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Journal articles on the topic 'Connected component'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Møller, Jesper, and Rasmus Plenge Waagepetersen. "Markov connected component fields." Advances in Applied Probability 30, no. 1 (1998): 1–35. http://dx.doi.org/10.1239/aap/1035227989.

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A new class of Gibbsian models with potentials associated with the connected components or homogeneous parts of images is introduced. For these models the neighbourhood of a pixel is not fixed as for Markov random fields, but is given by the components which are adjacent to the pixel. The relationship to Markov random fields and marked point processes is explored and spatial Markov properties are established. Extensions to infinite lattices are also studied, and statistical inference problems including geostatistical applications and statistical image analysis are discussed. Finally, simulatio

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2

Møller, Jesper, and Rasmus Plenge Waagepetersen. "Markov connected component fields." Advances in Applied Probability 30, no. 01 (1998): 1–35. http://dx.doi.org/10.1017/s0001867800008053.

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A new class of Gibbsian models with potentials associated with the connected components or homogeneous parts of images is introduced. For these models the neighbourhood of a pixel is not fixed as for Markov random fields, but is given by the components which are adjacent to the pixel. The relationship to Markov random fields and marked point processes is explored and spatial Markov properties are established. Extensions to infinite lattices are also studied, and statistical inference problems including geostatistical applications and statistical image analysis are discussed. Finally, simulatio

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3

He, Lifeng, Yuyan Chao, Kenji Suzuki, and Kesheng Wu. "Fast connected-component labeling." Pattern Recognition 42, no. 9 (2009): 1977–87. http://dx.doi.org/10.1016/j.patcog.2008.10.013.

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4

M⊘ller, Jesper, and Rasmus Waagepetersen. "Markov connected component fields." Advances in Applied Probability 28, no. 2 (1996): 340. http://dx.doi.org/10.1017/s0001867800048400.

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A new class of Gibbsian models with potentials associated with the connected components or homogeneous parts of images is introduced. The relationship with Markov random fields and marked point processes is explored and spatial Markov properties are established. Further, extensions to infinite lattices are studied. Statistical inference problems including geostatistical applications and statistical image analysis are also discussed. Finally, simulation studies are presented which show that the models may be appropriate for a variety of interesting patterns.

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5

Rakhmadi. "Connected Component Labeling Using Components Neighbors-Scan Labeling Approach." Journal of Computer Science 6, no. 10 (2010): 1099–107. http://dx.doi.org/10.3844/jcssp.2010.1099.1107.

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6

Gayathri Devi, G. "Positional Connected Component Labeling Algorithm." Indian Journal of Science and Technology 7, no. 3 (2013): 306–11. http://dx.doi.org/10.17485/ijst/2014/v7i3.5.

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7

Al-Ghour, Samer, and Hanan Al-Saadi. "Soft weakly connected sets and soft weakly connected components." AIMS Mathematics 9, no. 1 (2023): 1562–75. http://dx.doi.org/10.3934/math.2024077.

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<abstract><p>Although the concept of connectedness may seem simple, it holds profound implications for topology and its applications. The concept of connectedness serves as a fundamental component in the Intermediate Value Theorem. Connectedness is significant in various applications, including geographic information systems, population modeling and robotics motion planning. Furthermore, connectedness plays a crucial role in distinguishing between different topological spaces. In this paper, we define soft weakly connected sets as a new class of soft sets that strictly contains the

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8

Bok, Kyoungsoo, Namyoung Kim, Dojin Choi, Jongtae Lim, and Jaesoo Yoo. "Incremental Connected Component Detection for Graph Streams on GPU." Electronics 12, no. 6 (2023): 1465. http://dx.doi.org/10.3390/electronics12061465.

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Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent data transmission when GPU is used. In this paper, we propose a new incremental processing method to solve the problems found in the existing methods for detecting connected components on GPUs. The proposed method minimizes the amount of data to be sent to the GPU by determining the subgraph affected by the graph stream update and by detecting the p

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9

IIJIMA, Yusuke, and Tomo MURAO. "On Connected Component Decompositions of Quandles." Tokyo Journal of Mathematics 42, no. 1 (2019): 63–82. http://dx.doi.org/10.3836/tjm/1502179252.

