Journal articles: 'Connected components labeling (CCL)'

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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 (November 24, 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 looking for the Euclidean distance value from adjacent centroids. The experiment succeeded in segmenting each Hijaiyah character with an accuracy value of 86%.

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Dharmajaya, Gede Putra, and I. Dewa Made Bayu Atmaja Darmawan. "Tempo Tracking on Guru Ding Dong Transcript using Connected Component Labeling (CCL) Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 2 (January 8, 2020): 137. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p05.

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Music notation is a system of writing musical expressions as outlined in the form of symbols in the form of numbers or blocks. Music notation is used to document the composer's work in the form of songs so that it can be used by the public. In Balinese culture there is also a musical notation called Guru Ding Dong's Notation. This study discusses the segmentation of guru ding dong transcript to determine the tempo of each notation using the Connected Component Labeling method and the rule-based method. CCL algorithm applies Graph theory, where all pixels in an area that have a relationship with obeying the rules of pixel proximity will become a new image. The image that can be processed by the CCL algorithm is a binary image. In addition, this study also uses the image preprocessing method for initial data processing, namely grayscaling and binarization. The system built for research uses the MATLAB 2017b application. The results of the test resulted in an accuracy of the successful identification of characters and the tempo of each notation of 82%, this is influenced by the preprocessing process where there is a lot of noise in the image.

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Ammar, Maan, Muhammad Shamdeen, Mazen Kasedeh, Kinan Mansour, and Waad Ammar. "Using Distance Measure based Classification in Automatic Extraction of Lungs Cancer Nodules for Computer Aided Diagnosis." Signal & Image Processing : An International Journal 12, no. 3 (June 30, 2021): 25–43. http://dx.doi.org/10.5121/sipij.2021.12303.

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We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying lungs connected components into nodule and not-nodule. We explain also using Connected Component Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some morphological operations. Our tests have shown that the performance of the introduce method is high. Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we tested the method by some images of healthy persons and demonstrated that the overall performance of the method is satisfactory.

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Dung, Le, and Makoto Mizukawa. "Fast Hand Feature Extraction Based on Connected Component Labeling, Distance Transform and Hough Transform." Journal of Robotics and Mechatronics 21, no. 6 (December 20, 2009): 726–38. http://dx.doi.org/10.20965/jrm.2009.p0726.

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In hand gesture recognition or hand tracking systems relied on hand modeling methods, it is usually required to extract from a hand image some hand features. This paper presents a new robust method based on connected component labeling (CCL), distance transform (DT) and Hough transform (HT) to fast and precisely extract the center of the hand, the directions and the fingertip positions of all outstretched fingers on a skin color detection image. First, the method uses a simple but reliable technique that is performed on both the connected component labeling image and the distance transform image to extract the center of the hand and a set of features pixels, which are called distance-based feature pixels. Then, the Hough transform is calculated on these feature pixels to detect all outstretched fingers as lines. From the line detection result, the finger directions and the fingertip positions are determined easily and precisely. This method can be carried out fast and accurately, even when the skin color detection image includes hand, faces and some noise. Moreover, the number of distance-based feature pixels is usually not so high; therefore, the line detection process based on the Hough transform can be performed very fast. That can satisfy the demands of a real-time human-robot interaction system based on hand gestures or hand tracking.

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Suriani, Uci, and Tri Basuki Kurniawan. "Comparing the Prediction of Numeric Patterns on Form C1 Using the K-Nearest Neighbors (K-NN) Method and a Combination of K-Nearest Neighbors (K-NN) with Connected Component Labeling (CCL)." Journal of Information Systems and Informatics 5, no. 4 (December 3, 2023): 1569–80. http://dx.doi.org/10.51519/journalisi.v5i4.592.

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Indonesia's elections serve as a cornerstone of its democratic system, with the active participation of its citizens being of paramount importance. To bolster transparency and civic engagement during these elections, the SITUNG system (Election Result Information System) is employed for the tabulation of election results. However, the current tabulation process remains manual, potentially leading to data entry errors and a reduced accuracy of election outcomes. This research endeavor seeks to enhance the efficiency and accuracy of election result tabulation by employing the K-Nearest Neighbors (K-NN) method for recognizing numeric patterns on Form C1, both independently and in combination with Connected Component Labeling (CCL). The K-NN method demonstrates a commendable 60.0% accuracy in recognizing numeric patterns from the original Form C1 data. However, when combined with CCL, the accuracy drops to 51.2%. This research makes a significant contribution by simplifying the tabulation process and improving the accuracy of election results in Indonesia through the application of the K-NN method. The technology is anticipated to fortify democracy by promoting a more transparent and participatory electoral process for the citizens.

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Aissou, B., and A. Belhadj Aissa. "AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 191–95. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-191-2020.

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Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.

