Готові списки джерел за темами / Connected components labeling (CCL)

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Добірка наукової літератури з теми "Connected components labeling (CCL)"

Автор: Grafiati

Опубліковано: 2 листопада 2024

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Статті в журналах з теми "Connected components labeling (CCL)"

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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Більше джерел

Дисертації з теми "Connected components labeling (CCL)"

Sundström, Alex, and Victor Ähdel. "Is GPGPU CCL worth it? : A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186270.

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Connectedcomponentlabeling(CCL)isatraditionallysequentialproblem that is hard to parallelize. This report aims to test the performance of solving CCL using massively parallel hardware through GPGPU. To achieve this several CCL algorithms were researched and implemented using C++ and OpenCL. The results showed an improvement of up to a factor of 2, which is insigniﬁcant when also considering memory transfer. In conclusion, performing CCL on the GPU is not worth it if the data has to ﬁrst be transferred to and from the GPU.
Etikettering av sammansatta komponenter (CCL) är ett traditionellt sekventiellt problem som är svårt att parallellisera. Denna rapport ämnar atttestaprestandanavattlösaCCLmedanvändningavmassivtparallell hårdvara genom metoden GPGPU. För att uppnå detta undersöktes och implementerades ett ﬂertal CCL algoritmer i C++ och OpenCL Resultaten pekar på en förbättring upp till en faktor av 2, vilket är obetydligt när man också tar hänsyn till minnesöverföringen. Sammanfattningsvis så är det ej värt att utföra CCL med GPGPU om data även måste överföras till och från GPU.

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Babilotte, Killian. "Étude d'endommagement sous choc en dynamique moléculaire par développement d'un algorithme d'analyse in-situ massivement parallèle." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP085.

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Dans le contexte de l'étude en condition extrême de la matière condensée, de nombreuses questions restent encore ouvertes pour comprendre certains phénomènes, notamment à cause de la difficulté de leur étude expérimentale. Selon les phénomènes étudiés, l'insuffisance de données expérimentale peut limiter la modélisation des phénomènes, ou, lorsque les modèles existent, elle peut empêcher leur calibration ou d'évaluer leur pertinence. La dynamique moléculaire (DM) est une technique de choix pour étudier ces phénomènes car elle permet de simuler la réponse d'un système uniquement à partir de la description des interactions atomiques, pour lesquelles des modèles bien éprouvés existent souvent. Cependant, une des contreparties de cette technique est qu'elle nécessite une grande quantité d'atomes pour atteindre une échelle suffisamment représentative des phénomènes étudiés et pouvoir ainsi les observer en s'affranchissant au mieux des effets de tailles. Dans la dernière décennie, de nombreux efforts ont été fait pour adapter les codes de DM aux dernières architectures de supercalculateurs et permettent maintenant de simuler des systèmes à plusieurs milliards d'atomes. Ces simulations de DM sont devenues de véritables expériences numériques et génèrent de gigantesque quantités données à traiter. Désormais, le temps de post-traitement et d'analyse de ces données peut devenir prépondérant comparativement à celui de la simulation et devient un enjeu essentiel. Dans cette thèse, nous avons développé un algorithme d'analyse de simulation de DM capable d'analyser ces simulations de plusieurs milliards d'atomes avec un coût de calcul de l'ordre du pourcent du temps total de simulation. L'algorithme détecte et caractérise des zones d'intérêts dans la simulation à partir de critères définis par l'utilisateur, le rendant ainsi très versatile. Nous avons fondé cet algorithme sur les techniques d'étiquetage en composantes connexes parallélisé en mémoire partagée que nous avons étendu à un parallélisme hybride en mémoire partagée et distribuée. Cette extension à la parallélisation en mémoire distribuée permet d'une part de traiter la quantité de données générée par les simulations, et nous a permis d'autre part d'incorporer notre analyse dans le code de DM du CEA exaStamp pour un traitement in-situ des simulations. Ce traitement in-situ de la simulation permet de diminuer la quantité de données à stocker sur disque et d'augmenter la fréquence d'analyse sur une simulation comparée à ce qui pourrait être fait en post-traitement. Une caractérisation des erreurs associés aux mesures réalisées par notre analyse a été effectuée, le rendant directement exploitable par des physiciens. Nous l'avons en particulier appliqué à divers cas physiques: d'éjection de matière type “micro-jetting” et “splashing” où des détections d'agrégats sont nécessaires, ainsi que des simulations d'endommagement sous choc (écaillage) impliquant la détection de vides
In the perspective of the study of condensed matter under extreme conditions, many questions remain open in order to understand some phenomena, due to the difficulty of studying them experimentally. Depending on the phenomena studied, the lack of experimental data may prevent the modeling of these phenomena, or, when models do exist, it may prevent their calibration or settling which one to use. Molecular dynamics (MD) is the technique of choice for studying these phenomena, as it enables the response of a system to be simulated solely from the description of atomic interactions, for which well-tested models often exist. However, one of the disadvantages of this technique is that it requires a large number of atoms to reach a scale that is sufficiently representative of the phenomena being studied, in order to be able to observe them and overcome size effects as far as possible. Over the past decade, numerous efforts have been made to adapt DM codes to the latest supercomputer architectures, enabling us to simulate systems with several billion atoms. These MD simulations have become true numerical experiments, generating huge quantities of data to be processed. Nowadays, the time required for post-processing and analysis of this data can now become more important than for simulation, and is becoming an essential issue. In this thesis, we have developed an analysis algorithm for MD simulation capable of processing multi-billion-atom workloads with a computational cost of the order of one percent of the total simulation time. The algorithm detects and characterizes areas of interest in the simulation based on user-defined criteria, making it highly versatile. We have based this algorithm on connected components labeling techniques, parallelized in shared-memory, which we have extended to hybrid shared and distributed memory parallelism. This extension to distributed-memory settings not only enables us to handle the quantity of data generated by the simulations, but has also enabled us to incorporate our analysis into the CEA MD code exaStamp for in-situ processing of the simulations. This in-situ processing of the simulation reduces the amount of data to be stored on disk and increases the frequency of analysis on a simulation compared to what could be done in post-processing. A characterization of the errors associated with the measurements made by our analysis has been carried out, making it directly exploitable by physicists. In particular, we have applied it to a number of physical cases: micro-jetting and splashing where aggregate detection is required, as well as shock damage simulations (spalling) involving void detection

