Connected Components Research Articles

Study of microstructural evolution during operation of electrically connected components and analysis of failure mechanism

Abstract As a key current-carrying structure of high-voltage bushings, the reliability of electrical connection components is crucial to the safe and stable operation of power equipment. To obtain the microstructural evolution of electrical connection components with different deterioration states, CUD strap contactors were deteriorated in different ways, and electron backscatter diffraction technique was used to test the microstructure of strap contactors with different deterioration states. The results showed that compared to the unused contactors, the contact resistance of the contactors under the combined effect of friction and high temperature increased 203.12 times and was in a failed state. During the process from unused state to wear deterioration, high temperature deterioration, and then to eventual failure of the contactors, the average grain size gradually grows from 8.15 μm to 25 μm, the dislocation density gradually decreases from 2.38 × 1014 m−2 to 1.04 × 1014 m−2, and there are a significant proportion of the recrystallized organization. These changes are detrimental to the mechanical properties of the contactors. In addition, the distribution of grain boundaries in the contact area proves the occurrence of over-temperature phenomenon in this area, which will accelerate the deterioration of the contactors and eventually lead to the failure of the component. The relevant conclusions can provide a theoretical basis for the design of electrical connection structure of strap contacts as well as the study of deterioration mechanism.

Research on the Influence of Disc–Drum Connection Bolt Preloading Rotor Assembly Modal Characteristics and Diagnosis Technology

The drum rotor of an aero-engine is connected by one or multiple mounting edges through bolts, and their dynamics are significantly influenced by the preload state of the bolts. Long working hours in challenging environments can result in the deterioration of bolt pre-tightening during assembly or service, which impacts the rotor’s dynamic stability and overall performance. Currently, there are no available methods for detecting the dynamic characteristics of the drum connection components. This paper analyzes the impact of the natural characteristics of the drum composite structure of a high-pressure aero-engine turbine based on the refined finite element method when the preloading state changes. Two conditions of deviation and uneven stiffness distribution were applied to the connected components of the drum. The analysis focused on the impact of the pre-tightening state on its natural frequency. After analyzing the feasibility of identifying the pre-tightening state, two methods are proposed. These methods focus on changes in natural frequency and mode shape, specifically the sensitive natural frequency change method and the mode step change method. The methods proposed in this paper can serve as a reference for evaluating the quality of assembling complex disc–drum structures with multiple bolt connections.

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20–80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Abstract 3138: Topological analysis of spatial transcriptomics reveals different spatial interaction patterns in tumor microenvironment of lung cancer

Abstract Background: As the tumor microenvironment (TME) consists of various cell types with complex spatial interaction, its spatial organization patterns affect response to immune-oncology treatment. Therefore, describing the spatial composition and interaction of cells in the tumor microenvironment (TME) is necessary. Here, we developed a tool, STopover, which adopts topological analysis in spatial transcriptomics to reveal cell-cell colocalization patterns in TME and capture the key components and niche of intercellular communication, and applied it to human lung cancer data. Methods: The spatial colocalization pattern of pairs of features was defined using topological data analysis with Morse filtration. The spatial network of spatial gene expression data was generated for each sample and then the patterns were summarized as connected components (CCs) based on the spatial distance between unit tissue regions and the persistence of each CC. The global and local extent of spatial overlap of a feature pair was calculated as Jaccard indices between extracted CC pairs. We applied STopover to 11 barcode-based spatial transcriptomic data of human lung adenocarcinoma with different PD-L1 expressions. Spatial mapping of cell types in TME was performed by CellDART. In addition, image-based spatial transcriptomic data of lung cancer were also used to find key spatial molecular interactions in TME using STopover. Results: First, STopover disclosed the distinct immune and stromal infiltration patterns in lung cancer tissues. Spatial colocalization of cancer cells and T-cells was heterogeneous and correlated with the immune-related markers such as MHC class I signature. The cancer types were clustered according to spatial colocalization patterns of various immune cells with cancer cells, which showed different infiltration patterns of immune cells. Moreover, STopover could estimate the top cell-cell interaction and emphasize the key locations based on the literature-supported ligand-receptor database. Conclusion: STopover is expected to account for significant spatial cell interactions in tumor-immune and tumor-stromal components and could be utilized as a platform to decipher the mechanisms underlying immune-oncology treatment response. Citation Format: Sungwoo Bae, Hyekyoung Lee, Kwon Joong Na, Dong Soo Lee, Hongyoon Choi, Young Tae Kim. Topological analysis of spatial transcriptomics reveals different spatial interaction patterns in tumor microenvironment of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3138.