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Kowalczyk, Marcin, Piotr Ciarach, Dominika Przewlocka-Rus, Hubert Szolc, and Tomasz Kryjak. "Real-Time FPGA Implementation of Parallel Connected Component Labelling for a 4K Video Stream." Journal of Signal Processing Systems 93, no. 5 (April 1, 2021): 481–98. http://dx.doi.org/10.1007/s11265-021-01636-4.

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AbstractIn this paper, a hardware implementation in reconfigurable logic of a single-pass connected component labelling (CCL) and connected component analysis (CCA) module is presented. The main novelty of the design is the support of a video stream in 2 and 4 pixel per clock format (2 and 4 ppc) and real-time processing of 4K/UHD video stream (3840 x 2160 pixels) at 60 frames per second. We discuss several approaches to the issue and present in detail the selected ones. The proposed module was verified in an exemplary application – skin colour areas segmentation – on the ZCU 102 and ZCU 104 evaluation boards equipped with Xilinx Zynq UltraScale+ MPSoC devices.

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Tian, Yifei, Wei Song, Long Chen, Yunsick Sung, Jeonghoon Kwak, and Su Sun. "A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming." Sensors 20, no. 8 (April 18, 2020): 2309. http://dx.doi.org/10.3390/s20082309.

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Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.

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Rakhmadi. "Connected Component Labeling Using Components Neighbors-Scan Labeling Approach." Journal of Computer Science 6, no. 10 (October 1, 2010): 1099–107. http://dx.doi.org/10.3844/jcssp.2010.1099.1107.

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Asano, Tetsuo, and Hiroshi Tanaka. "In-Place Algorithm for Connected Components Labeling." Journal of Pattern Recognition Research 5, no. 1 (2010): 10–22. http://dx.doi.org/10.13176/11.218.

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Bolelli, Federico, Stefano Allegretti, Lorenzo Baraldi, and Costantino Grana. "Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling." IEEE Transactions on Image Processing 29 (2020): 1999–2012. http://dx.doi.org/10.1109/tip.2019.2946979.

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Banjarnahor, Jepri, Well Friend Giawa, Wanda Pratama Putra Simamora, Dina Pratiwi, Rafif Rahil, and Evta Indra. "ANALISIS SISTEM MENGGUNAKAN METODE SUBSTRAKSI UNTUK MODEL LALU LINTAS KENDARAAN SECARA REALTIME." Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) 5, no. 1 (August 13, 2021): 1–6. http://dx.doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v5i1.1891.

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Pengetahuan tentang teknologi visi komputer semakin maju serta sejalan dengan kebutuhan akan sistem yang terkendali secara terus menerus, menyebabkan penelitian di bidang visi komputer akan terus dilakukan. Kebutuhan akan sistem kendali pengaturan volume kendaraan (TrafikPlan) sudah sangat dibutuhkan pada era sekarang ini dikarenakan tingkat penjualan kendaraan semakin meningkat tetapi peningkatan jalan yang tersedia tidak mencukupi. Penelitian ini mencoba untuk membuat sebuah model lalu lintas dengan beberapa metode pada visi komputer hough transform sebagai batas area yang akan dideteksi serta connected compenent labeling (CCL) dalam penghitungan jumlah kepadatan pada area yang dideteksi memungkinkan pengaturan kendaraan menggunakan sistem secara realtime akan terwujud. Penelitian ini mengambil inputan citra berupa video dan akan dieksekusi pada pengujian program dengan tahapan proses dari segmentasi, deteksi tepi, hough transform, eliminasi, subtraksi, grayscale dan threshold, morfologi, dan CCL. Hasil pengujian akan mendeteksi jumlah kendaraan serta persentase kepadatan jalan yang dilalui apakah sepi, sedang atau padat.

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Zhang, Danyang, Huadong Ma, and Linqiang Pan. "A gamma-signal-regulated connected components labeling algorithm." Pattern Recognition 91 (July 2019): 281–90. http://dx.doi.org/10.1016/j.patcog.2019.02.022.

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Lianqiang, Niu, Chen Xin, Peng Min, and Zhang Gang. "Connected components labeling based on union-find operations applied to connected branches." Journal of Intelligent & Fuzzy Systems 32, no. 5 (April 24, 2017): 3739–48. http://dx.doi.org/10.3233/jifs-169306.

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Netzel, Pawel, and Tomasz F. Stepinski. "Connected components labeling for giga-cell multi-categorical rasters." Computers & Geosciences 59 (September 2013): 24–30. http://dx.doi.org/10.1016/j.cageo.2013.05.014.

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Grana, Costantino, Daniele Borghesani, and Rita Cucchiara. "Optimized Block-Based Connected Components Labeling With Decision Trees." IEEE Transactions on Image Processing 19, no. 6 (June 2010): 1596–609. http://dx.doi.org/10.1109/tip.2010.2044963.

