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Journal articles on the topic 'Deformable models'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Terzopoulos, Demetri, and Kurt Fleischer. "Deformable models." Visual Computer 4, no. 6 (November 1988): 306–31. http://dx.doi.org/10.1007/bf01908877.

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2

Terzopoulos, Demetri, John Platt, Alan Barr, and Kurt Fleischer. "Elastically deformable models." ACM SIGGRAPH Computer Graphics 21, no. 4 (August 1987): 205–14. http://dx.doi.org/10.1145/37402.37427.

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3

DeCarlo, D., and D. Metaxas. "Blended deformable models." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 4 (April 1996): 443–48. http://dx.doi.org/10.1109/34.491626.

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4

Metaxas, D. N., and I. A. Kakadiaris. "Elastically adaptive deformable models." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 10 (October 2002): 1310–21. http://dx.doi.org/10.1109/tpami.2002.1039203.

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5

Güdükbay, Uğur, and Bülent Özgüç. "Animation of deformable models." Computer-Aided Design 26, no. 12 (December 1994): 868–75. http://dx.doi.org/10.1016/0010-4485(94)90051-5.

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6

Jain, Anil K., Yu Zhong, and Marie-Pierre Dubuisson-Jolly. "Deformable template models: A review." Signal Processing 71, no. 2 (December 1998): 109–29. http://dx.doi.org/10.1016/s0165-1684(98)00139-x.

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7

Terzopoulos, Demetri, John Platt, and Kurt Fleischer. "Heating and melting deformable models." Journal of Visualization and Computer Animation 2, no. 2 (April 1991): 68–73. http://dx.doi.org/10.1002/vis.4340020208.

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8

PUJOL, ORIOL, and PETIA RADEVA. "TEXTURE SEGMENTATION BY STATISTICAL DEFORMABLE MODELS." International Journal of Image and Graphics 04, no. 03 (July 2004): 433–52. http://dx.doi.org/10.1142/s021946780400149x.

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Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.

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9

Rougon, Nicolas. "Directional adaptive deformable models for segmentation." Journal of Electronic Imaging 7, no. 1 (January 1, 1998): 231. http://dx.doi.org/10.1117/1.482641.

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10

Hirabayashi, Manato, Shinpei Kato, Masato Edahiro, Kazuya Takeda, and Seiichi Mita. "Accelerated Deformable Part Models on GPUs." IEEE Transactions on Parallel and Distributed Systems 27, no. 6 (June 1, 2016): 1589–602. http://dx.doi.org/10.1109/tpds.2015.2453962.

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11

Battle, X. L., G. S. Cunningham, and K. M. Hanson. "Tomographic reconstruction using 3D deformable models." Physics in Medicine and Biology 43, no. 4 (April 1, 1998): 983–90. http://dx.doi.org/10.1088/0031-9155/43/4/025.

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12

Xiaolei Huang and D. N. Metaxas. "Metamorphs: Deformable Shape and Appearance Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 8 (August 2008): 1444–59. http://dx.doi.org/10.1109/tpami.2007.70795.

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13

Moutsopoulos, Konstantinos, and Duncan Gillies. "Deformable models for laparoscopic surgery simulation." Computer Networks and ISDN Systems 29, no. 14 (October 1997): 1675–83. http://dx.doi.org/10.1016/s0169-7552(97)00088-3.

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14

Bouchara, Frédéric, and Sofiane Ramdani. "Subpixel edge refinement using deformable models." Journal of the Optical Society of America A 26, no. 4 (March 18, 2009): 820. http://dx.doi.org/10.1364/josaa.26.000820.

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15

Staib, L. H., and J. S. Duncan. "Boundary finding with parametrically deformable models." IEEE Transactions on Pattern Analysis and Machine Intelligence 14, no. 11 (1992): 1061–75. http://dx.doi.org/10.1109/34.166621.

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16

Pentland, Alex P. "Automatic extraction of deformable part models." International Journal of Computer Vision 4, no. 2 (March 1990): 107–26. http://dx.doi.org/10.1007/bf00127812.

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17

Der, Kevin G., Robert W. Sumner, and Jovan Popović. "Inverse kinematics for reduced deformable models." ACM Transactions on Graphics 25, no. 3 (July 2006): 1174–79. http://dx.doi.org/10.1145/1141911.1142011.

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18

Xu, Weiwei, Jun Wang, KangKang Yin, Kun Zhou, Michiel van de Panne, Falai Chen, and Baining Guo. "Joint-aware manipulation of deformable models." ACM Transactions on Graphics 28, no. 3 (July 27, 2009): 1–9. http://dx.doi.org/10.1145/1531326.1531341.

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19

Amit, Y., and K. M. Manbeck. "Deformable template models for emission tomography." IEEE Transactions on Medical Imaging 12, no. 2 (June 1993): 260–68. http://dx.doi.org/10.1109/42.232254.