FARNet: Fragmented affinity reasoning network of text instances for arbitrary shape text detection

AbstractArbitrary shape text detection is a challenging task in scene text recognition. Driven by deep learning and large‐scale data sets, the detection method based on connected component (CC) has increasingly gained popularity. However, there are still problems of unclear separation of text instances and incorrect component links. Thus, in this paper, the authors propose a novel component connection method, that is, Fragmented Affinity Reasoning Network of Text Instances (FARNet), for arbitrary shape text detection. The network consists of a Weighted Feature Fusion Pyramid Network (WFFPN), Text Fragments Subgraph (TFS), and Dense Graph Attention Network (DGAT), which can be trained end‐to‐end. The WFFPN is used to generate text fragments, TFS and DGAT jointly construct an affinity reasoning network. Since the neighbouring boundaries between text instances may blend them into a single instance, the core idea is to use the WFFPN to divide the text instance into a series of rectangular fragments, the affinity reasoning network infers the affinity between fragments and then links them to rebuild text instances. Extensive experiments on seven challenging datasets (ICDAR2015, MSRA‐TD500, Totaltext, CTW‐1500, ICDAR 2019MLT, ICDAR2019 ArT, and DAST‐1500) demonstrate that the proposed text detector achieves state‐of‐the‐art performance in both on polygon datasets and quadrilateral datasets. The code is available at https://github.com/giganticpower/FARNet.

The t/k-Diagnosability and a t/k Diagnosis Algorithm of the Data Center Network BCCC under the MM* Model

The evaluation of the fault diagnosis capability of a data center network (DCN) is important research in measuring network reliability. The g-extra diagnosability is defined under the condition that every component except the fault vertex set contains at least g+1 vertices. The t/k diagnosis strategy is that the number of fault nodes does not exceed t, and all fault nodes can be isolated into a set containing up to k fault-free nodes. As an important data center network, BCube Connected Crossbars (BCCC) has many excellent properties that have been widely studied. In this paper, we first determine that the g-extra connectivity of BCn,k for 0≤g≤n−1. Based on this, we establish the g-extra conditional diagnosability of BCn,k under the MM* model for 1≤g≤n−1. Next, based on the conclusion of the largest connected component in g-extra connectivity, we prove that the t/k-diagnosability of BCn,k under the MM* model for 1≤k≤n−1. Finally, we present a t/k diagnosis algorithm on BCCC under the MM* model. The algorithm can correctly identify all nodes at most k nodes undiagnosed. So far, t/k-diagnosability and diagnosis algorithms for most networks in the MM* model have not been studied.

River extraction from Gaofen-1 Satellite images combined with relative total variation and stroke width transform

Extraction of rivers from remote sensing images is crucial for understanding the utilization of water resources. Because rivers have similar and consistent widths in a consistent range of remote sensing images, the stroke width transform (SWT) algorithm has a good effect on river extraction. However, the algorithm is susceptible to the influence of features around the river. A river extraction method combining relative total variation (RTV) and SWT was proposed to obtain river area information with high accuracy. First, the near-infrared channel of the image was selected as the input and the RTV model was used for texture smoothing and edge enhancement. Thereafter, the image edge map calculateds by the Canny operator was used as the input of the SWT to obtain the stroke width map. Subsequently, the connectivity components were labeled, and a specific filter was constructed according to the geometric characteristics of the river to filter out nonriver noise. The retained connected components were filled with holes, and a highly accurate river basin map was generated. In this study, six images of rivers flowing through four different landforms, including woodland, urban, mountainous, and cultivated land, were captured by the Gaofen-1 satellite for validation and comparison analysis. The experimental results demonstrated that the method’s completeness, accuracy, and extraction quality in this study could be maintained above 95%. Compared with the mainstream methods of the Otsu method, normalized difference water index, U-Net, and direct use of the SWT algorithm, the method in this study is significantly better in terms of extraction effect and algorithm stability. This can effectively improve the accuracy of river segmentation.