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CHANG, YUAN CHUAN, and 張永專. "Connected Components Labeling Algorithm Applied to Image Segmentation." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/86231509563166292537.

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碩士
國立中山大學
電機工程研究所
83
A new image segmentation algorithm based on the textural information of image pixels is presented. Image segmentation is a fundamental technique for the application of computer vision. There are two difficulties for the technique of image segmentation.First, the choice of the characteristic with which the regions of an image segmentation are homogeneous enough for the decision.Second,the connection problem for pixels within the segmented components in an arbitrary shape. In this thesis, we propose a new property vector related to the concept of image texture and employ the technique of connected components labeling to solve the problems. The image structure are defined to be the linear relationship between pixels with their upper and left neighbors. Image can be very inhomogenious. However, the image structure may be uniform enough in some image components. By this image texture, individual image constraint equations can be set up for those triple pixel. These equations are the property vectors chosen by us for segmentation decision. Discrepancy between equations are measured by sequential least squared method. A recursive method for computing the error is developed in this thesis for simplifying computation. Connected components labeling method was originally developed for the binary images. By the introduction of our property vector, the labeling method are extended into the gray image segmentation. For computing efficiency, the Ronse and Devijver''s run-length version of labeling method is modified by us for our application. Our methods of computing segmentation error for property vectors and labeling segmentation components are both based upon a top-down and left- right scanning order. As a summary, our segmentation computing are very efficient due to the recursive computation structure and the scanning method.

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WU, BO-YEN, and 吳柏彥. "Connected Components Labeling Algorithm By Unidirectional Run-length Table Searching." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/52054847292075951596.

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碩士
輔仁大學
資訊工程學系碩士班
104
In order to improve the efficiency of connected components labeling, this paper presents a new connected components labeling method by unidirectional run-length table searching. Instead of searching the run-length table up and down, this method only needs to search the table in one direction. The number of run-length code searching in the labeling algorithm is reduced with our method, thus, our method increases the efficiency of connected components labeling. Also in our method the run-length code of each connected components are stored as a linked list. When extracting the blobs, it only need to read the linked list of each blobs. The result of experiments demonstrates that our method reduces the total searching times, compared with the previous algorithm.

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Lin, Keng-li, and 林耿立. "An Efficient Two-Phase Algorithm for Labeling Connected Components Based on a Two-Scan." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/21955013549406582369.

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碩士
國立臺灣科技大學
資訊工程系
97
Owing to the demand of more efficient technology about robot vision applications, the researches on intelligent pattern detection and recognition have been rapidly grown in recent years. In addition to that an adaptive pattern classifier may affect recognition results, one of the most important characteristics of an intelligent robot vision system is to employ a quick and efficient pattern extraction method. To achieve such a good, in this thesis, we present an efficient two-phase labeling connected components algorithm based on a two-scan structure. In the labeling step, unlike conventional labeling algorithms using the same labeling operations for each object pixel for labeling or relation checking, our algorithm executes different labeling operations for each pixel depending on its location in a row of contiguous object pixels. Thus, we can reduce many unnecessary labeling operations and lessen the execution time. As to the label relation table, we propose a novel table structure similar to a circle to resolve label equivalence between provisional label sets. It not only can record all information as same as conventional label relation tables, but also can reduce 1/3 memory space at most than the other so. The implementation of this structure only requires two 1-D arrays, and we can easily record the relation between provisional labels and their corresponding representative labels by use of our record procedure. Experimental results reveal that the performance of our algorithm is superior to those of all conventional labeling algorithms for both ordinary and noisy images under the common sequential execution hardware.

