threshold parameter was introduced. The described approach is more comprehensive than CMR in terms of the object shape. The tests were conducted on several planar objects, and the results were compared with the original CMR axes and Medial Axis.

Slicing-guided Skeleton Extraction Method for 3D Point Clouds of Human Body

&lt;p&gt;In the paper, a slicing-guided method is introduced to extract the curve skeleton from the point cloud body model. Firstly, the dominant eigenvector of body model as slicing direction is chosen adaptively, and the input body model is sliced accordingly. each slice is projected and classified into different regions, and the centroid of each region can be considered as initial skeleton point. Then, those skeleton points are removed outside models, and initial skeleton lines are generated by connecting points based on different region of body model. Finally, the two-step post-processing approach is proposed to improve the initial skeleton results for accurate topological analysis. With the branch point merging strategy, the initial skeleton of the model is optimized. Furthermore, the skeleton lines by interpolation optimization are refined and smoothed. Compared with similar skeleton extraction algorithms, the method proposed in the paper has relatively strong robustness and effectiveness, and can be applied to human body model in point cloud data.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy.

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.

A Method for Regularizing Buildings through Combining Skeleton Lines and Minkowski Addition

With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this author for extracting the skeleton lines of buildings through integrating vector and raster data using jagged building skeleton lines as the input data, a new method is proposed here for regularizing building outlines through combining the skeleton lines with the Minkowski addition algorithm. Since the size and orientation of the structuring elements remain constant in the traditional morphological method, they can easily lead to large changes in the area between the regularized results and area of the original building. In this work, structuring elements are constructed with the adaptive adjustment of size and orientation. The proposed method has an outstanding ability to maintain the area of the original building. The orthogonal characteristics of the building can be better preserved via rotating the structuring elements. Finally, the angular bisector method is used to dissipate conflicts among the redundant vertices in the building outlines. In comparison to the simplification method used in QGIS software, the method proposed in this paper could reduce the variation in the area while maintaining the orthogonal characteristics of the building more significantly.

Mapping gully affected areas by using Sentinel 2 imagery and digital elevation model based on the Google Earth Engine

Gullies pose a great threat to the sustainable development of agricultural production and the ecological environment. Accurate mapping of gully affected areas plays an important role in regional environmental monitoring and management. In this paper, an object-based image analysis method based on the Google Earth Engine (OBIA-GEE) is proposed for mapping gully affected areas by using Sentinel 2 imagery and AW3D30 DEM data. The method utilizes the simple non-iterative clustering (SNIC) algorithm to segment the top-five principal component images derived from Sentinel-2 surface reflectance images. Then, the random forest (RF) algorithm is used for feature importance analysis and gully affected area mapping. Finally, the gully affected area mapping results are further optimized by establishing terrain skeleton lines. The proposed method is applied to five study areas with different landform types on the Chinese Loess Plateau, and the results show that the method achieves good performance in mapping gully affected areas, with an overall accuracy (OA) of 86.44%, an F score (F1) of 0.84, a user’s accuracy (UA) of 84.97%, and a producer’s accuracy (PA) of 83.90%. Compared with the OA of the results of a previous study (OA was 78.8%), the accuracy of the gully affected area mapping results herein improved by 7.64%. The RF feature importance results and findings of different feature combination cases demonstrate that textural information, time series spectral information, and topographic factors are important for mapping gully affected areas. Object-based and pixel-based classification results are compared, and the performance of the OBIA-GEE method is better than that of the pixel-based RF method. In addition, the OBIA-GEE method is highly efficient, which provides the possibility of applying the proposed method in large-scale studies. This research is beneficial to studies related to monitoring gullies and managing soil erosion.

Edge Detection Method for High-Resolution Remote Sensing Imagery by Combining Superpixels with Dual-Threshold Edge Tracking

Edge detection in high-spatial-resolution remote sensing images (HSRIs ) is a key technology for automatic extraction, analysis, and understanding of image information. With respect to the problem of fake edges in image edge detection caused by image noise and the phenomenon of the same class objects reflecting different spectra, this article proposes a novel edge detection method for HSRIs by combin- ing superpixels with dual-threshold edge tracking. First, the image is smoothed using the simple linear iterative clustering algorithm to eliminate the influence of image noise and the phenomenon of the same class objects reflecting different spectra on image edge detec - tion. Second, initial edge detection results of the image are obtained using the dual-threshold edge tracking algorithm. Finally, the initial image edge detection results are post-processed by removing the burrs and extracting skeleton lines to obtain accurate edge detection results. The experimental results confirm that the proposed method outperforms the others and can obtain smooth, continuous, and single-pixel response edge detection results for HSRIs .

Testing a Method Based on an Improved UNet and Skeleton Thinning Algorithm to Obtain Branch Phenotypes of Tall and Valuable Trees Using Abies beshanzuensis as the Research Sample.

Sudden changes in the morphological characteristics of trees are closely related to plant health, and automated phenotypic measurements can help improve the efficiency of plant health monitoring, and thus aid in the conservation of old and valuable tress. The irregular distribution of branches and the influence of the natural environment make it very difficult to monitor the status of branches in the field. In order to solve the problem of branch phenotype monitoring of tall and valuable plants in the field environment, this paper proposes an improved UNet model to achieve accurate extraction of trunk and branches. This paper also proposes an algorithm that can measure the branch length and inclination angle by using the main trunk and branches separated in the previous stage, finding the skeleton line of a single branch via digital image morphological processing and the Zhang-Suen thinning algorithm, obtaining the number of pixel points as the branch length, and then using Euclidean distance to fit a straight line to calculate the inclination angle of each branch. These were carried out in order to monitor the change in branch length and inclination angle and to determine whether plant branch breakage or external stress events had occurred. We evaluated the method on video images of Abies beshanzuensis, and the experimental results showed that the present algorithm has more excellent performance at 94.30% MIoU as compared with other target segmentation algorithms. The coefficient of determination (R2) is higher than 0.89 for the calculation of the branch length and inclination angle. In summary, the algorithm proposed in this paper can effectively segment the branches of tall plants and measure their length and inclination angle in a field environment, thus providing an effective method to monitor the health of valuable plants.

G2 Blending Ball B-Spline Curve by B-Spline

Blending two Ball B-Spline Curves(BBSC) is an important tool in modeling tubular objects. In this paper, we propose a new BBSC blending method. Our method has the following three main contributions: First, we use BBSC instead of ball Bézier to model the blending part to expand the solution space and make the resultant BBSC have better fairness. Second, we consider both the skeleton line and radius of BBSC, which makes the skeleton line and radius consistent. Thirdly, we propose a two-step optimization process to solve the problem of excessive amount of parameters brought by expanding the solution space, so that our method satisfies the real-time.