Accelerating Graph-Connected Component Computation With Emerging Processing-In-Memory Architecture

Computing the connected component (CC) of a graph is a basic graph computing problem, which has numerous applications like graph partitioning and pattern recognition. Existing methods for computing CC suffer from memory wall problems because of the frequent data transmission between CPU and memory. To overcome this challenge, in this article, we propose to accelerate CC computation with the emerging processing-in-memory (PIM) architecture through an algorithm–architecture co-design manner. The innovation lies in computing CC with bitwise logical operations (such as AND and OR), and the customized data flow management methods to accelerate computation and reduce energy consumption. As a proof of concept, experimental results with computational spin-transfer torque magnetic RAM (STT-MRAM) arrays demonstrate on average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$19.8\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12.4\times $ </tex-math></inline-formula> speedups compared with the CPU and GPU implementations, and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$35.4 \times $ </tex-math></inline-formula> energy efficiency improvement over the CPU implementation. Moreover, we investigate the potential associations between graph computing and bitwise Boolean logic, which could help design more general in-memory graph computing accelerators in the future.

Computing Medial Axis Transform with Feature Preservation via Restricted Power Diagram

We propose a novel framework for computing the medial axis transform of 3D shapes while preserving their medial features via restricted power diagram (RPD). Medial features, including external features such as the sharp edges and corners of the input mesh surface and internal features such as the seams and junctions of medial axis, are important shape descriptors both topologically and geometrically. However, existing medial axis approximation methods fail to capture and preserve them due to the fundamentally under-sampling in the vicinity of medial features, and the difficulty to build their correct connections. In this paper we use the RPD of medial spheres and its affiliated structures to help solve these challenges. The dual structure of RPD provides the connectivity of medial spheres. The surfacic restricted power cell (RPC) of each medial sphere provides the tangential surface regions that these spheres have contact with. The connected components (CC) of surfacic RPC give us the classification of each sphere, to be on a medial sheet, a seam, or a junction. They allow us to detect insufficient sphere sampling around medial features and develop necessary conditions to preserve them. Using this RPD-based framework, we are able to construct high quality medial meshes with features preserved. Compared with existing sampling-based or voxel-based methods, our method is the first one that can preserve not only external features but also internal features of medial axes.

Script-Independent Text Segmentation from Document Images

Document image analysis finds broad application in the digital world for the purpose of information retrieval. This includes optical character recognition (OCR), indexing of digital libraries, web image processing, etc. One of the important steps in this field is text segmentation. This segmentation becomes complicated for the documents containing text of uneven spacing and characters of varying font sizes. In this paper, script-independent text-line segmentation and word segmentation algorithms are presented. Fast marching method is used for text-line segmentation, whereas wavelet transform with connected components (CCs) labeling is used for word segmentation. Fast marching method is used as a region growing process that detects potential text-lines. For word segmentation, energy map is calculated using wavelet transform to create text-blocks. Both the proposed algorithms are evaluated on different databases containing documents of different scripts, where highest text-line and word segmentation accuracies of 98.9% and 99.1%, respectively, are obtained.

