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Journal articles on the topic 'Euclidean Distance Transform'

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Published: 4 June 2021
Last updated: 2 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

PAVEL, SANDY, and SELIM G. AKL. "EFFICIENT ALGORITHMS FOR THE EUCLIDEAN DISTANCE TRANSFORM." Parallel Processing Letters 05, no. 02 (June 1995): 205–12. http://dx.doi.org/10.1142/s0129626495000187.

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The Euclidean Distance Transform is an important computational tool for the processing of binary images, with applications in many areas such as computer vision, pattern recognition and robotics. We investigate the properties of this transform and describe an O(n2) time optimal sequential algorithm. A deterministic EREW-PRAM parallel algorithm which runs in O( log n) time using O(n2) processors and O(n2) space is also derived. Further, a cost optimal randomized parallel algorithm which runs within the same time bounds with high probability, is given.
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12

Chen, L. "Efficient Parallel Algorithms for Euclidean Distance Transform." Computer Journal 47, no. 6 (June 1, 2004): 694–700. http://dx.doi.org/10.1093/comjnl/47.6.694.

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13

Lee, Yu-Hua, Shi-Jinn Horng, Tzong-Wann Kao, Ferng-Shi Jaung, Yuung-Jih Chen, and Horng-Ren Tsai. "Parallel computation of exact Euclidean distance transform." Parallel Computing 22, no. 2 (February 1996): 311–25. http://dx.doi.org/10.1016/0167-8191(95)00066-6.

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14

Wright, Mark W., Roberto Cipolla, and Peter J. Giblin. "Skeletonization using an extended Euclidean distance transform." Image and Vision Computing 13, no. 5 (June 1995): 367–75. http://dx.doi.org/10.1016/0262-8856(95)99723-e.

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15

Ragnemalm, Ingemar. "The Euclidean distance transform in arbitrary dimensions." Pattern Recognition Letters 14, no. 11 (November 1993): 883–88. http://dx.doi.org/10.1016/0167-8655(93)90152-4.

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16

Huang, C. T., and O. R. Mitchell. "A Euclidean distance transform using grayscale morphology decomposition." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 4 (April 1994): 443–48. http://dx.doi.org/10.1109/34.277600.

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17

GAVRILOVA, MARINA L., and MUHAMMAD H. ALSUWAIYEL. "TWO ALGORITHMS FOR COMPUTING THE EUCLIDEAN DISTANCE TRANSFORM." International Journal of Image and Graphics 01, no. 04 (October 2001): 635–45. http://dx.doi.org/10.1142/s0219467801000359.

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Given an n × n binary image of white and black pixels, we present two optimal algorithms for computing the distance transform and the nearest feature transform using the Euclidean metric. The first algorithm is a fast sequential algorithm that runs in linear time in the input size. The second is a parallel algorithm that runs in O(n2/p) time on a linear array of p processors, p, 1 ≤ p ≤ n.
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18

Xu, Dong, and Yang Zhang. "Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform." PLoS ONE 4, no. 12 (December 2, 2009): e8140. http://dx.doi.org/10.1371/journal.pone.0008140.

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19

Chen, Ling, and Henry Y. H. Chuang. "Designing systolic architectures for complete Euclidean distance transform." Journal of VLSI signal processing systems for signal, image and video technology 10, no. 2 (July 1995): 169–79. http://dx.doi.org/10.1007/bf02407034.

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20

BOSSOMAIER, TERRY, NATALINA ISIDORO, and ADRIAN LOEFF. "DATA PARALLEL COMPUTATION OF EUCLIDEAN DISTANCE TRANSFORMS." Parallel Processing Letters 02, no. 04 (December 1992): 331–39. http://dx.doi.org/10.1142/s0129626492000477.

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The Euclidean Distance Transform is an important, but computationally expensive, technique of computational geometry, with applications in many areas including image processing, graphics and pattern recognition. Since the data sets used are typically large, one might hope that parallel computers would be suitable for its determination. We show that existing parallel algorithms perform poorly on certain data sets and introduce new strategies. These achieve high speed on diverse data sets, but fail occasionally in pathological cases. We determine the maximum error in such cases and demonstrate that it is satisfactorily low. Although adequate efficiency is achievable on SIMD machines, we demonstrate that problems of this kind are data parallel yet best suited to MIMD architectures.
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21

Alfakih, Abdo Y., and Henry Wolkowicz. "Two theorems on Euclidean distance matrices and Gale transform." Linear Algebra and its Applications 340, no. 1-3 (January 2002): 149–54. http://dx.doi.org/10.1016/s0024-3795(01)00403-7.