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10

Umniati, Naeli, Achmad Benny Mutiara, Tubagus Maulana Kusuma, and Suryarini Widodo. "Component-connected Feature for Signature Identification." International Journal on Advanced Science, Engineering and Information Technology 8, no. 3 (2018): 756. http://dx.doi.org/10.18517/ijaseit.8.3.2880.

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11

ZHAO, Xiao, Lifeng HE, Bin YAO, and Yuyan CHAO. "A New Connected-Component Labeling Algorithm." IEICE Transactions on Information and Systems E98.D, no. 11 (2015): 2013–16. http://dx.doi.org/10.1587/transinf.2015edl8135.

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12

Lifeng He, Xiao Zhao, Yuyan Chao, and Kenji Suzuki. "Configuration-Transition-Based Connected-Component Labeling." IEEE Transactions on Image Processing 23, no. 2 (2014): 943–51. http://dx.doi.org/10.1109/tip.2013.2289968.

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13

Matsumoto, T., L. O. Chua, and H. Suzuki. "CNN cloning template: connected component detector." IEEE Transactions on Circuits and Systems 37, no. 5 (1990): 633–35. http://dx.doi.org/10.1109/31.55003.

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14

Chin, Y. C., and A. J. Baddeley. "On connected component Markov point processes." Advances in Applied Probability 31, no. 2 (1999): 279–82. http://dx.doi.org/10.1239/aap/1029955135.

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We note some interesting properties of the class of point processes which are Markov with respect to the ‘connected component’ relation. Results in the literature imply that this class is closed under random translation and independent cluster generation with almost surely non-empty clusters. We further prove that it is closed under superposition. A wide range of examples is also given.

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15

Chin, Y. C., and A. J. Baddeley. "On connected component Markov point processes." Advances in Applied Probability 31, no. 02 (1999): 279–82. http://dx.doi.org/10.1017/s0001867800009113.

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We note some interesting properties of the class of point processes which are Markov with respect to the ‘connected component’ relation. Results in the literature imply that this class is closed under random translation and independent cluster generation with almost surely non-empty clusters. We further prove that it is closed under superposition. A wide range of examples is also given.

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16

Hecquard, Jean, and Raj Acharya. "Connected component labeling with linear octree." Pattern Recognition 24, no. 6 (1991): 515–31. http://dx.doi.org/10.1016/0031-3203(91)90018-z.

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17

Sehgal, Isha. "CONNECTED COMPONENT LABELING FOR BINARY IMAGES." International Journal of Advanced Research 7, no. 8 (2019): 916–27. http://dx.doi.org/10.21474/ijar01/9580.

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18

D. Dorathi Jayaseeli, J., D. Malathi, Sarvesh Karandikar, Aditi Singh, and Gopika S. "Road Extraction using Connected Component Techniques." International Journal of Engineering & Technology 7, no. 4.10 (2018): 823. http://dx.doi.org/10.14419/ijet.v7i4.10.26126.

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Extraction of Roads, Rivers and other map objects is an important step in many military and civilian applications. In this process the information is extracted which possess high efficiency and accuracy and is fed into GIS (Geographical Information System). In this paper, we have explored different algorithms with better efficiency and accuracy. Road extraction can take place for two kinds of roads namely: urban and non-urban roads. Urban roads are more complex to analyze because of their architectural complexity, occlusions created by trees, heavy traffic and extensive network, whereas non-ur

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19

ITO, YASUAKI, and KOJI NAKANO. "LOW-LATENCY CONNECTED COMPONENT LABELING USING AN FPGA." International Journal of Foundations of Computer Science 21, no. 03 (2010): 405–25. http://dx.doi.org/10.1142/s0129054110007337.