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Частини книг з теми "Connected components labeling (CCL)"

Bolelli, Federico, Stefano Allegretti, and Costantino Grana. "Connected Components Labeling on Bitonal Images." In Image Analysis and Processing – ICIAP 2022, 347–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06430-2_29.

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Grana, Costantino, Lorenzo Baraldi, and Federico Bolelli. "Optimized Connected Components Labeling with Pixel Prediction." In Advanced Concepts for Intelligent Vision Systems, 431–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48680-2_38.

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Ma, Dongdong, Shaojun Liu, and Qingmin Liao. "Run-Based Connected Components Labeling Using Double-Row Scan." In Lecture Notes in Computer Science, 264–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_24.

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Bolelli, Federico, Michele Cancilla, Lorenzo Baraldi, and Costantino Grana. "Connected Components Labeling on DRAGs: Implementation and Reproducibility Notes." In Reproducible Research in Pattern Recognition, 89–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23987-9_7.

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Asano, Tetsuo, and Sergey Bereg. "A New Framework for Connected Components Labeling of Binary Images." In Combinatorial Image Analaysis, 90–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34732-0_7.

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Bolelli, Federico, Michele Cancilla, and Costantino Grana. "Two More Strategies to Speed Up Connected Components Labeling Algorithms." In Image Analysis and Processing - ICIAP 2017, 48–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68548-9_5.

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Harrison, Cyrus, Jordan Weiler, Ryan Bleile, Kelly Gaither, and Hank Childs. "A Distributed-Memory Algorithm for Connected Components Labeling of Simulation Data." In Mathematics and Visualization, 3–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44900-4_1.

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Allegretti, Stefano, Federico Bolelli, Michele Cancilla, Federico Pollastri, Laura Canalini, and Costantino Grana. "How Does Connected Components Labeling with Decision Trees Perform on GPUs?" In Computer Analysis of Images and Patterns, 39–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29888-3_4.

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Rasmusson, A., T. S. Sørensen, and G. Ziegler. "Connected Components Labeling on the GPU with Generalization to Voronoi Diagrams and Signed Distance Fields." In Advances in Visual Computing, 206–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41914-0_21.

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Bolelli, Federico, Stefano Allegretti, and Costantino Grana. "A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes." In Reproducible Research in Pattern Recognition, 139–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76423-4_9.

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Тези доповідей конференцій з теми "Connected components labeling (CCL)"

Monif, Mamdouh, Kinan Mansour, Waad Ammar, and Maan Ammar. "Automatic Detection and Extraction of Lungs Cancer Nodules Using Connected Components Labeling and Distance Measure Based Classification." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110705.

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We introduce in this paper a method for reliable automatic extraction of lung area from CT chest images with a wide variety of lungs image shapes by using Connected Components Labeling (CCL) technique with some morphological operations. The paper introduces also a method using the CCL technique with distance measure based classification for the efficient detection of lungs nodules from extracted lung area. We further tested our complete detection and extraction approach using a performance consistency check by applying it to lungs CT images of healthy persons (contain no nodules). The experimental results have shown that the performance of the method in all stages is high.

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Khoshki, Rohollah Mazrae, and Subramaniam Ganesan. "Improved Automatic License Plate Recognition (ALPR) system based on single pass Connected Component Labeling (CCL) and reign property function." In 2015 IEEE International Conference on Electro/Information Technology (EIT). IEEE, 2015. http://dx.doi.org/10.1109/eit.2015.7293378.

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Bolelli, Federico, Lorenzo Baraldi, Michele Cancilla, and Costantino Grana. "Connected Components Labeling on DRAGs." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545505.

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Grana, Costantino, Daniele Borghesani, and Rita Cucchiara. "Fast block based connected components labeling." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413731.

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Nagaraj, Nithin, and Shekhar Dwivedi. "CxCxC: compressed connected components labeling algorithm." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.709210.

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Zuo, Yingnan, and Danyang Zhang. "Connected Components Labeling Algorithms: A Review." In 2023 9th International Conference on Computer and Communications (ICCC). IEEE, 2023. http://dx.doi.org/10.1109/iccc59590.2023.10507420.

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Grana, Costantino, Daniele Borghesani, Paolo Santinelli, and Rita Cucchiara. "High Performance Connected Components Labeling on FPGA." In 2010 21st International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2010. http://dx.doi.org/10.1109/dexa.2010.57.

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Grana, Costantino, Federico Bolelli, Lorenzo Baraldi, and Roberto Vezzani. "YACCLAB - Yet Another Connected Components Labeling Benchmark." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900112.

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Allegretti, Stefano, Federico Bolelli, Michele Cancilla, and Costantino Grana. "Optimizing GPU-Based Connected Components Labeling Algorithms." In 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2018. http://dx.doi.org/10.1109/ipas.2018.8708900.

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Santiago, Diego J. C., Tsang Ing Ren, George D. C. Cavalcanti, and Tsang Ing Jyh. "Fast block-based algorithms for connected components labeling." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638021.

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Звіти організацій з теми "Connected components labeling (CCL)"

Yang, Xue D. An Improved Algorithm for Labeling Connected Components in a Binary Image. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada210100.

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