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20

ABD-ALMAGEED, WAEL, and CHRISTOPHER E. SMITH. "ACTIVE DEFORMABLE MODELS USING DENSITY ESTIMATION." International Journal of Image and Graphics 04, no. 03 (July 2004): 343–61. http://dx.doi.org/10.1142/s0219467804001452.

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This paper introduces a new approach to active deformable models. The paper starts with a brief review of the relevant approaches. We then use the Expectation Maximization and Parzen Window techniques to develop a more robust formulation for pressure snakes. The generic nature of the proposed model allows it to perform well on complex colored images. We show, by several examples, the enhanced performance of the snake under the new formulation, compared to the performance of other known snake formulations.

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21

Cover, S. A., N. F. Ezquerra, J. F. O'Brien, R. Rowe, T. Gadacz, and E. Palm. "Interactively deformable models for surgery simulation." IEEE Computer Graphics and Applications 13, no. 6 (November 1993): 68–75. http://dx.doi.org/10.1109/38.252559.

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22

Tournier, Maxime, Matthieu Nesme, François Faure, and Benjamin Gilles. "Velocity-based adaptivity of deformable models." Computers & Graphics 45 (December 2014): 75–85. http://dx.doi.org/10.1016/j.cag.2014.08.004.

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23

Ferryman, J. M., A. D. Worrall, G. D. Sullivan, and K. D. Baker. "Visual surveillance using deformable models of vehicles." Robotics and Autonomous Systems 19, no. 3-4 (March 1997): 315–35. http://dx.doi.org/10.1016/s0921-8890(97)83348-9.

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24

Cheung, Kwok-Wai, Dit-Yan Yeung, and Roland T. Chin. "On deformable models for visual pattern recognition." Pattern Recognition 35, no. 7 (July 2002): 1507–26. http://dx.doi.org/10.1016/s0031-3203(01)00135-2.

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25

Chang, W., and M. Zwicker. "Range Scan Registration Using Reduced Deformable Models." Computer Graphics Forum 28, no. 2 (April 2009): 447–56. http://dx.doi.org/10.1111/j.1467-8659.2009.01384.x.

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26

Niessen, W. J., B. M. T. H. Romeny, and M. A. Viergever. "Geodesic deformable models for medical image analysis." IEEE Transactions on Medical Imaging 17, no. 4 (1998): 634–41. http://dx.doi.org/10.1109/42.730407.

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27

Sclaroff, Stan, and John Isidoro. "Active blobs: region-based, deformable appearance models." Computer Vision and Image Understanding 89, no. 2-3 (February 2003): 197–225. http://dx.doi.org/10.1016/s1077-3142(03)00003-1.

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28

Faure, François, Benjamin Gilles, Guillaume Bousquet, and Dinesh K. Pai. "Sparse meshless models of complex deformable solids." ACM Transactions on Graphics 30, no. 4 (July 2011): 1–10. http://dx.doi.org/10.1145/2010324.1964968.

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29

Zhang, Jinao, Yongmin Zhong, and Chengfan Gu. "Deformable Models for Surgical Simulation: A Survey." IEEE Reviews in Biomedical Engineering 11 (2018): 143–64. http://dx.doi.org/10.1109/rbme.2017.2773521.

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30

Jiaolong Xu, Sebastian Ramos, David Vazquez, and Antonio M. Lopez. "Domain Adaptation of Deformable Part-Based Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 12 (December 1, 2014): 2367–80. http://dx.doi.org/10.1109/tpami.2014.2327973.

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31

Pepik, Bojan, Michael Stark, Peter Gehler, and Bernt Schiele. "Multi-View and 3D Deformable Part Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 11 (November 1, 2015): 2232–45. http://dx.doi.org/10.1109/tpami.2015.2408347.

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32

Goldenstein, S. K., C. Vogler, and D. Metaxas. "Statistical cue integration in DAG deformable models." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 7 (July 2003): 801–13. http://dx.doi.org/10.1109/tpami.2003.1206510.

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33

Haiyan Wang and Bijoy Ghosh. "Geometric active deformable models in shape modeling." IEEE Transactions on Image Processing 9, no. 2 (2000): 302–8. http://dx.doi.org/10.1109/83.821748.

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34

Felzenszwalb, Pedro, Ross Girshick, David McAllester, and Deva Ramanan. "Visual object detection with deformable part models." Communications of the ACM 56, no. 9 (September 2013): 97–105. http://dx.doi.org/10.1145/2494532.

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35

Felzenszwalb, Pedro, Ross Girshick, David McAllester, and Deva Ramanan. "Visual object detection with deformable part models." Communications of the ACM 56, no. 9 (September 1, 2013): 97. http://dx.doi.org/10.1145/2500468.2494532.

