Skeleton Extraction Algorithm Research Articles

A Pavement Crack Detection Method via Deep Learning and a Binocular-Vision-Based Unmanned Aerial Vehicle

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification.

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

AbstractThe Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.

A Method for Tomato Plant Stem and Leaf Segmentation and Phenotypic Extraction Based on Skeleton Extraction and Supervoxel Clustering

To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering. To carry out growth and cultivation experiments on tomato plants in a solar greenhouse, we obtained multi-view image sequences of the tomato plants to construct three-dimensional models of the plant. We used Laplace’s skeleton extraction algorithm to extract the skeleton of the point cloud after removing the noise points using a multi-filtering algorithm, and, based on the plant skeleton, searched for the highest point path, height constraints, and radius constraints to separate the stem from the leaf. At the same time, a supervoxel segmentation method based on Euclidean distance was used to segment each leaf. We extracted a total of six phenotypic parameters of the plant: height, stem diameter, leaf angle, leaf length, leaf width and leaf area, using the segmented organs, which are important for the phenotype. The results showed that the average accuracy, average recall and average F1 scores of the stem and leaf segmentation were 0.88, 0.80 and 0.84, and the segmentation indexes were better than the other four segmentation algorithms; the coefficients of determination between the measurement values of the phenotypic parameters and the real values were 0.97, 0.84, 0.88, 0.94, 0.92 and 0.93; and the root-mean-square errors were 2.17 cm, 0.346 cm, 5.65°, 3.18 cm, 2.99 cm and 8.79 cm2. The measurement values of the proposed method had a strong correlation with the actual values, which could satisfy the requirements of daily production and provide technical support for the extraction of high-throughput phenotypic parameters of tomato plants in solar greenhouses.

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

<p>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.</p> <p> </p>

A method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm

Automatic acquisition of tomato canopies’ phenotypic traits is essential for tomato varieties’ selection and scientific cultivation. Due to the infinite growth characteristics of tomato, its organ development is stochastic and the canopies' internal structure of is also complex. These make it challenging to obtain organs’ detailed phenotypic traits. Thus, this work proposed a method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm (ISEA). Firstly, after collecting tomato canopies' point cloud data from multiple perspectives, this work reconstructed its three-dimensional (3D) model accurately. Secondly, the least squares method was used to simplify the spatial contraction model of the Laplace Skeleton Extraction algorithm to obtain the tomatoes’ skeleton point set. On this basic, Greedy algorithm was used to optimise the Edge Collapse algorithm to extract more accurate and reliable skeleton structure. Then, in conjunction with the canopy growth characteristics of the crop and the Intrinsic Shape Signatures (ISS) principle, the simplified skeleton structure was subjected to local principal component analysis (PCA), which achieved the separation of tomatoes’ main stem and leaves. Finally, a modelling algorithm based on Delaunay triangulation was applied to construct the separated organs’ model to calculate phenotypic traits such as stem diameter, leaf area index and average leaf inclination. The calculated results were also compared with the measured values at different growth stages for performance evaluation. The average precision, average recall, average accuracy and micro F1 score of ISEA were 0.9144, 0.7751, 0.7243 and 0.8306, respectively. The overall R2 between calculated values and measured values for stem diameter, leaf area index and average leaf inclination were 0.9638, 0.9067, and 0.9428, and the root mean square errors (RMSE) were 0.3922, 0.0029, and 0.0186, respectively. As a result, the proposed method can extract a simple skeleton from the complex tomato canopy and calculate phenotypic traits accurately. It also provided solid technical support for studying the interaction about “genotype-phenotype-envirotype” in tomato.

In-Water Fish Body-Length Measurement System Based on Stereo Vision.

