Relevant bibliographies by topics / Real time skeletonization

Academic literature on the topic 'Real time skeletonization'

Author: Grafiati
Published: 1 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Journal articles on the topic "Real time skeletonization":

1

Ma, Jing, Jin Wang, Jituo Li, and Dongliang Zhang. "Real-time skeletonization for sketch-based modeling." Computers & Graphics 102 (February 2022): 56–66. http://dx.doi.org/10.1016/j.cag.2021.11.005.

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Majumdar, B., V. V. Ramakrishna, P. S. Dey, and A. K. Majumdar. "Design of an ASIC Chip for Skeletonization of Graylevel Digital Images." VLSI Design 4, no. 1 (January 1, 1996): 83–90. http://dx.doi.org/10.1155/1996/51972.

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This paper describes the design of an ASIC chip for thinning of graylevel images. The chip implements a Min-Max skeletonization algorithm and is based on a pipeline architecture where each stage of the pipeline performs masking operations on the graylevel images. The chip operates in real time at a frequency of 8 MHz and utilizes about 321 mils × 410 mils of silicon area.
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Villegas-Ch., William, Santiago Barahona-Espinosa, Walter Gaibor-Naranjo, and Aracely Mera-Navarrete. "Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly." Computation 10, no. 11 (November 2, 2022): 195. http://dx.doi.org/10.3390/computation10110195.

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Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.
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Chopard, Adrien, Jean-Paul Guillet, Pierre Gellie, Benoit RECUR, H. Balacey, and Patrick Mounaix. "Skeletonization and 3D Rendering with real time Terahertz tomography." Optics Continuum, March 9, 2023. http://dx.doi.org/10.1364/optcon.486227.

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Randlett, Owen. "pi_tailtrack: A compact, inexpensive, and open-source behaviour-tracking system for head-restrained zebrafish." Journal of Experimental Biology, October 11, 2023. http://dx.doi.org/10.1242/jeb.246335.

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Quantifying animal behavior during microscopy is crucial to associate optically recorded neural activity with behavioural outputs and states. Here I describe an imaging and tracking system for head-restrained larval zebrafish compatible with functional microscopy. This system is based on the Raspberry Pi computer, Pi NoIR camera, and open-source software for the real-time tail segmentation and skeletonization of the zebrafish tail at over 100hz. This allows for precise and long-term analyses of swimming behaviour, that can be related to functional signals recorded in individual neurons. This system offers a simple but performant solution for quantifying the behavior of head-restrained larval zebrafish, which can be built for 340€.
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Bin Wee, Xian, Manuel Herrera, Georgios M. Hadjidemetriou, and Ajith Kumar Parlikad. "Simulation and Criticality Assessment of Urban Rail and Interdependent Infrastructure Networks." Transportation Research Record: Journal of the Transportation Research Board, June 30, 2022, 036119812211035. http://dx.doi.org/10.1177/03611981221103594.

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The role of urban infrastructure is becoming increasingly interdependent, resulting in new sources of vulnerability. Infrastructural asset failure can propagate between rail transportation and other infrastructure networks. There remains a lack of academic research focusing on the dynamic simulation of city-wide infrastructure using real-life data to quantify and cross-compare the criticality of assets. This paper aims to bridge this gap by developing a modeling methodology for interdependent urban infrastructure using complex network theory, which serves as a basis for investigating asset criticality and failure propagation. This modeling framework comprises the distribution of resource supply and demand, the topological representation and skeletonization of the infrastructure network, as well as modeling the propagation of asset failures. The framework is thereafter applied to a case study of the exposure of Greater London’s rail transportation network to failures from electricity infrastructure, selected as a representative example of interdependent infrastructures within a large-scale urban metropolitan area. Two time-based criticality metrics are also proposed to measure the topological extent of infrastructural failures and economic impacts resulting from the failure propagation of given initial failure scenarios. The results of the case study demonstrate that these proposed criticality metrics are effective in capturing the dynamics of failure propagation, and that topological metrics in criticality assessment do not always reflect the resulting economic damages of infrastructural failures.
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Burney, S. M. Syed Aqil, Adnan Alam Khan, and Asim Iftikhar. "Survival Analysis of Tumor using 7 Tesla MRI." Pakistan Journal of Engineering, Technology & Science 5, no. 2 (October 18, 2016). http://dx.doi.org/10.22555/pjets.v5i2.917.