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22

Boxer, Laurence, and Russ Miller. "Corrigendum to “Efficient Computation of the Euclidean Distance Transform”." Computer Vision and Image Understanding 86, no. 2 (May 2002): 137–40. http://dx.doi.org/10.1006/cviu.2002.0968.

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23

Chen, Mingli, Weiguo Lu, Quan Chen, Kenneth Ruchala, and Gustavo Olivera. "Efficient gamma index calculation using fast Euclidean distance transform." Physics in Medicine and Biology 54, no. 7 (March 13, 2009): 2037–47. http://dx.doi.org/10.1088/0031-9155/54/7/012.

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24

Macedo, Márcio, and Antônio Apolinário. "Improved anti-aliasing for Euclidean distance transform shadow mapping." Computers & Graphics 71 (April 2018): 166–79. http://dx.doi.org/10.1016/j.cag.2017.11.006.

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25

Arcelli, Carlo, and Gabriella Sanniti di Baja. "Finding local maxima in a pseudo-euclidean distance transform." Computer Vision, Graphics, and Image Processing 43, no. 1 (July 1988): 113. http://dx.doi.org/10.1016/0734-189x(88)90055-2.

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26

Manduhu, Manduhu, and Mark W. Jones. "A Work Efficient Parallel Algorithm for Exact Euclidean Distance Transform." IEEE Transactions on Image Processing 28, no. 11 (November 2019): 5322–35. http://dx.doi.org/10.1109/tip.2019.2916741.

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27

Shih, Frank Y., and Christopher C. Pu. "A skeletonization algorithm by maxima tracking on Euclidean distance transform." Pattern Recognition 28, no. 3 (March 1995): 331–41. http://dx.doi.org/10.1016/0031-3203(94)00104-t.

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28

Wulanningrum, Resty, and Nandha Vera Wihra Lelitavistara. "DISCRETE COSINE TRANSFORM UNTUK IDENTIFIKASI CITRA HYLOCEREUS COSTARICENSIS." Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 6, no. 2 (November 1, 2015): 353. http://dx.doi.org/10.24176/simet.v6i2.472.

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ABSTRAK Pengolahan citra digital memiliki peranan sangat luas terhadap kehidupan sehari- hari. Salah satunya dapat digunakan untuk mengidentifikasi citra buah untuk mengetahui tingkat kematangan buah tersebut. Pada penelitian ini identifikasi citra tersebut diterapkan pada hylocereus costaricensis(buah naga merah). Seringkali ketika membeli buah tersebut setelah dimakan daging buah naga terasa sangat lunak dan hambar. Padahal pada buah naga tersebut mengandung rasa manis, asam serta segar.Pada penelitian ini menggunakan metode Discrete Cosine Transform (DCT) dengan objek buah naga merah di kebun buah naga Ngunut, Tulungagung. Citra buah naga dilakukan tahap pre- processing yaitu grayscale dan deteksi tepi, kemudian dilanjutkan pada tahap metode DCT dan pengenalan menggunakan euclidean distances.Penelitian dilakukan dengan pengambilan gambar dari masing- masing tingkat kematangan yang berbeda yaitu pada tingkat kematangan 25%, 40%, 60%, 75%, dan 90%. Dari masing- masing sampel dilakukan pengambilan gambar dengan background warna putih. Hasil dari penelitian ini adalah citra buah naga merah mampu diidentifikasi menggunakan DCT dan Euclidean Distance dengan prosentase akurasi sebesar 80%. Besarnya tingkat akurasi dipengaruhi oleh banyaknya jumlah data training yang digunakan. Kata kunci: DCT, deteksi tepi, euclidean distances, grayscale, hylocereus costaricensis.
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29

David, David, and Ferdinand Ariandy Luwinda. "Perbandingan DTCWT dan NMF pada Face Recognition menggunakan Euclidean Distance." ComTech: Computer, Mathematics and Engineering Applications 5, no. 1 (June 30, 2014): 46. http://dx.doi.org/10.21512/comtech.v5i1.2580.

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Dual tree complex wavelet transform (DTCWT) is widely used for representation of face image features. DTCWT is more frequently used than Gabor or Discrete Wavelet Transform (DWT) because it provides good directional selectivity in six different directions. Meanwhile, non-negative matrix factorization (NMF) is also frequently used since it can reduce high dimensional feature into smaller one without losing important features. This research focused on comparison between DTCWT and NMF as feature extraction and Euclidean Distance for classification. This research used ORL Faces database. Experimental result showed that NMF provided better results than DTCWT did. NMF reached 92% of accuracy and DTCWT reached 78% of accuracy.
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30

Wang, Jun, and Ying Tan. "Efficient Euclidean distance transform algorithm of binary images in arbitrary dimensions." Pattern Recognition 46, no. 1 (January 2013): 230–42. http://dx.doi.org/10.1016/j.patcog.2012.07.030.

