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Relevant bibliographies by topics / Euclidean Distance Transform

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Academic literature on the topic 'Euclidean Distance Transform'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Euclidean Distance Transform"

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Gustavson, Stefan, and Robin Strand. "Anti-aliased Euclidean distance transform." Pattern Recognition Letters 32, no. 2 (January 2011): 252–57. http://dx.doi.org/10.1016/j.patrec.2010.08.010.

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Fabbri, Ricardo, Luciano Da F. Costa, Julio C. Torelli, and Odemir M. Bruno. "2D Euclidean distance transform algorithms." ACM Computing Surveys 40, no. 1 (February 2008): 1–44. http://dx.doi.org/10.1145/1322432.1322434.

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Zhang, S., and M. A. Karim. "Euclidean distance transform by stack filters." IEEE Signal Processing Letters 6, no. 10 (October 1999): 253–56. http://dx.doi.org/10.1109/97.789602.

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Breu, H., J. Gil, D. Kirkpatrick, and M. Werman. "Linear time Euclidean distance transform algorithms." IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 5 (May 1995): 529–33. http://dx.doi.org/10.1109/34.391389.

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Ho, Viet-Ha, Duc-Hoang Vo, Van-Sy Ngo, and Huu-Hung Huynh. "Person Identification Based on Euclidean Distance Transform." Journal of Engineering and Applied Sciences 14, no. 13 (December 10, 2019): 4312–16. http://dx.doi.org/10.36478/jeasci.2019.4312.4316.

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Elizondo-Leal, Juan Carlos, José Gabriel Ramirez-Torres, Jose Hugo Barrón-Zambrano, Alan Diaz-Manríquez, Marco Aurelio Nuño-Maganda, and Vicente Paul Saldivar-Alonso. "Parallel Raster Scan for Euclidean Distance Transform." Symmetry 12, no. 11 (October 31, 2020): 1808. http://dx.doi.org/10.3390/sym12111808.

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Distance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of m×m pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.

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Miyazawa, M., Peifeng Zeng, N. Iso, and T. Hirata. "A systolic algorithm for Euclidean distance transform." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 7 (July 2006): 1127–34. http://dx.doi.org/10.1109/tpami.2006.133.

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Boxer, Laurence, and Russ Miller. "Efficient Computation of the Euclidean Distance Transform." Computer Vision and Image Understanding 80, no. 3 (December 2000): 379–83. http://dx.doi.org/10.1006/cviu.2000.0880.

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Kwon, Oh-Kyu, and Jung W. Suh. "Improved 3 × 3 sequential Euclidean distance transform." IEEJ Transactions on Electrical and Electronic Engineering 8, no. 3 (April 4, 2013): 305–7. http://dx.doi.org/10.1002/tee.21858.

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Kozinska, Dorota, Oleh J. Tretiak, Jonathan Nissanov, and Cengizhan Ozturk. "Multidimensional Alignment Using the Euclidean Distance Transform." Graphical Models and Image Processing 59, no. 6 (November 1997): 373–87. http://dx.doi.org/10.1006/gmip.1997.0447.

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Dissertations / Theses on the topic "Euclidean Distance Transform"

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Wright, Mark William. "The extended Euclidean distance transform." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388344.

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Hirata, Tomio. "VLSI Algorithm for Euclidean Distance Transform." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10354.

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Torelli, Julio Cesar. ""Implementação paralela da transformada de distância euclidiana exata"." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21102005-132225/.

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Transformada de distância euclidiana (TDE) é a operação que converte uma imagem binária composta de pontos de objeto e de fundo em outra, chamada mapa de distâncias euclidianas, onde o valor armazenado em cada ponto corresponde à menor distância euclidiana entre este ponto e o fundo da imagem. A TDE é muito utilizada em visão computacional, análise de imagens e robótica, mas é uma transformação muito demorada, principalmente em imagens 3-D. Neste trabalho são utilizados dois tipos de computadores paralelos, (i) multiprocessadores simétricos (SMPs) e (ii) agregados de computadores, para reduzir o tempo de execução da TDE. Dois algoritmos de TDE são paralelizados. O primeiro, um algoritmo de TDE por varredura independente, é paralelizado em um SMP e em um agregado. O segundo, um algoritmo de TDE por propagação ordenada, é paralelizado no agregado.
The Euclidean distance transform is the operation that converts a binary image made of object and background pixels into another image, the Euclidean distance map, where each pixel has a value corresponding to the Euclidean distance from this pixel to the background. The Euclidean distance transform has important uses in computer vision, image analysis and robotics, but it is time-consuming, mainly when processing 3-D images. In this work two types of parallel computers are used to speed up the Euclidean distance transform, (i) symmetric multiprocessors (SMPs) and (ii) clusters of workstations. Two algorithms are parallelized. The first one, an independent line-column Euclidean distance transform algorithm, is parallelized on a SMP, and on a cluster. The second one, an ordered propagation Euclidean distance transform algorithm, is paralellized on a cluster.