Fish body length is an essential monitoring parameter in aquaculture engineering. However, traditional manual measurement methods have been found to be inefficient and harmful to fish. To overcome these shortcomings, this paper proposes a non-contact measurement method that utilizes binocular stereo vision to accurately measure the body length of fish underwater. Binocular cameras capture RGB and depth images to acquire the RGB-D data of the fish, and then the RGB images are selectively segmented using the contrast-adaptive Grab Cut algorithm. To determine the state of the fish, a skeleton extraction algorithm is employed to handle fish with curved bodies. The errors caused by the refraction of water are then analyzed and corrected. Finally, the best measurement points from the RGB image are extracted and converted into 3D spatial coordinates to calculate the length of the fish, for which measurement software was developed. The experimental results indicate that the mean relative percentage error for fish-length measurement is 0.9%. This paper presents a method that meets the accuracy requirements for measurement in aquaculture while also being convenient for implementation and application.

Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud

Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV RGB images with photogrammetry. We conducted a comparison experiment by flying a UAV from two altitudes (50 m and 20 m) above a university orchard with several fruit tree species and developed three metrics, namely the F1-score of bifurcation point (FBP), the F1-score of end point (FEP), and the Hausdorff distance (HD) to evaluate the performance of the proposed algorithm. The results show that the average values of FBP, FEP, and HD for the point cloud of fruit tree branches collected at 50 m altitude were 64.15%, 69.94%, and 0.0699, respectively, and those at 20 m were 83.24%, 84.66%, and 0.0474, respectively. This paper provides a branch skeleton extraction method for low-cost 3D digital management of orchards, which can effectively extract the main skeleton from the sparse fruit tree branch point cloud, can assist in analyzing the growth state of different types of fruit trees, and has certain practical application value in the management of orchards.

A novel approach for fracture skeleton extraction from rock surface images

The complexity and diversity of information on rock mass surfaces leads to disparities in many image properties among different rock mass surface images, including image brightness, color, contrast and pixel distribution. As a result, there is currently no unified automatic approach for extracting the rock fracture skeleton from the images. In this paper, according to common features of rock surface images and curvilinearity of fractures which are set as the extraction targets, a dark region curvilinear structure enhancement (DRCSE) algorithm for automatic rock fracture skeleton extraction is proposed. This algorithm mainly relies on the curvilinear structure enhancement filter Frangi in image pre-processing stage. Through multiple image processing, dark region contrast of rock surface images is enhanced while noise is reduced. After this process, the effect of Frangi filtering can be significantly improved. Particularly, by using Homomorphic filtering coupled Gamma transforming (HCG) to enhance dark region contrast, a new edge-entropy (EE) index is proposed to describe the contrast-enhanced image quality. Particle swarm optimization (PSO) is utilized to calculate the value of Gamma transforming parameters that maximize the EE index. The test results show that it has great advantages compared to traditional methods for rock fracture skeleton extraction.

Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning

This paper proposes an image-based method for automated rock fracture segmentation and fracture trace quantification. It is integrated using a CNN-based model named FraSegNet, a skeleton extraction algorithm, and a chain code-based polyline approximation algorithm. A rock tunnel fracture database with a total of 3,000 images of rock tunnel faces is established and selected to train and test the FraSegNet model. A comparison study is further conducted and shows that the FraSegNet model shows advanced performance in pixel-level fracture trace map extraction and noise reduction compared to other deep learning approaches and traditional image edge detection algorithms. Next, the skeletons of the predicted fracture trace maps are extracted and the corresponding polyline for each fracture skeleton is thus obtained and output as a text file composed of key nodes coordinates. The fracture trace characteristics (trace length, dip angle, density, and intensity) are acquired using node-based files. The quantitative evaluation of the proposed method illustrates that it can extract trace occurrences effectively and accurately. A case study of three full scale tunnel sections demonstrates the proposed method to be an efficient approach for acquiring and evaluating 2D fracture occurrences of under-construction rock tunnel faces.