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<span>Magnetic resonance imaging (MRI7) is a very powerful imaging technique for the assessment of stroke <span>aetiology (Condition) and brain imaging. Another class of MRI is ultrahigh frequency based MRI using 7 <span>Tesla is now developed by seamen’s for better imaging in humans. This study examines these MRI. This<br /><span>article highlights an alternative approach, denoted “interval monitoring,” whose aims is related with more <span>timely detection of tumor cancer changes. The conceptual background and the computational realization <span>of the proposed method are outlined, and its application is illustrated by an empirical example from the <span>image-based photo science, cancer registry of America. Monitoring of cancer patient survival is the first <span>step of its cure so across the globe practice routinely employed by many cancer registries, which is an <span>essential component for its cure. However, changes in prognosis over time are disclosed with<br /><span>considerable delay, with traditional methods of monitoring cumulative survival. Our study took sequence <span>of MRI images, GMPLS function locate the cancer after filtering and skeletonization. This study saves <span>time and difference for calculation of cancer equation. This study uses statistical technique to get the <span>desired matrix, further its inverse provides us real time mathematical equation which is unique for each <span>patient. Further survivor analysis is employed to achieve the break or death of subject. The Aim of this <span>research is to provide unique mathematical model of a cancer patient, provides real time graph about<br /><span>cancer health and survivor function depicts the death of subject respectively.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>
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Saitta, S., F. Sturla, A. Caimi, A. Riva, MC Palumbo, E. Votta, A. Redaelli, and MM Marrocco-Trischitta. "A deep learning-based and fully automated pipeline for thoracic aorta geometric analysis and TEVAR planning from computed tomography." European Heart Journal - Cardiovascular Imaging 22, Supplement_1 (January 1, 2021). http://dx.doi.org/10.1093/ehjci/jeaa356.251.

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Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ministry of Publich Health - Ricerca Corrente Introduction Thoracic endovascular aortic repair (TEVAR) represents a well-established alternative to open repair in selected patients. Its preoperative feasibility assessment and planning requires a computational tomography (CT)-based analysis of the geometric aortic features to identify an adequate proximal and distal landing zone (LZ) for endograft deployment. Yet, controversies persist on the definition and methods of measurement of specific geometric features of the LZs, including angulation and tortuosity, which are associated with an increased risk of postoperative endograft failure. In this respect, the development of a preoperative image processing method that provides an automatic and highly reproducible 3D identification of critical geometric features and specific anatomical landmarks, thus reducing the time and uncertainties related to manual segmentation, remains a largely unmet clinical need. In this study, we developed and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, recognize the relevant anatomical landmarks and LZs, and quantifies the geometry of the aortic arch in each proximal LZ s (i.e. 0 to 3). Methods Ninety CT scans of healthy aortas were retrieved, being the study conceived as a proof of concept analysis. The thoracic aorta was manually segmented by five independent and expert operators. 72 scans with the corresponding ground truth segmentations were randomly selected and used to train the CNN, which was based on a 3D U-Net architecture. The other 18 scans were used to test the CNN-based segmentations. The fully automated pipeline was obtained by integrating the CNN, 3D geometry skeletonization, and processing of the aortic centerline and wall via computational geometry (Figure). The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation and tortuosity calculated according to previously published work. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA), and the different landing zones in each arch type. Results The CNN segmentation yielded a mean Dice score of 0.94 with respect to manual ground truth segmentations. Standard arches were characterized by significantly larger radius of curvature (p = 0.002) and lower tortuosity in zone 3 (p = 0.004) vs. CILCA arches. For both standard and CILCA arches, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p &lt; 0.0001), angulation (p &lt; 0.0001) and tortuosity (p &lt; 0.0001). Conclusions We developed a CNN-based automated pipeline for the automated, and reliable geometric quantification of standard and CILCA aortic arches. This tool has the potential to support TEVAR pre-procedural planning in a real clinical setting. Abstract Figure. Automatic pipeline scheme

Dissertations / Theses on the topic "Real time skeletonization":

1

Marie, Romain. "Exploration autonome et construction de cartes topologiques référencées vision omnidirectionnelle." Amiens, 2014. https://theses.hal.science/tel-04515697.

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Dans ce travail, nous abordons l'exploration autonome d'un robot mobile terrestre et la construction de carte topologique en environnement inconnu, à partir d'un capteur de vision catadioptrique. L'image acquise à chaque instant permet de générer différentes représentations locales, qui offrent au robot une interprétation de la scène dans laquelle il évolue. D'un côté, un algorithme de squelettisation original est appliqué à l'espace navigable extrait dans l'image, et permet de caractériser la topologie de l'espace libre proximal. De l'autre, une signature de lieu est construite à partir du complément de l'espace navigable, afin de ne considérer que l'information photométrique la plus riche. Cette seconde représentation permet au robot d'indexer l'environnement en un ensemble de lieux, et de se localiser tout au long de sa mission. Ces représentations sont ensuite structurées sous la forme d'une carte topologique, qui, en fournissant un modèle de l'environnement, offre des possibilités comportementales élaborées (permettant notamment de répondre aux problématiques de couverture de l'espace)
In this work, we address the problem of autonomous exploration and topological map building in totally unknown environments for a mobile robot equipped with a sole catadioptric sensor. Multiple local representations for spatial knowledge are built upon visual information only. First, we develop an adaptated skeletonization algorithm. Applied on the extracted free space in the image, it carries the topological properties of the observed scene, and describes safe trajectories in the environment. Second, we propose a visual signature using the complement of the free space in the image, so that only the most relevant photometric information is considered. Using this representation, the robot can map the environment into a collection of places, and use them to keep track of its localization. The built representations are then organized in a topological map of the environment, which allows the robot to handle high-level behaviours (leading for instance to a structured exploration and coverage of the environment)

Conference papers on the topic "Real time skeletonization":

1

Ki-Hoon Kim, Pham Dai Xuan, Pham Cong Thien, and Jae-Wook Jeon. "Real-time skeletonization using FPGA." In 2007 International Conference on Control, Automation and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iccas.2007.4406513.