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ITO, YASUAKI, and KOJI NAKANO. "LOW-LATENCY CONNECTED COMPONENT LABELING USING AN FPGA." International Journal of Foundations of Computer Science 21, no. 03 (June 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 FPGA, and evaluated the performance. The implementation result shows that for a 10-concave binary image of 2048 × 2048, our connected component labeling algorithm runs in approximately 70ms and its latency is approximately 750µs.

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Kranke, Derrick, Shant Barmak, Eugenia Weiss, and Aram Dobalian. "The Application of a Self-Labeling Approach among Military-Connected Adolescents in a Public School Setting." Health & Social Work 44, no. 3 (March 4, 2019): 193–201. http://dx.doi.org/10.1093/hsw/hlz007.

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AbstractThe contextual factors and individual responses to the labeling of military-connected adolescents as “being in a military family” is an understudied yet important phenomenon. Minimal research construes the experience of being in a military family as a label applied to military-connected populations by people in society. However, social environmental factors associated with school setting among military-connected adolescents being in a military family have common components to the process of self-labeling. This article seeks to explore the concept and application of self-labeling by (a) providing a literature review of self-labeling among military-connected adolescents and (b) relying on modified labeling theory to identify any consistencies or potential nuances. The analysis of the process is strictly hypothetical, but could help to account for widely varying responses, sequence of events, and underlying reasons for the behaviors among some military-connected adolescents identified in the literature review and in light of the U.S. protracted military involvement in Iraq and Afghanistan. Authors conclude by highlighting the need for future research to assess the adequacy of this self-labeling framework to ensure the healthy development of military-connected youths.

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Muhammad Daffa Khairi, Bedy Purnama, Imamura Kosuke, and Miki Abo. "Estimate the Region of Interest, Movement and Magnitude of Ciliary Beat with Dense Optical Flow." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 11 (August 8, 2024): 66–79. http://dx.doi.org/10.3991/ijoe.v20i11.48029.

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In this study, we analyze mucociliary transport (MCT) by measuring the magnitude and identifying regions of ciliary beats using high-frame-rate microscopic videos. Our methodology, integrating dense optical flow (DOF), connected component labeling (CCL), Butterworth filter, and Fast Fourier Transform (FFT), captures ciliary movement and magnitude. We focus on region extraction, quantification of ciliary activity, and classification of power and recovery strokes in ciliary beat frequency (CBF), which are crucial for evaluating MCT efficiency. Our approach was able to extract the ciliary region semi-automatically, obtain the CBF, and visualize the ciliary movement in each frame. Despite dataset challenges and limited ground truth, our approach shows a promising result for ciliary dynamics research and medical diagnostics. We hope for future open-source datasets with ground-truth ciliary beat patterns to enable developing and evaluating automated ciliary analysis techniques, leading to improved assessment.

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Bolelli, Federico, Michele Cancilla, Lorenzo Baraldi, and Costantino Grana. "Toward reliable experiments on the performance of Connected Components Labeling algorithms." Journal of Real-Time Image Processing 17, no. 2 (March 2, 2018): 229–44. http://dx.doi.org/10.1007/s11554-018-0756-1.

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Stamatovic, Biljana, and Roman Trobec. "Cellular automata labeling of connected components in n-dimensional binary lattices." Journal of Supercomputing 72, no. 11 (May 23, 2016): 4221–32. http://dx.doi.org/10.1007/s11227-016-1761-4.

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Pradnyawati, Putu Indah, and I. Gede Arta Wibawa. "Character Segmentation to the Case Study Image of Quadratic Equation Expression." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 3 (January 25, 2020): 285. http://dx.doi.org/10.24843/jlk.2020.v08.i03.p10.

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The introduction of digital mathematical expressions can be said to be unusual because mathematical expressions consist of various symbols. The introduction of mathematical expressions can be divided into two main steps, namely the introduction of symbols and structural analysis. Segmentation of an image is an important part in the recognition of handwritten mathematical expressions, because segmentation is the first step of the recognition process. In this study, we will present the process of handwriting image segmentation for mathematical expressions in the form of quadratic equations using the Connected Component Labeling (CCL) method. From the results of the research conducted, it can be concluded that the segmentation process has succeeded in segmenting the handwritten images of mathematical expressions in the form of quadratic equations by producing the characters (compound characters) that make up mathematical expressions and grouping the ranks and basic numbers of quadratic equations.

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Ranganathan, N., R. Mehrotra, and S. Subramanian. "A high speed systolic architecture for labeling connected components in an image." IEEE Transactions on Systems, Man, and Cybernetics 25, no. 3 (March 1995): 415–23. http://dx.doi.org/10.1109/21.364855.

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GU, Qingyi, Takeshi TAKAKI, and Idaku ISHII. "A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling." IEICE Transactions on Information and Systems E95-D, no. 2 (2012): 636–45. http://dx.doi.org/10.1587/transinf.e95.d.636.