NIMG-23. AI-BASED CONNECTED COMPONENT MARKERS OF BRAIN PERITUMORAL MICROENVIRONMENT USING WATER RESTRICTION INFORMATION

Abstract PURPOSE Glioblastoma is the most aggressive adult brain tumor, with heterogeneous neoplastic cell peritumoral infiltration. Characterization of this infiltrative peritumoral heterogeneity is an unmet clinical need that could contribute to strengthening our understanding of this disease. We propose novel AI-based markers of infiltration using deep-learning (DL) based on water restriction caused by infiltration, identified by diffusion tensor imaging (DTI). These markers could contribute to precision/personalized medicine, towards influencing clinical decision-making, including planning for biopsies, surgery, and radiation. METHOD: We automatically extracted peritumoral patches from free water volume fraction maps (FW-VF) of a retrospective cohort of 44 brain metastases and 66 glioblastomata patients and labelled them as high- and low- free water, respectively. An AI/DL model was then trained on these patches to distinguish differences in water restriction. Our trained AI/DL model was then applied on FW-VF of 264 hold-out glioblastoma patients (survival:0.43-76.9 months, age:21-88, 104 females) to generate a peritumoral microenvironment index (PMI) map quantifying infiltrative heterogeneity. Connected components (CCs) of high PMI values were calculated and their descriptive characteristics of size, number, shape, directionality, and spatial location, were extracted as AI-based markers. Gaussian mixture model clustering was then applied on these markers to determine if their representative infiltrative peritumoral heterogeneity can capture overall survival differences, by partitioning the patients into three clusters: low, moderate, and high risk. RESULTS The log-rank test yielded significant differences (p&amp;lt; 10-5) between low- and high-risk patients, (HR= 0.47, 95% CI:0.34-0.65; P&amp;lt; 0.005). Average PMI values were significantly greater in high-risk patients (P&amp;lt; 0.05). CONCLUSION We introduced novel AI-based markers of infiltration in the peritumoral microenvironment, using information of water restriction extracted from DTI. Our proposed markers can capture overall survival differences, based on the patterns of infiltration using DTI-based characterization of the water restriction, that show promise as clinically relevant prognostic biomarkers.

An efficient ROI detection algorithm for Bangla text extraction and recognition from natural scene images

This research work plays a significant role in finding information from the scene images to fulfill the demand of real life applications like detection of license plate, navigation of robot and helping the visually impaired persons. Here, a new algorithm has been proposed and applied on the scene images to extract Region of Interest (ROI). All the Bangla words are then separated from a sentence by analyzing and applying the Connected Component (CC) method along with bounding box technology. Another new algorithm has been proposed and applied to apart and bring-out Bangla characters from the Bangla words. This algorithm works by the method of vertical scanning of the images of Bangla words. Finally, the extracted characters are recognized by using the Support Vector Machine (SVM) as a classifier which works with Histogram of Oriented Gradient (HOG) features. There are 500 scene images with variations in colors, writing styles and orientations in our designed database. The proposed algorithm yields the accuracy 92.70% and 93.23% in extraction of ROI and character respectively. In the recognition of Bangla characters (digits, Basic characters, and joined characters), the average accuracy is 99.16%. The recognition accuracy of Bangla characters using Convolutional Neural Network (CNN) is also calculated and the obtained result is 83.52%.

Identification of Debris‐Flow Channels Using High‐Resolution Topographic Data: A Case Study in the Quebrada del Toro, NW Argentina

AbstractResolving Earth's surface at the meter scale is essential for an improved understanding of the dynamics of mass‐movement processes. In this study, we explore the applicability and potential of digital elevation models (DEMs) derived from stereophotogrammetry to detect debris‐flow channels in the Quebrada del Toro in the northwestern Argentine Andes. Our analysis relies on a high‐resolution (3 m) DEM created from SPOT‐7 tri‐stereo satellite data. We carefully validated DEM quality with ∼6,000 differential GPS points and identified optimal parameters for DEM generation in high‐relief terrain. After multiple processing steps, we achieved an accuracy of 0.051 ± 1.915 m (1σ) using n = 3,139 control points with cm precision. Previous studies have used the drainage area and slope framework to identify topographic signatures of debris flows within a catchment. We built upon this and investigated individual river‐channel segments using connected‐component (CC) analysis on meter‐scale topographic data. We define CC as segments of similar slope along the channel profile. Based on seven manually identified debris‐flow catchments, we developed a debris‐flow similarity index using component length and mean channel‐segment slope and identified channel segments that have likely been shaped by debris flows. The presented approach has the potential to resolve intra‐catchment variability of transport processes, allows to constrain the extent of debris‐flow channels more precisely than slope‐area analysis, and highlights the versatility of combined space‐ and field‐based observations for natural‐hazard assessments.