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31

Datta, Amitava, and Subbiah Soundaralakshmi. "Constant-Time Algorithm for the Euclidean Distance Transform on Reconfigurable Meshes." Journal of Parallel and Distributed Computing 61, no. 10 (October 2001): 1439–55. http://dx.doi.org/10.1006/jpdc.2000.1684.

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32

Pan, Y., M. Hamdi, and K. Li. "Euclidean distance transform for binary images on reconfigurable mesh-connected computers." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 30, no. 1 (2000): 240–44. http://dx.doi.org/10.1109/3477.826967.

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33

Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering Research and Science 3, no. 7 (July 31, 2018): 67. http://dx.doi.org/10.24018/ejers.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
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34

Lucet, Yves. "New sequential exact Euclidean distance transform algorithms based on convex analysis." Image and Vision Computing 27, no. 1-2 (January 2009): 37–44. http://dx.doi.org/10.1016/j.imavis.2006.10.011.

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35

Baum, Daniel, James C. Weaver, Igor Zlotnikov, David Knötel, Lara Tomholt, and Mason N. Dean. "High-Throughput Segmentation of Tiled Biological Structures using Random-Walk Distance Transforms." Integrative and Comparative Biology 59, no. 6 (July 8, 2019): 1700–1712. http://dx.doi.org/10.1093/icb/icz117.

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Abstract Various 3D imaging techniques are routinely used to examine biological materials, the results of which are usually a stack of grayscale images. In order to quantify structural aspects of the biological materials, however, they must first be extracted from the dataset in a process called segmentation. If the individual structures to be extracted are in contact or very close to each other, distance-based segmentation methods utilizing the Euclidean distance transform are commonly employed. Major disadvantages of the Euclidean distance transform, however, are its susceptibility to noise (very common in biological data), which often leads to incorrect segmentations (i.e., poor separation of objects of interest), and its limitation of being only effective for roundish objects. In the present work, we propose an alternative distance transform method, the random-walk distance transform, and demonstrate its effectiveness in high-throughput segmentation of three microCT datasets of biological tilings (i.e., structures composed of a large number of similar repeating units). In contrast to the Euclidean distance transform, the random-walk approach represents the global, rather than the local, geometric character of the objects to be segmented and, thus, is less susceptible to noise. In addition, it is directly applicable to structures with anisotropic shape characteristics. Using three case studies—tessellated cartilage from a stingray, the dermal endoskeleton of a starfish, and the prismatic layer of a bivalve mollusc shell—we provide a typical workflow for the segmentation of tiled structures, describe core image processing concepts that are underused in biological research, and show that for each study system, large amounts of biologically-relevant data can be rapidly segmented, visualized, and analyzed.
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36

TORELLI, JULIO CESAR, RICARDO FABBRI, GONZALO TRAVIESO, and ODEMIR MARTINEZ BRUNO. "A HIGH PERFORMANCE 3D EXACT EUCLIDEAN DISTANCE TRANSFORM ALGORITHM FOR DISTRIBUTED COMPUTING." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 06 (September 2010): 897–915. http://dx.doi.org/10.1142/s0218001410008202.

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The Euclidean distance transform (EDT) is used in various methods in pattern recognition, computer vision, image analysis, physics, applied mathematics and robotics. Until now, several sequential EDT algorithms have been described in the literature, however they are time- and memory-consuming for images with large resolutions. Therefore, parallel implementations of the EDT are required specially for 3D images. This paper presents a parallel implementation based on domain decomposition of a well-known 3D Euclidean distance transform algorithm, and analyzes its performance on a cluster of workstations. The use of a data compression tool to reduce communication time is investigated and discussed. Among the obtained performance results, this work shows that data compression is an essential tool for clusters with low-bandwidth networks.
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37

JANG, SEOK-WOO, and SANG-HONG LEE. "NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA." Journal of Mechanics in Medicine and Biology 20, no. 09 (September 16, 2020): 2040017. http://dx.doi.org/10.1142/s0219519420400175.