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36

Pereira, Danillo Roberto, and Jorge Stolfi. "Fitting 3D Deformable Models to Microscope Images." Learning and Nonlinear Models 9, no. 1 (2011): 32–41. http://dx.doi.org/10.21528/lnlm-vol9-no1-art3.

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37

Neuenschwander, W., P. Fua, G. Székely, and O. Kübler. "Velcro Surfaces: Fast Initialization of Deformable Models." Computer Vision and Image Understanding 65, no. 2 (February 1997): 237–45. http://dx.doi.org/10.1006/cviu.1996.0578.

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38

Williams, Christopher K. I., Michael Revow, and Geoffrey E. Hinton. "Instantiating Deformable Models with a Neural Net." Computer Vision and Image Understanding 68, no. 1 (October 1997): 120–26. http://dx.doi.org/10.1006/cviu.1997.0540.

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39

Ghebreab, S., A. W. M. Smeulders, and P. R. Pfluger. "Necklaces: Inhomogeneous and Point-Enhanced Deformable Models." Computer Vision and Image Understanding 86, no. 2 (May 2002): 96–117. http://dx.doi.org/10.1006/cviu.2002.0969.

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40

Moss, William, Ming C. Lin, and Dinesh Manocha. "Constraint-based motion synthesis for deformable models." Computer Animation and Virtual Worlds 19, no. 3-4 (2008): 421–31. http://dx.doi.org/10.1002/cav.246.

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41

Nealen, Andrew, Matthias Müller, Richard Keiser, Eddy Boxerman, and Mark Carlson. "Physically Based Deformable Models in Computer Graphics." Computer Graphics Forum 25, no. 4 (December 2006): 809–36. http://dx.doi.org/10.1111/j.1467-8659.2006.01000.x.

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42

Kavan, L., and J. Žára. "Fast Collision Detection for Skeletally Deformable Models." Computer Graphics Forum 24, no. 3 (September 2005): 363–72. http://dx.doi.org/10.1111/j.1467-8659.2005.00861.x.

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43

Goldenstein, Siome, Christian Vogler, and Luiz Velho. "Adaptive Deformable Models for Graphics and Vision+." Computer Graphics Forum 24, no. 4 (December 2005): 729–41. http://dx.doi.org/10.1111/j.1467-8659.2005.00898.x.

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44

Klein, A. K., F. Lee, and A. A. Amini. "Quantitative coronary angiography with deformable spline models." IEEE Transactions on Medical Imaging 16, no. 5 (1997): 468–82. http://dx.doi.org/10.1109/42.640737.

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45

GIRALDI, GILSON A., and ANTONIO A. F. OLIVEIRA. "INVARIANT SNAKES AND INITIALIZATION OF DEFORMABLE MODELS." International Journal of Image and Graphics 04, no. 03 (July 2004): 363–84. http://dx.doi.org/10.1142/s0219467804001464.

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Parametric snake models are deformable models very useful for boundary extraction and tracking. Despite their abilities, the non-invariance of the internal energy under affine transformations and the non-convexity of the model energy are known limitations for most of these methods. The former has been addressed in the context of active shape models. The latter can be addressed through Dual contour approaches as well as an automatic procedure to initialize the model closer to the desired boundary. In this paper we first analyze the effects of reparameterization for invariant snake models through the Lie Group Theory. In the second part of this work, we present our Dual-T-Snakes model and a methodology based on the T-Snakes framework to initialize deformable models. In the experimental results we explore and combine these techniques for segmentation of medical images.

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46

Cani-Gascuel, M., and M. Desbrun. "Animation of deformable models using implicit surfaces." IEEE Transactions on Visualization and Computer Graphics 3, no. 1 (1997): 39–50. http://dx.doi.org/10.1109/2945.582343.

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47

Christoudias, C. Mario, Louis-Philippe Morency, and Trevor Darrell. "Non-parametric and light-field deformable models." Computer Vision and Image Understanding 104, no. 1 (October 2006): 16–35. http://dx.doi.org/10.1016/j.cviu.2006.06.001.

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48

Berenguer-Vidal, R., R. Verdú-Monedero, and J. Morales-Sánchez. "Convergence analysis of multidimensional parametric deformable models." Computer Vision and Image Understanding 135 (June 2015): 157–77. http://dx.doi.org/10.1016/j.cviu.2015.01.009.

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49

Delibasis, K., and PE Undrill. "Anatomical object recognition using deformable geometric models." Image and Vision Computing 12, no. 7 (September 1994): 423–33. http://dx.doi.org/10.1016/0262-8856(94)90026-4.

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50

Vogler, Christian, Siome Goldenstein, Jorge Stolfi, Vladimir Pavlovic, and Dimitris Metaxas. "Outlier rejection in high-dimensional deformable models." Image and Vision Computing 25, no. 3 (March 2007): 274–84. http://dx.doi.org/10.1016/j.imavis.2005.10.010.

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