BRMP-04. AI-based biomarker of the peritumoral region using tissue microstructure

Abstract PURPOSE Glioblastomas, the most common malignant brain tumor [BS1], infiltrate into peritumoral brain structures, making clinical management challenging. An unmet need is to develop a biomarker that reliably characterize infiltration in the peritumoral region, where surgical biopsy or resection may be hazardous. Diffusion tensor imaging (DTI) with multicompartment modeling can characterize extracellular free water, providing unique information of the tissue microstructure that is able to capture this heterogeneity. We propose a novel biomarker based on peritumoral tissue microstructure, using deep-learning on DTI-based free water map. METHOD Peritumoral regions were automatically segmented for 136 patients with brain tumors (86 glioblastomas and 50 metastases, ages 23–87 years, 65 females). We trained a Convolutional Neural Network (CNN) on free-water maps using automatically defined patches in the peritumoral area from glioblastomas and metastases, labeled as low free-water and high free-water to extract a microstructural index for each voxel. To extract the biomarker, we grouped peritumoral voxels into connected components (CCs) where adjacent voxels have high (&amp;gt;0.9) microstructural index values. Two independent test sets related to two clinically significant problems were evaluated: i) metastases vs. glioblastomas; ii) glioma patients categorized into short and long survival groups and the number of CCs were statistically compared. RESULT Two-sample t-tests showed significant group difference in the number of CCs between metastases and glioblastomas (p&amp;lt; 0.05), and short and long-survivals (p&amp;lt;0.05) with higher number of CCs in metastases and long-survivals, which suggests smaller number of voxels in CCs. CONCLUSION The proposed biomarker based on CCs of microstructural index captures the differences in infiltration of the peritumoral region, showing larger CCs in glioblastomas and short-survivals corresponding to higher infiltration. CLINICAL IMPORTANCE The proposed biomarker provides a novel insight into the peritumoral microenvironment and can be derived from clinically feasible DTI data, providing new possibilities for the diagnosis and treatment of glioblastoma.

Coherent network partitions: Characterizations with cographs and prime graphs

We continue to study coherent partitions of graphs whereby the vertex set is partitioned into subsets that induce biclique spanned subgraphs. The problem of identifying the minimum number of edges to obtain biclique spanned connected components (CNP), called the coherence number, is NP-hard even on bipartite graphs. Here, we propose a graph transformation geared towards obtaining an O(log⁡n)-approximation algorithm for the CNP on a bipartite graph with n vertices. The transformation is inspired by a new characterization of biclique spanned subgraphs. In addition, we study coherent partitions on prime graphs, and show that finding coherent partitions reduces to the problem of finding coherent partitions in a prime graph. Therefore, these results provide future directions for approximation algorithms for the coherence number of a given graph.

A high-performance approach to detecting small targets in long-range low-quality infrared videos

Since targets are small in long-range infrared (IR) videos, it is challenging to accurately detect targets in those videos. In this paper, we propose a high-performance approach to detecting small targets in long-range and low-quality infrared videos. Our approach consists of a video resolution enhancement module, a proven small target detector based on local intensity and gradient (LIG), a connected component (CC) analysis module, and a track association module known as Simple Online and Real-time Tracking (SORT) to connect detections from multiple frames. Extensive experiments using actual mid-wave infrared (MWIR) videos in range between 3500 and 5000 m from a benchmark dataset clearly demonstrated the efficacy of the proposed approach. In the 5000 m case, the F1 score has been improved from 0.936 without SORT to 0.977 with SORT.