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Yu Xiaoyu, 于潇宇, 郭玉波 Guo Yubo, 陈刚 Chen Gang, and 叶东 Ye Dong. "Real-Time Point Feature Extraction Based on Connected Components Labeling and Distributed Computing." Acta Optica Sinica 35, no. 2 (2015): 0210001. http://dx.doi.org/10.3788/aos201535.0210001.

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HE, LIFENG, YUYAN CHAO, and KENJI SUZUKI. "A RUN-BASED ONE-AND-A-HALF-SCAN CONNECTED-COMPONENT LABELING ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 04 (June 2010): 557–79. http://dx.doi.org/10.1142/s0218001410008032.

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This paper presents a run- and label-equivalence-based one-and-a-half-scan algorithm for labeling connected components in a binary image. Major differences between our algorithm and conventional label-equivalence-based algorithms are: (1) all conventional label-equivalence-based algorithms scan all pixels in the given image at least twice, whereas our algorithm scans background pixels once and object pixels twice; (2) all conventional label-equivalence-based algorithms assign a provisional label to each object pixel in the first scan and relabel the pixel in the later scan(s), whereas our algorithm assigns a provisional label to each run in the first scan, and after resolving label equivalences between runs, by using the recorded run data, it assigns each object pixel a final label directly. That is, in our algorithm, relabeling of object pixels is not necessary any more. Experimental results demonstrated that our algorithm is highly efficient on images with many long runs and/or a small number of object pixels. Moreover, our algorithm is directly applicable to run-length-encoded images, and we can obtain contours of connected components efficiently.

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Diederichs, Claas, and Sergej Fatikow. "FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics." International Journal of Intelligent Mechatronics and Robotics 3, no. 1 (January 2013): 27–37. http://dx.doi.org/10.4018/ijimr.2013010103.

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Object-detection and classification is a key task in micro- and nanohandling. The microscopic imaging is often the only available sensing technique to detect information about the positions and orientations of objects. FPGA-based image processing is superior to state of the art PC-based image processing in terms of achievable update rate, latency and jitter. A connected component labeling algorithm is presented and analyzed for its high speed object detection and classification feasibility. The features of connected components are discussed and analyzed for their feasibility with a single-pass connected component labeling approach, focused on principal component analysis-based features. It is shown that an FPGA implementation of the algorithm can be used for high-speed tool tracking as well as object classification inside optical microscopes. Furthermore, it is shown that an FPGA implementation of the algorithm can be used to detect and classify carbon-nanotubes (CNTs) during image acquisition in a scanning electron microscope, allowing fast object detection before the whole image is captured.

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Gabbur, Prasad, Hong Hua, and Kobus Barnard. "A fast connected components labeling algorithm and its application to real-time pupil detection." Machine Vision and Applications 21, no. 5 (January 24, 2009): 779–87. http://dx.doi.org/10.1007/s00138-009-0183-1.

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Thurfjell, Lennart, Ewert Bengtsson, and Bo Nordin. "A new three-dimensional connected components labeling algorithm with simultaneous object feature extraction capability." CVGIP: Graphical Models and Image Processing 54, no. 4 (July 1992): 357–64. http://dx.doi.org/10.1016/1049-9652(92)90083-a.

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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 (January 2019): 17–31. http://dx.doi.org/10.1109/tip.2018.2851445.

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Fatichah, Chastine, Joko Lianto Buliali, Ahmad Saikhu, and Silvester Tena. "A HYBRID FUZZY MORPHOLOGY AND CONNECTED COMPONENTS LABELING METHODS FOR VEHICLE DETECTION AND COUNTING SYSTEM." International Journal on Smart Sensing and Intelligent Systems 9, no. 2 (2016): 765–79. http://dx.doi.org/10.21307/ijssis-2017-894.

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Wu, Shao Bao, and Dong Sheng Shen. "Blind Detection of Tampered JPEG Image Based on Blocking Artifacts of Double Quantization." Applied Mechanics and Materials 263-266 (December 2012): 3104–7. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.3104.

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In this paper, a novel approach is proposed to detect JPEG image forgery based on the inconsistencies of quantization tables. The primary quantization table of tempered image is firstly estimated. Then the blocking artifact measure (BAM) of each 8×8 DCT coefficient block is computed using the estimated quantization matrix, and the BAMs of all blocks are sorted in descending order. Select a certain number of blocks of which the BAM is in the front of the order. Labeling the connected components of blocks in order to eliminate some abnormal blocks and gain the connected doctored blocks. Experimental results demonstrate the validity of the proposed approach.

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Gilani, S. A. N., M. Awrangjeb, and G. Lu. "FUSION OF LIDAR DATA AND MULTISPECTRAL IMAGERY FOR EFFECTIVE BUILDING DETECTION BASED ON GRAPH AND CONNECTED COMPONENT ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 65–72. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-65-2015.