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Connected component labeling is a process that assigns unique labels to the connected components of a binary image. The main contribution of this paper is to present a low-latency hardware connected component labeling algorithm for k-concave binary images designed and implemented in FPGA. Pixels of a binary image are given to the FPGA in raster order, and the resulting labels are also output in the same order. The advantage of our labeling algorithm is low latency and to use a small internal storage of the FPGA. We have implemented our hardware labeling algorithm in an Altera Stratix Family FP

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20

Tian, Shiji, Min Lu, Ruikang Wang, and Di’an Liu. "Research on the Control Method of the PV Grid-Connected Inverter under an Asymmetrical Power Grid Fault." Energies 16, no. 22 (2023): 7504. http://dx.doi.org/10.3390/en16227504.

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The negative-sequence current component and harmonic components generated when an asymmetrical fault occurs in the power grid seriously affect the normal operation of the photovoltaic (PV) grid-connected inverter. In order to suppress the negative-sequence current component and the harmonic component of the grid-connected current, and to meet the normal grid-connected operation requirements of the PV grid-connected inverter when asymmetrical faults occur in the grid, this paper proposes the proportional integral double-resonant (PI-DR) current controller, which consists of the PI controller an

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21

Liu, Yan, and You Guang Chen. "Fast Chain Coding of Connected-Component Boundaries." Applied Mechanics and Materials 513-517 (February 2014): 1402–5. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1402.

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A new method to obtain connected component in binary images is presented. The method uses DFA automaton to obtain chain code and label the component boundary It is theoretically proved that the algorithm improves the image encoding efficiency closer to the lowest time consumption.

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22

Shoji, Kenji, and Juichi Miyamichi. "Fast Connected Component Labeling Using Run Information." IEEJ Transactions on Electronics, Information and Systems 121, no. 2 (2001): 392–400. http://dx.doi.org/10.1541/ieejeiss1987.121.2_392.

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23

Kurtz, Camille, Benoit Naegel, and Nicolas Passat. "Connected Filtering Based on Multivalued Component-Trees." IEEE Transactions on Image Processing 23, no. 12 (2014): 5152–64. http://dx.doi.org/10.1109/tip.2014.2362053.

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24

He, Zhenyu, Xin Li, Xinge You, Dacheng Tao, and Yuan Yan Tang. "Connected Component Model for Multi-Object Tracking." IEEE Transactions on Image Processing 25, no. 8 (2016): 3698–711. http://dx.doi.org/10.1109/tip.2016.2570553.

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25

Cruz, J. M., and L. O. Chua. "A CNN chip for connected component detection." IEEE Transactions on Circuits and Systems 38, no. 7 (1991): 812–17. http://dx.doi.org/10.1109/31.135756.

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26

Karthikeyan, V. "License Plate Segmentation Using Connected Component Analysis." IOSR Journal of Electronics and Communication Engineering 4, no. 5 (2013): 18–24. http://dx.doi.org/10.9790/2834-0451824.

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27

Wu, Kesheng, Ekow Otoo, and Kenji Suzuki. "Optimizing two-pass connected-component labeling algorithms." Pattern Analysis and Applications 12, no. 2 (2008): 117–35. http://dx.doi.org/10.1007/s10044-008-0109-y.

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28

Tsin, Yung H. "A Simple 3-Edge-Connected Component Algorithm." Theory of Computing Systems 40, no. 2 (2005): 125–42. http://dx.doi.org/10.1007/s00224-005-1269-4.

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29

Mitchell, Phillip E., and Hong Yan. "Newspaper layout analysis incorporating connected component separation." Image and Vision Computing 22, no. 4 (2004): 307–17. http://dx.doi.org/10.1016/j.imavis.2003.11.001.

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30

Jones, Ronald. "Connected Filtering and Segmentation Using Component Trees." Computer Vision and Image Understanding 75, no. 3 (1999): 215–28. http://dx.doi.org/10.1006/cviu.1999.0777.

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31

Agarwal, P. K., and M. van Kreveld. "Connected component and simple polygon intersection searching." Algorithmica 15, no. 6 (1996): 626–60. http://dx.doi.org/10.1007/bf01940884.