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Srijeyanthan, K., A. Thusyanthan, C. N. Joseph, S. Kokulakumaran, C. Gunasekara, and C. Gamage. "Skeletonization in a real-time gesture recognition system." In 2010 5th International Conference on Information and Automation for Sustainability (ICIAfS). IEEE, 2010. http://dx.doi.org/10.1109/iciafs.2010.5715662.

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Li, Y., A. Kostrzewski, D. H. Kim, and George Eichmann. "Real time programmable optical morphological filter." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.thx1.

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Morphological filtering (MF), a form of nonlinear image processing, has recently been applied to optical image processing. Among the MF processing capabilities are the noise removal, median filtering, edge detection, and skeletonization. For an electronic MF implementation, a time-consuming 2-D image correlation is performed. Although an electronic special-purpose cellular processor can be used for MF processing, its processing and interconnect complexity is large. On the other hand, using free-space optics, both coherent and incoherent optical MF have been suggested. The existing optical MF approaches have drawbacks, such as their lack of programmability, employing a less-compact architecture, etc. In this talk, a new liquid crystal TV (LCTV) based compact, real time programmable optical MF is proposed and described. Using a LCTV addressed lenslet array that serves as a structuring element and a projection lens followed by an electronic threshold processor, this new compact optical device experimentally demonstrates its capability of performing real time image dilation, erosion, and noise clean-up operations.
4

"HAND GESTURE RECOGNITION THROUGH ON-LINE SKELETONIZATION - Application of Continuous Skeleton to Real-time Shape Analysis." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003315505550560.

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Lengauer, Stefan, Peter Houska, and Reinhold Preiner. "Efficient Point Cloud Skeletonization with Locally Adaptive L1-Medial Projection." In WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022. Západočeská univerzita, 2022. http://dx.doi.org/10.24132/csrn.3201.6.

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3D line skeletons are simplistic representations of a shape’s topology which are used for a wide variety of geometry-processing tasks, including shape recognition, retrieval, and reconstruction. Numerous methods have been proposed to generate a skeleton from a given 3D shape. While mesh-based methods can exploit existing knowledge about the shape’s topology and orientation, point-based techniques often resort to precomputed per- point normals to ensure robustness. In contrast, previously proposed techniques for unprocessed point clouds either exhibit inferior robustness or require expensive operations, which in turn increases computation time. In this paper, we present a new and highly efficient skeletonization approach for raw point cloud data, which produces overall competitive results compared to previous work, while exhibiting much lower computation times. Our algo- rithm performs robustly in the face of noisy and fragmented inputs, as they are usually obtained from real-world 3D scans. We achieve this by first transferring the input point cloud into a Gaussian mixture model (GMM), obtaining a more compact representation of the surface. Our method then iteratively projects a small subset of the points into local L1-medians, yielding a rough outline of the shape’s skeleton. Finally, we present a new branch detection technique to obtain a coherent line skeleton from those projected points. We demonstrate the capabilities of our proposed method by extracting the line skeletons of a diverse selection of input shapes and evaluating their visual appearance as well as the efficiency compared to alternative state-of-the-art methods.
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Duncan, Bradley D., Ting-Chung Poon, and Ronald J. Pieper. "Nonlinear scanning image processing." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mi6.

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Image processing often employs sophisticated digital algorithms or precisely aligned coherent optical techniques. Although these methods are typically efficient in their image processing capabilities, neither method readily lends itself to simple, real-time implementation. For binary image processing, the importance of optical morphological filtering has recently been recognized.1,2 Through the optical implementation of mathematical morphology, systems capable of performing operations, such as median filtering, skeletonization, edge extraction, and impulsive noise removal have been studied. Though often real-time in nature, these systems usually require complex optical arrangements or nonlinear electronic-feedback networks. We present an edge-extraction technique for binary images that implements morphological transformations through the use of a technically simple hybrid optical/electronic system. The optical portion of our system consists of an optical-scanning arrangement that requires only minimal optical alignment. The electronic portion of our system uses simple analog circuitry, which requires no feedback and eliminates the need for sophisticated digital/electronic processing. The same system is also used to extract edges from gray-scale objects by subtracting binary slices obtained through a threshold-decomposition operation.2 The processing speed of our system is essentially limited by how fast we can scan an object. We have obtained good results in extracting edges in real time from both binary and gray-scale objects.

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