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Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building’s occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m2 and 10 m2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m2 in area.

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Pérez-Aguila, Ricardo. "Efficient Boundary Extraction from Orthogonal Pseudo-Polytopes: An Approach Based on the D-EVM." Journal of Applied Mathematics 2011 (2011): 1–29. http://dx.doi.org/10.1155/2011/937263.

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This work is devoted to contribute with two algorithms for performing, in an efficient way, connected components labeling and boundary extraction from orthogonal pseudo-polytopes. The proposals are specified in terms of the extreme vertices model in the -dimensional space (D-EVM). An overview of the model is presented, considering aspects such as its fundamentals and basic algorithms. The temporal efficiency of the two proposed algorithms is sustained in empirical way and by taking into account both lower dimensional cases (2D and 3D) and higher-dimensional cases (4D and 5D).

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ALNUWEIRI, HUSSEIN M. "A FAST RECONFIGURABLE NETWORK FOR GRAPH CONNECTIVITY AND TRANSITIVE CLOSURE." Parallel Processing Letters 04, no. 01n02 (June 1994): 105–15. http://dx.doi.org/10.1142/s0129626494000120.

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This paper presents a VLSI array for labeling the connected components of a graph on N nodes in O(r) steps using a reconfigurable bus of width m bits, such that [Formula: see text] and 1≤r≤m. The network architecture consists of an array of simple logic nodes which are connected by a reconfigurable bus. To solve a problem on N nodes, the array uses N processors and N(N−1)/2 switches. The proposed connectivity and transitive closure algorithms are based on a processor indexing scheme which employs constant-weight codes. It is shown that when r is a constant, then the algorithm takes O(1) time using a bus of width O(N1/r), and when r=[ log N/ loglog N], the algorithm takes O( log N/ loglog N) time using a bus of width O( log N) bits.

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Kurniasari, Arvita Agus, Ali Ridho Barakbah, and Achmad Basuki. "Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features." EMITTER International Journal of Engineering Technology 7, no. 1 (June 15, 2019): 223–42. http://dx.doi.org/10.24003/emitter.v7i1.361.

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The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the usersâ€™ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)â€™s approach with 15 kernels.Â For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.

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Zhou, Chuanyou, Yongming He, Lu Wang, Shaohua Li, Siyu Yu, Yisheng Liu, and Wei Dong. "A Method for Enhancing the Simulation Continuity of the Snesim Algorithm in 2D Using Multiple Search Trees." Energies 17, no. 5 (February 22, 2024): 1022. http://dx.doi.org/10.3390/en17051022.

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Multiple-point geostatistics (MPS) has more advantages than two-point geostatistics in reproducing the continuity of geobodies in subsurface reservoir modeling. For fluvial reservoir modeling, the more continuous a channel, the more consistent it is with geological knowledge in general, and fluvial continuity is also of paramount importance when simulating fluid flow. Based on the pixel-based MPS algorithm Snesim, this study proposes a method that utilizes multiple search trees (MSTs) to enhance simulation continuity in 2D fluvial reservoir modeling. The objective of the MST method is to capture complete data events from a training image (TI), which aims to achieve enhanced continuity in fluvial reservoir sublayer modeling. By resorting to search neighborhoods based on their proximity to the central node of the data template, multiple data templates that correspond to the MSTs will be generated. Here, four data templates were generated by arranging the relative search neighborhood coordinates in ascending and descending order with respect to the central node. Parallel computing was tried for the construction of the search trees. This work calculated the conditional probability distribution function (CPDF) of the simulating nodes by averaging the CPDFs derived from the MSTs, and double retrieval was employed to filter out the search trees that possessed an inaccurate local CPDF for the simulating nodes. In addition, the connected component labeling (CCL) method was introduced to evaluate the simulation continuity in MPS. The results indicated that the MST method can enhance the simulation continuity of the Snesim algorithm by reproducing the fine connectivity of channel facies in 2D fluvial reservoir modeling.

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Wattanavekin, Theeraphol, Taiki Ogata, Tatsunori Hara, and Jun Ota. "Mobile Robot Exploration by Using Environmental Boundary Information." ISRN Robotics 2013 (July 18, 2013): 1–11. http://dx.doi.org/10.5402/2013/954610.

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We present the method of exploration using environmental boundary information for an indoor map generation problem of a mobile robot. We introduce an exploration method by (i) integration of the exploration method with Reaction-Diffusion Equation on a Graph (RDEG) and connected components labeling and (ii) a replanning framework in updating exploration plan for the currently obtained sensor information. Our approach has been implemented in simulation environments and has been compared with two existing methods: frontier-based exploration method and zig-zag method. The results demonstrate the efficiency of our approach over others. Lastly, the approach was implemented and tested on an actual robot, demonstrating its efficiency in a real-world situation.