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32

Chin, Y. C., and A. J. Baddeley. "Markov interacting component processes." Advances in Applied Probability 32, no. 3 (2000): 597–619. http://dx.doi.org/10.1239/aap/1013540233.

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A generalization of Markov point processes is introduced in which interactions occur between connected components of the point pattern. A version of the Hammersley-Clifford characterization theorem is proved which states that a point process is a Markov interacting component process if and only if its density function is a product of interaction terms associated with cliques of connected components. Integrability and superpositional properties of the processes are shown and a pairwise interaction example is used for detailed exploration.

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33

Chin, Y. C., and A. J. Baddeley. "Markov interacting component processes." Advances in Applied Probability 32, no. 03 (2000): 597–619. http://dx.doi.org/10.1017/s0001867800010144.

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A generalization of Markov point processes is introduced in which interactions occur between connected components of the point pattern. A version of the Hammersley-Clifford characterization theorem is proved which states that a point process is a Markov interacting component process if and only if its density function is a product of interaction terms associated with cliques of connected components. Integrability and superpositional properties of the processes are shown and a pairwise interaction example is used for detailed exploration.

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34

He, Guofeng, Junfang Lin, Guojiao Li, Yanfei Dong, and Wenjie Zhang. "DC Component Suppression of Grid-Connected Z-Source Inverter Based on Disturbance Observer." Energies 15, no. 15 (2022): 5700. http://dx.doi.org/10.3390/en15155700.

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Regarding the problems of resonance and direct current (DC) components when the Z-source inverter (ZSI) without an isolation transformer is connected to the grid through an LCL filter, this paper proposes a novel DC component suppression strategy for a grid-connected ZSI based on the split capacitor method of disturbance observer (DOB). The split capacitor method is utilized to convert a third-order LCL filter into a first-order one to eliminate the resonance problem. The DC component of the inverter output voltage is regarded as an external disturbance, and the employed DOB is used to observe

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35

Manohar, M., and H. K. Ramapriyan. "Connected component labeling of binary images on a mesh connected massively parallel processor." Computer Vision, Graphics, and Image Processing 45, no. 2 (1989): 133–49. http://dx.doi.org/10.1016/0734-189x(89)90129-1.

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36

Madhavi, G. Bindu, Vijaya Kumar V., and V. Krishna Vakula. "An Effective Slant Detection and Correction Method Based on the Tilted Rectangle Method for Telugu Manuscript Terms." International Journal of Information Technology Project Management 12, no. 4 (2021): 25–37. http://dx.doi.org/10.4018/ijitpm.2021100103.

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This paper proposes an efficient method called tilted rectangle (TR) for detecting and correcting of slant angle of the manuscript Telugu words (MTW). Telugu language is one of India's common languages spoken by over 80 million individuals. The complex characters are attached with some extra marks known as “maatras” and “vatthus,” and it is challenging to detect slant angle. The proposed TR method initially performs preprocessing and identifies a connected component within the given Telugu manuscript word. Then, it estimates the slant angle of each connected component by deriving connected sla

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37

Roth, Christopher. "Logic Tree Analysis of Secondary Nonstructural Systems with Independent Components." Earthquake Spectra 15, no. 3 (1999): 385–95. http://dx.doi.org/10.1193/1.1586049.

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The seismic performance of a nonstructural secondary system depends on the performance of each component of the system, and on the way in which the components are connected. The performance of each component is commonly described using fragility curves. Logic trees may be used to describe the way in which the components are connected. The use of logic tree analysis for secondary systems is briefly reviewed, and the method extended to allow the determination of the sensitivity of the system failure probability to the failure probability of any component of the system. An approximate method is p

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38

Asad, Pedro, Ricardo Marroquim, and Andrea L. e. L. Souza. "On GPU Connected Components and Properties: A Systematic Evaluation of Connected Component Labeling Algorithms and Their Extension for Property Extraction." IEEE Transactions on Image Processing 28, no. 1 (2019): 17–31. http://dx.doi.org/10.1109/tip.2018.2851445.

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39

Jaya, Cokorda Gde Teresna, and I. Gede Arta Wibawa. "Segmentation of Certificate With Connected Component Labeling Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 2 (2020): 119. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p02.