Bayesian damage recognition in document images based on a joint global and local homogeneity model

Physical damages (such as torn-offs and scratches) are commonly seen in historical documents. Recognition of such damages is currently absent in digitization-and-information-extraction (DIE) systems but crucial for automatic document comprehension and exploitation. In this paper we propose a generic damage recognition (DR) method based on a joint global and local modeling of the text homogeneity (TH) pattern exhibited in document images. More specifically, a connected component (CC) based formulation is developed as a global homogeneity measure, where TH is characterized using a probabilistic graph model for a coarse recognition of damaged regions. A multi-resolution analysis (MRA) of TH is further developed for a granular within-CC recognition of damage pixels, where the disparity between damage and text pixels is characterized by exploiting neighborhood transitions. This enables the formulation of a local homogeneity measure, where the neighborhood transition around an individual pixel is modeled using the propagation of the approximation coefficients of a stationary wavelet transform (SWT). The proposed global and local homogeneity measures are integrated as a joint likelihood in a Bayesian model with a Markov random field (MRF) prior, where DR is formulated as a maximum a posterior (MAP) inference which is addressed using Markov Chain Monte Carlo (MCMC) sampling. The resulting algorithm is tested on a set of real-life historical newspaper images containing damages of varying size and shape. The performance of the algorithm is evaluated using both F-measures and the Intersection-over-Union (IoU) metric, where test results demonstrate the promising potential of the proposed method.

VColor*: a practical approach for coloring large graphs

Graph coloring has a wide range of real world applications, such as in the operations research, communication network, computational biology and compiler optimization fields. In our recent work [1], we propose a divide-and-conquer approach for graph coloring, called VColor. Such an approach has three generic subroutines. (i) Graph partition subroutine: VColor partitions a graph G into a vertex cut partition (VP), which comprises a vertex cut component (VCC) and small non-overlapping connected components (CCs). (ii) Component coloring subroutine: VColor colors the VCC and the CCs by efficient algorithms. (iii) Color combination subroutine: VColor combines the local colors by exploiting the maximum matchings of color combination bigraphs (CCBs). VColor has revealed some major bottlenecks of efficiency in these subroutines. Therefore, in this paper, we propose VColor*, an approach which addresses these efficiency bottlenecks without using more colors both theoretically and experimentally. The technical novelties of this paper are the following. (i) We propose the augmented VP to index the crossing edges of the VCC and the CCs and propose an optimized CCB construction algorithm. (ii) For sparse CCs, we propose using a greedy coloring algorithm that is of polynomial time complexity in the worst case, while preserving the approximation ratio. (iii) We propose a distributed graph coloring algorithm. Our extensive experimental evaluation on real-world graphs confirms the efficiency of VColor*. In particular, VColor* is 20X and 50X faster than VColor and uses the same number of colors with VColor on the Pokec and PA datasets, respectively. VColor* also significantly outperforms the state-of-the-art graph coloring methods.

A Multiscale Method for Road Network Extraction from High-Resolution SAR Images Based on Directional Decomposition and Regional Quality Evaluation

Road network is an important part of modern transportation. For the demands of accurate road information in practical applications such as urban planning and disaster assessment, we propose a multiscale method to extract road network from high-resolution synthetic aperture radar (SAR) images, which consists of three stages: potential road area segmentation, preliminary network generation, and road network refinement. Multiscale analysis is implemented using an image pyramid framework together with a fixed-size filter. First, a directional road detector is designed to highlight road targets in feature response maps. Subsequently, adaptive fusion is performed independently at each image scale, followed by a threshold method to produce potential road maps. Then, binary maps are decomposed according to the obtained direction information. For each connected component (CC), quality evaluation is conducted to further distinguish road segments and polynomial curve fitting is adopted as a thinning method. Multiscale information fusion is realized through the weighted sum of road curves. Finally, tensor voting and spatial regularization are employed to generate the final road network. Experiments on three TerraSAR images demonstrate the effectiveness of the proposed algorithm to extract road network completely and correctly.

Simulink Modeling Based on SoC for Single-Pass Connected Component Labeling

Simulink Modeling Based on SoC for Single-Pass Connected Component Labeling