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Binti, Shaonil, Adison G. Linder, Philip T. Edeen, and David S. Fay. "A conserved protein tyrosine phosphatase, PTPN-22, functions in diverse developmental processes in C. elegans." PLOS Genetics 20, no. 8 (August 22, 2024): e1011219. http://dx.doi.org/10.1371/journal.pgen.1011219.

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Protein tyrosine phosphatases non-receptor type (PTPNs) have been studied extensively in the context of the adaptive immune system; however, their roles beyond immunoregulation are less well explored. Here we identify novel functions for the conserved C. elegans phosphatase PTPN-22, establishing its role in nematode molting, cell adhesion, and cytoskeletal regulation. Through a non-biased genetic screen, we found that loss of PTPN-22 phosphatase activity suppressed molting defects caused by loss-of-function mutations in the conserved NIMA-related kinases NEKL-2 (human NEK8/NEK9) and NEKL-3 (human NEK6/NEK7), which act at the interface of membrane trafficking and actin regulation. To better understand the functions of PTPN-22, we carried out proximity labeling studies to identify candidate interactors of PTPN-22 during development. Through this approach we identified the CDC42 guanine-nucleotide exchange factor DNBP-1 (human DNMBP) as an in vivo partner of PTPN-22. Consistent with this interaction, loss of DNBP-1 also suppressed nekl-associated molting defects. Genetic analysis, co-localization studies, and proximity labeling revealed roles for PTPN-22 in several epidermal adhesion complexes, including C. elegans hemidesmosomes, suggesting that PTPN-22 plays a broad role in maintaining the structural integrity of tissues. Localization and proximity labeling also implicated PTPN-22 in functions connected to nucleocytoplasmic transport and mRNA regulation, particularly within the germline, as nearly one-third of proteins identified by PTPN-22 proximity labeling are known P granule components. Collectively, these studies highlight the utility of combined genetic and proteomic approaches for identifying novel gene functions.

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Kim, Hyun-Koo, Kook-Yeol Yoo, Ju H. Park, and Ho-Youl Jung. "Traffic Light Recognition Based on Binary Semantic Segmentation Network." Sensors 19, no. 7 (April 10, 2019): 1700. http://dx.doi.org/10.3390/s19071700.

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A traffic light recognition system is a very important building block in an advanced driving assistance system and an autonomous vehicle system. In this paper, we propose a two-staged deep-learning-based traffic light recognition method that consists of a pixel-wise semantic segmentation technique and a novel fully convolutional network. For candidate detection, we employ a binary-semantic segmentation network that is suitable for detecting small objects such as traffic lights. Connected components labeling with an eight-connected neighborhood is applied to obtain bounding boxes of candidate regions, instead of the computationally demanding region proposal and regression processes of conventional methods. A fully convolutional network including a convolution layer with three filters of (1 × 1) at the beginning is designed and implemented for traffic light classification, as traffic lights have only a set number of colors. The simulation results show that the proposed traffic light recognition method outperforms the conventional two-staged object detection method in terms of recognition performance, and remarkably reduces the computational complexity and hardware requirements. This framework can be a useful network design guideline for the detection and recognition of small objects, including traffic lights.

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Gargoum, Suliman A., James C. Koch, and Karim El-Basyouny. "A Voxel-Based Method for Automated Detection and Mapping of Light Poles on Rural Highways using LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (July 24, 2018): 274–83. http://dx.doi.org/10.1177/0361198118787657.

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The number of light poles and their position (in terms of density and offset off the roadside) have significant impacts on the safe operation of highways. In current practice, inventory of such information is performed in periodic site visits, which are tedious and time consuming. This makes inventory and health monitoring of poles at a network level extremely challenging. To relieve the burden associated with manual inventory of poles, this paper proposes a novel algorithm which can automatically obtain such information from remotely sensing data. The proposed algorithm works by first tiling point cloud data collected using light detection and ranging (LiDAR) technology into manageable data tiles of fixed dimensions. The data are voxelized and attributes for each data voxel are calculated to classify them into ground and nonground points. Connected components labeling is then used to perform 3D clustering of the data voxels. Further clustering is performed using a density-based clustering to combine connected components of the same object. The final step involves classifying different objects into poles and non-poles based on a set of decision rules related to the geometric properties of the clusters. The proposed algorithm was tested on a 4 km rural highway segment in Alberta, Canada, which had substantial variation in its vertical alignment. The algorithm was accurate in detecting nonground objects, including poles. Moreover, the results also highlight the importance of considering the length of the highway and its terrain when detecting nonground objects from LiDAR.

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Fiedler, Christian, Paul-Philipp Jacobs, Marcel Müller, Silke Kolbig, Ronny Grunert, Jürgen Meixensberger, and Dirk Winkler. "A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data." Brain Sciences 11, no. 7 (June 30, 2021): 876. http://dx.doi.org/10.3390/brainsci11070876.