Rank based Approach for Extracting Unit Pixel Width Skeleton

Skeletonization is a transformation of a digital picture component into a subset of the original component. The skeleton is a qualitative tool for matching and analyzing 2D objects. Skeletonization is considered as an important issue because of the close link between an object’s skeleton and its boundary. Arank based skeleton extraction algorithm is presented which iterates itself to remove all the skeleton branches corresponding to the unimportant shape regions depending on the boundary extraction method. The method proposed performance is compared with various methods in the literature such as ZS, LW, and MZS skeletonization algorithms and identified that proposed method out performs well with respect to the performance measures elapsed time, thinning rate, thinning speed, and connectivity.

Visibility-driven skeleton extraction from unstructured points

We present an algorithm for curve skeleton extraction from unstructured points using visibility as a guide. We introduce visible cells inside the point samples. By locating a viewpoint in the space, visible points from the viewpoint are connected to indicate the visible region, which locally captures the interior structure of the points and is called a visible cell. We then analyze and clean the visible cells for noisy and incomplete points. By connecting the cleaned visible cells with a region growing method, we obtain the skeleton along which the visibility of the unstructured points is balanced. No normal computation or ellipse fitting is required by using the proposed method. We show that the proposed skeleton well represents the shape. We experiment with our algorithm on imperfect points and show that comparative curved skeletons are extracted with relative lower computational cost.

MODELLING A CELL TOWER USING SFM: AUTOMATED DETECTION OF STRUCTURAL ELEMENTS FROM SKELETON EXTRACTION ON A POINT CLOUD

Abstract. The surveying and management of telecommunication towers poses a series of engineering challenges. Not only they must be regularly inspected for the purpose of checking for issues that require maintenance interventions, but they are often sub-let by their owners to communication companies, requiring a survey of the many (several thousand per company) installed appliances to check that they respect the established contracts. This requires a surveying methodology that is fast and possibly automated. Photogrammetric techniques using UAV-mounted cameras seem to offer a solution that is both suitable and economical. Our research team was asked to evaluate whether, from the information acquired by small drones it was possible to obtain geometric information on the structure, with what degree of accuracy and what level of detail. The workflow of this process is naturally articulated in three steps: the acquisition, the construction of the point cloud, and the extraction of geometries. The case study is a tower carrying antennas owned by several operators and placed in the industrial district of Cagliari. The article examines the problems found in modelling such structures using point clouds derived from the Structure-from-Motion technique, in order to obtain a model of nodes and beams suitable for the reconstruction of the structure’s geometric elements, and possibly for a finite elements analysis or for populating GIS and BIM, either automatically or with minimal user intervention. In order to achieve this, we have used voxelization and skeleton extraction algorithms to obtain a 3D graph of the structure. The analysis of the results was carried out by varying the parameters relating to the voxel size, which defines the resolution, and the density of the points contained inside each voxel.

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

A hybrid method for skeleton extraction on Kinect sensor data: Combination of L1-Median and Laplacian shrinking algorithms

The distinction of three-dimensional objects is one of the main challenges in computer graphics and computer vision. Distinguishing and recognizing between objects and shapes which are frequently encountered in everyday life is an important problem. In this work, a robust curve skeleton extraction algorithm is introduced on point clouds data for 3D real objects. The curve skeleton of the 3D object is a discrete geometric and topological representation of 3D shapes and maps spatial relationship of the geometric parts according to the graphical structure. Skeleton structure is the integrated stage of an average point clouds data obtained from the existing point cloud. The presented algorithm works on the average metric values of the point clouds and compensates for some missing point clouds that can be found in point clouds generated from objects. The developed method uses a combination of L1-Median and Laplacian shrinking algorithms. Moreover, a curve skeleton can be extracted on the partially deformed point cloud. Thus, curve skeleton becomes convenient to define and process objects used in the geometric modeling. The resulting skeletal structure provides a method of object recognition that can cope with objects having complex geometry.