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This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.
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38

Rajakumar. "A FRAMEWORK FOR MRI IMAGE RETRIEVAL USING CURVELET TRANSFORM AND EUCLIDEAN DISTANCE." Journal of Computer Science 9, no. 3 (March 1, 2013): 285–90. http://dx.doi.org/10.3844/jcssp.2013.285.290.

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39

Elizondo-Leal, Juan Carlos, Ezra Federico Parra-González, and José Gabriel Ramírez-Torres. "The Exact Euclidean Distance Transform: A New Algorithm for Universal Path Planning." International Journal of Advanced Robotic Systems 10, no. 6 (January 2013): 266. http://dx.doi.org/10.5772/56581.

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40

Bruno, Odemir Martinez, and Luciano da Fontoura Costa. "A parallel implementation of exact Euclidean distance transform based on exact dilations." Microprocessors and Microsystems 28, no. 3 (April 2004): 107–13. http://dx.doi.org/10.1016/j.micpro.2004.01.001.

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41

Chen, Ling, and Henry Y. H. Chuang. "An efficient algorithm for complete Euclidean distance transform on mesh-connected SIMD." Parallel Computing 21, no. 5 (May 1995): 841–52. http://dx.doi.org/10.1016/0167-8191(94)00103-h.

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42

da Fontoura Costa, Luciano. "Robust Skeletonization through Exact Euclidean Distance Transform and its Application to Neuromorphometry." Real-Time Imaging 6, no. 6 (December 2000): 415–31. http://dx.doi.org/10.1006/rtim.1999.0177.

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43

Kipli, Kuryati, Mohammed Enamul Hoque, Lik Thai Lim, Tengku Mohd Afendi Zulcaffle, Siti Kudnie Sahari, and Muhammad Hamdi Mahmood. "Retinal image blood vessel extraction and quantification with Euclidean distance transform approach." IET Image Processing 14, no. 15 (December 2020): 3718–24. http://dx.doi.org/10.1049/iet-ipr.2020.0336.

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44

Wang, Liang Ku, Cheng Jin Li, Qing Wang, Zhao Hui Yang, and Zhi Jie Wang. "Image Fusion Base on K-Means Clustering and Contourlet Transform." Key Engineering Materials 500 (January 2012): 540–44. http://dx.doi.org/10.4028/www.scientific.net/kem.500.540.

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The robustness of K-means clustering is poor in non-spherical distribution data, in order to improve the universal ability of clustering algorithms, the cross-entropy distance measure was used to replace the Euclidean distance measure . Contour let transform, not only has characteristics of multi-resolution, locality and critical sampling which wavelet has, but also has the characteristics of multiple decomposition directions and anisotropy which wavelets lack. So we combine the modified K-means clustering and Contour let transform to apply for image fusion. Experimental results show that this method is feasible.
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45

Yu-Hua Lee, Shi-Jinn Horng, and J. Seitzer. "Parallel computation of the euclidean distance transform on a three-dimensional image array." IEEE Transactions on Parallel and Distributed Systems 14, no. 3 (March 2003): 203–12. http://dx.doi.org/10.1109/tpds.2003.1189579.

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46

Ling Chen, Y. Pan, and Xiao-hua Xu. "Scalable and efficient parallel algorithms for Euclidean distance transform on the LARPBS model." IEEE Transactions on Parallel and Distributed Systems 15, no. 11 (November 2004): 975–82. http://dx.doi.org/10.1109/tpds.2004.71.

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47

LIANG, XUEFENG, ARIJIT BISHNU, and TETSUO ASANO. "A COMBINATORIAL APPROACH TO FINGERPRINT BINARIZATION AND MINUTIAE EXTRACTION USING EUCLIDEAN DISTANCE TRANSFORM." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 07 (November 2007): 1141–58. http://dx.doi.org/10.1142/s0218001407005910.

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Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.
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48

Sudha, N., and A. R. Mohan. "Design of a hardware accelerator for path planning on the Euclidean distance transform." Journal of Systems Architecture 54, no. 1-2 (January 2008): 253–64. http://dx.doi.org/10.1016/j.sysarc.2007.06.003.

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49

Weiguang Guan and Songde Ma. "A list-processing approach to compute Voronoi diagrams and the Euclidean distance transform." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 7 (July 1998): 757–61. http://dx.doi.org/10.1109/34.689306.

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50

Angayarkanni, N., and D. Kumar. "Euclidean Distance Transform (EDT) Algorithm Applied to Binary Image for Finding Breast Cancer." Biomedical and Pharmacology Journal 8, no. 1 (June 30, 2015): 407–11. http://dx.doi.org/10.13005/bpj/628.

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