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Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

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Fu, Gang, Lei Zhao, Erin Dymek, Yuqing Hou, Kangkang Song, Nhan Phan, Zhiguo Shang, Elizabeth F. Smith, George B. Witman, and Daniela Nicastro. "Structural organization of the C1a-e-c supercomplex within the ciliary central apparatus." Journal of Cell Biology 218, no. 12 (October 31, 2019): 4236–51. http://dx.doi.org/10.1083/jcb.201906006.

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Nearly all motile cilia contain a central apparatus (CA) composed of two connected singlet microtubules with attached projections that play crucial roles in regulating ciliary motility. Defects in CA assembly usually result in motility-impaired or paralyzed cilia, which in humans causes disease. Despite their importance, the protein composition and functions of the CA projections are largely unknown. Here, we integrated biochemical and genetic approaches with cryo-electron tomography to compare the CA of wild-type Chlamydomonas with CA mutants. We identified a large (>2 MD) complex, the C1a-e-c supercomplex, that requires the PF16 protein for assembly and contains the CA components FAP76, FAP81, FAP92, and FAP216. We localized these subunits within the supercomplex using nanogold labeling and show that loss of any one of them results in impaired ciliary motility. These data provide insight into the subunit organization and 3D structure of the CA, which is a prerequisite for understanding the molecular mechanisms by which the CA regulates ciliary beating.

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Sahare, Parul, Jitendra V. Tembhurne, Mayur R. Parate, Tausif Diwan, and Sanjay B. Dhok. "Script-Independent Text Segmentation from Document Images." International Journal of Ambient Computing and Intelligence 13, no. 1 (January 1, 2022): 1–21. http://dx.doi.org/10.4018/ijaci.313967.

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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.

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Hijikata, T., T. Murakami, M. Imamura, N. Fujimaki, and H. Ishikawa. "Plectin is a linker of intermediate filaments to Z-discs in skeletal muscle fibers." Journal of Cell Science 112, no. 6 (March 15, 1999): 867–76. http://dx.doi.org/10.1242/jcs.112.6.867.

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Plectin is a versatile linker protein which is associated with various types of cytoskeletal components and/or filaments including intermediate filaments, and its deficiency causes the disruption of myofibrils, or muscular dystrophy. To better understand the functional role of plectin in skeletal muscle fibers, we have examined the topological and structural relationships of plectin to intermediate filaments and Z-discs in rat diaphragm muscles by confocal and immunoelectron microscopy. Immunofluorescence analysis revealed that plectin was colocalized with desmin at the periphery of Z-discs. This plectin localization around Z-discs was constantly maintained irrespective of the contracted or extended state of the muscle fibers, suggesting either direct or indirect association of plectin with Z-discs. Immunogold labeling in skinned muscle fibers clearly demonstrated that plectin-labeled fine threads linked desmin intermediate filaments to Z-discs and connected intermediate filaments to each other. These results indicate that through plectin threads desmin intermediate filaments form lateral linkages among adjacent Z-discs, preventing individual myofibrils from disruptive contraction and ensuring effective force generation.

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Nowosad, J., T. F. Stepinski, and M. Iwicki. "A METHOD FOR UNIVERSAL SUPERCELLS-BASED REGIONALIZATION (PRELIMINARY RESULTS)." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 337–44. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-337-2022.

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Abstract. Geospatial data comes in various forms, including multi and hyperspectral images but also rasters of local composition, local time series, local patterns, etc. Thus, we generalize the SLIC algorithm to work with a library of different data distance measures that are pertinent to geospatial rasters. This contribution includes a description of the generalized SLIC algorithm and a demonstration of its application to the regionalization of the raster of local compositions (of land cover classes). Two workflows were tested, both starting with SLIC preprocessing. In the first, superpixels are subject to regionalization using the graph-partitioning algorithm. In the second, superpixels are first clustered using the K-means algorithm, followed by regions delineation using the connected components labeling. These two workflows are compared visually and quantitatively. Based on these comparisons, coupling of superpixels with a graph-partitioning algorithm is the preferred choice. Finally, we propose using the SLIC superpixel preprocessing algorithm for the task of regionalization of various geospatial data in the same way as it is used for the task of image segmentation in computer vision.

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Kollman, Justin M., Alex Zelter, Eric G. D. Muller, Bethany Fox, Luke M. Rice, Trisha N. Davis, and David A. Agard. "The Structure of the γ-Tubulin Small Complex: Implications of Its Architecture and Flexibility for Microtubule Nucleation." Molecular Biology of the Cell 19, no. 1 (January 2008): 207–15. http://dx.doi.org/10.1091/mbc.e07-09-0879.