Skeleton Extraction Algorithm of Chinese Characters in Picture

This paper proposes a novel skeleton extraction algorithm of Chinese characters in picture. In the algorithm, we extract the strokes of horizontal (vertical) strokes firstly based on the definitions. And then, we get the intersection points among the stroke skeletons by extending the contour line. Finally, we thin other strokes based on the maximum inscribed rectangle discriminant and thinning algorithm. And the whole skeleton of Chinese characters is extracted.

Towards Robust Surface Skeleton Extraction and Its Applications in 3D Wireless Sensor Networks

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table (GHT) is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.

R3DG features: Relative 3D geometry-based skeletal representations for human action recognition

Recently introduced cost-effective depth sensors coupled with real-time skeleton extraction algorithms have generated a renewed interest in skeleton-based human action recognition. Most of the existing skeleton-based approaches use either the joint locations or the joint angles to represent the human skeleton. In this paper, we introduce and evaluate a new family of skeletal representations for human action recognition, which we refer to as R3DG features. The proposed representations explicitly model the 3D geometric relationships between various body parts using rigid body transformations, i.e., rotations and translations in 3D space. Using the proposed skeletal representations, human actions are modeled as curves in R3DG feature spaces. Finally, we perform action recognition by classifying these curves using a combination of dynamic time warping, Fourier temporal pyramid representation and support vector machines. Experimental results on five benchmark action datasets show that the proposed representations perform better than many existing skeletal representations. The proposed approach also outperforms various state-of-the-art skeleton-based human action recognition approaches.

A General Framework of Skeleton Extraction in Sensor Networks

In sensor networks, skeleton (also known as medial axis) extraction is recognized as an appealing approach to support many applications, such as load-balanced routing and location-free segmentation. Existing solutions in the literature rely heavily on the identified boundaries, posing severe limitations on the applicability of the skeleton extraction algorithm. In this paper, we conduct the first work of a connectivity-based and boundary-free skeleton extraction scheme in sensor networks, and propose a centrality-and-connectivity-based boundary-free algorithm, which is simple, distributed, and scalable, and can correctly identify a few skeleton nodes and connects them into a meaningful representation of the network, without reliance on any constraint on communication radio model or nodal distribution. The key idea of our algorithm is to exploit the necessary (but not sufficient) condition of skeleton points: the intersection area of the disk centered at a skeleton point $x$ should be the largest one as compared with the other points on the chord generated by $x$ , where the chord is referred to as the line segment connecting $x$ and the tangent point in the boundary. To that end, we present the concept of $r$ -centrality of a point, quantitatively measuring how central a point is. Accordingly, a skeleton point should have the largest value of $r$ -centrality, as compared with the other points on the chord generated by this point. We then propose a distributed algorithm to connect the identified skeleton nodes, while obtaining two by-products, i.e., the boundaries and the segmentation result of the network. We also design a light-weight scheme based on the Voronoi diagram, entitled Voronoi-based skeleton extraction algorithm, that yields a skeleton with less communication overhead while sacrificing slightly the skeleton accuracy, providing a tradeoff between skeleton accuracy and communication cost.

Visualization Technology of the Microstructure of Reservoir Core

Abstract To solve the problem in conventional technologies of analysis and integration in micro scale of reservoir core, virtual reality and visualization technology can be used in the research of reservoir core microstructure. Integrated with practical research and application works, the modeling method of reservoir core and the database store method of microstructure model are analyzed and discussed. A skeleton extraction algorithm based on maximum circumsphere method is proposed to achieve the skeleton model construction of reservoir core. And the visualization of reservoir core microstructure which includes the visualizations of voxel model, surface model, solid model and skeleton model of reservoir core are realized based on OpenGL graphics library. Taking a carbonate as a case study, we develop a set of software named “visualization system of reservoir core microstructure”, which is an effective evidence in the application to scientific research of reservoir core microstructure.

Fast vascular skeleton extraction algorithm

Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels – nodes – that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29 s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.