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Payal, Yalçin. "Identification of Push-to-Talk Transmitters Using Wavelets." Thesis, Monterey, California. Naval Postgraduate School, 1995. http://hdl.handle.net/10945/30740.

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The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.
The main objective of this study is to find a wavelet-based, feature extracting algorithm for push-to-talk transmitter identification. A distance-measure algorithm is introduced to classify signals belonging to one of four transmitters. The signals are first preprocessed to put them into a form suitable for wavelet analysis. The preprocessing scheme includes taking the envelopes and differentials. Median filtering is also applied to the outputs of the wavelet transform. The distance algorithm uses local extrema of the wavelet coefficients, and computes the distance between the local extrema of a template and the processed signals. A small distance implies high similarity . A signal from each transmitter is selected as a template. A small distance measure indicates that the signal belongs to the transmitter from which the template originated. The distance algorithm can classify correctly the four different signal sets provided for the research. Even at lower signal-to-noise levels, good identification is achieved.

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Hjelm, Andersson Patrick. "Binär matchning av bilder med hjälp av vektorer från deneuklidiska avståndstransformen." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2440.

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This thesis shows the result from investigations of methods that use distance vectors when matching pictures. The distance vectors are available in a distance map made by the Euclidean Distance Transform. The investigated methods use the two characteristic features of the distance vector when matching pictures, length and direction. The length of the vector is used to calculate a value of how good a match is and the direction of the vector is used to predict a transformation to get a better match. The results shows that the number of calculation steps that are used during a search can be reduced compared to matching methods that only uses the distance during the matching.

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Marroni, Lilian Saldanha. "Aplicação da Transformada de Hough para localização dos olhos em faces humanas." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-05062017-160633/.

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Com a crescente necessidade de segurança, o processo de identificação pessoal é cada vez mais exigido. A extração de características faciais é um passo importante quando se lida com interpretação visual automatizada no reconhecimento de faces humanas. Dentre as características faciais, os olhos são partes importantes no processo de reconhecimento, pois determinam o início da busca por outras características relevantes. Neste trabalho é apresentado um método de localização de olhos em imagens frontais de faces humanas. Este método é subdividido em duas partes. Primeiro, são identificados os possíveis candidatos a olhos usando a Transformada de Hough para círculos; depois é aplicada a Distância Euclidiana confirmando-se a localização pro biometria facial.
Personal identification process is an exigency for security systems. Facial feature extraction is a crucial step for automated visual interpretation in human face recognition. Withim all the facial features, the eyes are significantly parts for the recognition process, therefore they set up the start for another relevant feature search. In this work, we present a method for eyes locating in digital images of frontal human faces. This method is subdivided into two parts. First, we identify the possible eyes\'s candidates by Hough Transfor for circules, them we apply the Euclidian distance and calculate the eyes\'s position by facial biometric measurement.

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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.

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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.

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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.

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Lee, Yu-Hua, and 李鈺華. "Fast Euclidean Distance Transform." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/37761644918216444981.

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碩士
國立臺灣科技大學
電機工程研究所
82
Distance transform is extensively used in image processing, such as expanding, shrinking, thinning, computing shape factor, etc. There are many approximate Euclidean distance transform algorithms in the literature, but finding the exact Euclidean distance transform is rather time consuming. So, it is important to increase the computing speed. The parallel algorithm is given for the computation of exact Euclidean distance transform for all pixels with respect to black pixels in an N * N black and white image. The running time is O(log ** 2 N) both in the EREW PARM model and the hypercube computer with N ** 2 processors. An O(log N) time algorithm is proposed for both mesh of trees and hypercube. The number of processors used to solve this problem for the former is N * N * (N / log N) and that for the latter is N ** (2.5), respectively. And this algorithm can also be implemented on an N * N * N ** (0.5) mesh of trees in O( N ** (0.5)) time.

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Book chapters on the topic "Euclidean Distance Transform"

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Bailey, Donald G. "An Efficient Euclidean Distance Transform." In Lecture Notes in Computer Science, 394–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30503-3_28.