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Certificate is one of the documents that can be used as evidence of ownership or an event. For example, when certificate used as requirement to participate in an event. If a document is made as a requirement, of course the file verification process will be done. Seeing the time optimization problem when verifying the file, the authors carry out research by segmenting important data contained in a certificate as an initial step in the development of an automatic document verification system. The segmentation process carried out in this study uses the Connected Component Labeling method in deter

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40

Putri, Audini Nifira, and I. Putu Gede Hendra Suputra. "Hijaiyah Letter Segmentation Using Connected Component Labeling Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (2020): 249. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p12.

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Arabic letters or Hijaiyah letters recognition is a challenge in itself because one letter consists of more than one character, namely the main character, companion character such as dots and lines, and punctuation called harakat. The image segmentation process is the most important in a character recognition system because it affects the separation of objects in an image. In this research, Hijaiyah letter segmentation aims to separate the letters according to the character of each letter using the Connected Component Labeling (CCL) method. Merging labels on each character will be done by look

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41

Jordan, Jonathan. "The connected component of the partial duplication graph." Latin American Journal of Probability and Mathematical Statistics 15, no. 2 (2018): 1431. http://dx.doi.org/10.30757/alea.v15-53.

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42

Haralampiev, Vladislav. "A Simple Randomized 3-edge Connected Component Algorithm." Serdica Journal of Computing 12, no. 4 (2019): 265–80. http://dx.doi.org/10.55630/sjc.2018.12.265-280.

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Finding the 3-edge connected components of a graph is a well-researched problem for which many algorithms are known. In this paper, we present a new linear-time randomized algorithm for the problem. To the best of our knowledge, this is the first randomized algorithm for partitioning a graph into 3-edge connected components. The algorithm is a composition of simple building blocks, it is easy to understand and implement, and it has no corner cases.

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43

KONNO, Kazuhiro. "Chain-connected component decomposition of curves on surfaces." Journal of the Mathematical Society of Japan 62, no. 2 (2010): 467–86. http://dx.doi.org/10.2969/jmsj/06220467.

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44

Majanga, Vincent, and Serestina Viriri. "Dental Images’ Segmentation Using Threshold Connected Component Analysis." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/2921508.

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Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to

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45

Marsov, V. I., A. M. Kolbasin, V. S. Seleznev, and D. S. Tsarkov. "STATISTICAL CHARACTERISTICS OF THE MULTI-COMPONENT CONNECTED DOSING." V mire nauchnykh otkrytiy, no. 6 (June 21, 2015): 110. http://dx.doi.org/10.12731/wsd-2015-6-11.

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46

Dinstein, Its'Hak, David W. L. Yen, and Myron D. Flickner. "Handling Memory Overflow in Connected Component Labeling Applications." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7, no. 1 (1985): 116–21. http://dx.doi.org/10.1109/tpami.1985.4767627.

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47

Senthilkumar, S. "Connected component detector using Dormand and Prince algorithm." International Journal of Information and Communication Technology 3, no. 2 (2011): 180. http://dx.doi.org/10.1504/ijict.2011.041747.

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48

Han, Yujie, and Robert A. Wagner. "An efficient and fast parallel-connected component algorithm." Journal of the ACM 37, no. 3 (1990): 626–42. http://dx.doi.org/10.1145/79147.214077.

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49

Diaz-del-Rio, Fernando, Pablo Sanchez-Cuevas, Helena Molina-Abril, and Pedro Real. "Parallel connected-Component-Labeling based on homotopy trees." Pattern Recognition Letters 131 (March 2020): 71–78. http://dx.doi.org/10.1016/j.patrec.2019.11.039.

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50

Atmaja, Ratri Dwi, Junartho Halomoan, and Muhammad Ary Murti. "DEFECT DETECTION OF WOOD USING CONNECTED-COMPONENT LABELING." Far East Journal of Electronics and Communications 18, no. 5 (2018): 661–66. http://dx.doi.org/10.17654/ec018050661.

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