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The γ-tubulin small complex (γ-TuSC) is an evolutionarily conserved heterotetramer essential for microtubule nucleation. We have determined the structure of the Saccharomyces cerevisiae γ-TuSC at 25-Å resolution by electron microscopy. γ-TuSC is Y-shaped, with an elongated body connected to two arms. Gold labeling showed that the two γ-tubulins are located in lobes at the ends of the arms, and the relative orientations of the other γ-TuSC components were determined by in vivo FRET. The structures of different subpopulations of γ-TuSC indicate flexibility in the connection between a mobile arm and the rest of the complex, resulting in variation of the relative positions and orientations of the γ-tubulins. In all of the structures, the γ-tubulins are distinctly separated, a configuration incompatible with the microtubule lattice. The separation of the γ-tubulins in isolated γ-TuSC likely plays a role in suppressing its intrinsic microtubule-nucleating activity, which is relatively weak until the γ-TuSC is incorporated into higher order complexes or localized to microtubule-organizing centers. We propose that further movement of the mobile arm is required to bring the γ-tubulins together in microtubule-like interactions, and provide a template for microtubule growth.

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Xie, Bingteng, Yang Pu, Fan Yang, Wei Chen, Wei Yue, Jihong Ma, Na Zhang, et al. "Proteomic Mapping and Targeting of Mitotic Pericentriolar Material in Tumors Bearing Centrosome Amplification." Cancer Research 82, no. 14 (May 27, 2022): 2576–92. http://dx.doi.org/10.1158/0008-5472.can-22-0225.

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Abstract Recent work has made it clear that pericentriolar material (PCM), the matrix of proteins surrounding centrioles, contributes to most functions of centrosomes. Given the occurrence of centrosome amplification in most solid tumors and the unconventional survival of these tumor cells, it is tempting to hypothesize that gel-like mitotic PCM would cluster extra centrosomes to defend against mitotic errors and increase tumor cell survival. However, because PCM lacks an encompassing membrane, is highly dynamic, and is physically connected to centrioles, few methods can decode the components of this microscale matrix. In this study, we took advantage of differential labeling between two sets of APEX2-centrosome reactions to design a strategy for acquiring the PCM proteome in living undisturbed cells without synchronization treatment, which identified 392 PCM proteins. Localization of ubiquitination promotion proteins away from PCM was a predominant mechanism to maintain the large size of PCM for centrosome clustering during mitosis in cancer cells. Depletion of PCM gene kinesin family member 20A (KIF20A) caused centrosome clustering failure and apoptosis in cancer cells in vitro and in vivo. Thus, our study suggests a strategy for targeting a wide range of tumors exhibiting centrosome amplification and provides a proteomic resource for future mining of PCM proteins. Significance: This study identifies the proteome of pericentriolar material and reveals therapeutic vulnerabilities in tumors bearing centrosome amplification.

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Gong, Liang, Xiaofeng Du, Chenhui Lin, Kai Zhu, Chengliang Liu, and Wanqi Liang. "Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm." Mathematical Problems in Engineering 2021 (July 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/4021426.

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Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis.

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Liang, Bo, and Wuwei Feng. "Bearing Fault Diagnosis Based on ICEEMDAN Deep Learning Network." Processes 11, no. 8 (August 14, 2023): 2440. http://dx.doi.org/10.3390/pr11082440.

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Bearing fault diagnosis has evolved from machine learning to deep learning, addressing the issues of performance degradation in deep learning networks and the potential loss of key feature information. This paper proposes a fault diagnosis method for rolling bearing faults based on ICEEMDAN combined with the Hilbert transform (ICEEMDAN-Hilbert) and a residual network (ResNet). Firstly, the collected fault vibration signals are classified as fault samples and randomly sampled with a fixed length. The IMF components obtained by decomposing the bearing fault vibration signals using ICEEMDAN are able to maximize the restoration of fault vibrations. Then, the IMF components are transformed from one-dimensional time-domain signals to two-dimensional time-frequency domain images using Hilbert transformation. The RGB color images can be directly used in deep learning models without the need for manual labeling of a large amount of data, thereby avoiding the loss of key feature information. The ResNet network incorporates the attention mechanism (CBAM) structure for the precise extraction of fault features, enabling a more detailed classification of fault features. Additionally, the residual network effectively addresses the problem of performance degradation in multi-layer network models. Finally, transfer learning is applied in the deep learning network by freezing the training layer parameters and training the fully connected layer. This effectively solves the problem of insufficient data in real operating conditions, which hinders deep training of the model, while also reducing the training time. By combining the ResNet network with the convolutional block attention module (CBAM) structure, the model completes the recognition and training of time-frequency images for rolling bearing faults. The results demonstrate that the ResNet with CBAM model has strong fault feature extraction capabilities, achieving higher accuracy, 7–12% higher than other conventional network models, and exhibiting superior diagnostic performance compared to other deep learning models.

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Journal articles on the topic 'Connected components labeling (CCL)'

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