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Ye, Qin-Zhong. "Signed Euclidean Distance Transform Applied to Shape Analysis." In Issues on Machine Vision, 249–62. Vienna: Springer Vienna, 1989. http://dx.doi.org/10.1007/978-3-7091-2830-5_16.

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Canalini, Luca, Jan Klein, Dorothea Miller, and Ron Kikinis. "Registration of Ultrasound Volumes Based on Euclidean Distance Transform." In Lecture Notes in Computer Science, 127–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33642-4_14.

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Linnér, Elisabeth, and Robin Strand. "Anti-Aliased Euclidean Distance Transform on 3D Sampling Lattices." In Advanced Information Systems Engineering, 88–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-09955-2_8.

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Xu, Dong, and Hua Li. "Euclidean Distance Transform of Digital Images in Arbitrary Dimensions." In Advances in Multimedia Information Processing - PCM 2006, 72–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922162_9.

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Asano, Tetsuo, and Hiroshi Tanaka. "In-Place Linear-Time Algorithms for Euclidean Distance Transform." In Transactions on Computational Science VIII, 103–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16236-7_7.

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Xu, Dong, Hua Li, and Yang Zhang. "Fast and Accurate Calculation of Protein Depth by Euclidean Distance Transform." In Lecture Notes in Computer Science, 304–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37195-0_30.

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Strand, Robin. "The Euclidean Distance Transform Applied to the FCC and BCC Grids." In Pattern Recognition and Image Analysis, 243–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_30.

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Remy, Eric, and Edouard Thiel. "Look-Up Tables for Medial Axis on Squared Euclidean Distance Transform." In Discrete Geometry for Computer Imagery, 224–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39966-7_21.

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Maurer, Calvin R., Vijay Raghavan, and Rensheng Qi. "A Linear Time Algorithm for Computing the Euclidean Distance Transform in Arbitrary Dimensions." In Lecture Notes in Computer Science, 358–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45729-1_35.

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Conference papers on the topic "Euclidean Distance Transform"

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Macedo, Marcio Cerqueira De Farias, and Antonio Lopes Apolinario. "Euclidean Distance Transform Soft Shadow Mapping." In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2017. http://dx.doi.org/10.1109/sibgrapi.2017.38.

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Chen, Shuang, Junli Li, and Xiuying Wang. "A Fast Exact Euclidean Distance Transform Algorithm." In Graphics (ICIG). IEEE, 2011. http://dx.doi.org/10.1109/icig.2011.34.

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Chen, Ling, and Henry Y. Chuang. "Systolic array for complete Euclidean distance transform." In Optical Tools for Manufacturing and Advanced Automation, edited by Bruce G. Batchelor, Susan Snell Solomon, and Frederick M. Waltz. SPIE, 1993. http://dx.doi.org/10.1117/12.150277.

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Wang, Jun, and Ying Tan. "Efficient Euclidean distance transform using perpendicular bisector segmentation." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995644.

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Segalla, Luis Fernando, Alexandre Zabot, Diogo Nardelli Siebert, and Fabiano Wolf. "Level Set Method Optimized with the Euclidean Distance Transform." In 25th International Congress of Mechanical Engineering. ABCM, 2019. http://dx.doi.org/10.26678/abcm.cobem2019.cob2019-0308.

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Elizondo-Leal, Juan C., and Gabriel Ramirez-Torres. "An Exact Euclidean Distance Transform for Universal Path Planning." In 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA). IEEE, 2010. http://dx.doi.org/10.1109/cerma.2010.93.

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de Assis Zampirolli, Francisco, and Leonardo Filipe. "A Fast CUDA-Based Implementation for the Euclidean Distance Transform." In 2017 International Conference on High-Performance Computing & Simulation (HPCS). IEEE, 2017. http://dx.doi.org/10.1109/hpcs.2017.123.

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Linner, Elisabeth, and Robin Strand. "A Graph-Based Implementation of the Anti-aliased Euclidean Distance Transform." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.186.

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Bhujbal, Pradnya N., and Sandipann P. Narote. "Lane departure warning system based on Hough transform and Euclidean distance." In 2015 Third International Conference on Image Information Processing (ICIIP). IEEE, 2015. http://dx.doi.org/10.1109/iciip.2015.7414798.

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Boudjella, Aissa, Brahim Belhaouari Samir, H. Bt Daud, and Raja Syahira. "License plate recognition part II: Wavelet transform and Euclidean distance method." In 2012 4th International Conference on Intelligent & Advanced Systems (ICIAS). IEEE, 2012. http://dx.doi.org/10.1109/icias.2012.6306103.

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