Absolute Pose Estimation of Central Cameras Using Planar Regions

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The sample implementation of the absolute pose estimation algorithm proposed in the following paper will be available when the paper gets accepted:

1. Robert Frohlich, Levente Tamas, Zoltan Kato. Absolute Pose Estimation of Central Cameras Using Planar Regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, ESSN: 1939-3539, July 2019.

Affine Shape Alignment Using Covariant Gaussian Densities

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The sample implementation and benchmark dataset of the registration algorithm proposed in the following paper:

1. Csaba Domokos and Zoltan Kato. Affine Shape Alignment Using Covariant Gaussian Densities: A Direct Solution. Journal of Mathematical Imaging and Vision, 51(3) 385-399, March 2015.

Nonlinear Shape Registration without Correspondences

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The sample implementation and benchmark dataset of the nonlinear registration algorithm described in the following paper:

1. Csaba Domokos, Jozsef Nemeth, and Zoltan Kato. Nonlinear Shape Registration without Correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(5):943--958, May 2012.

Affine Registration of 3D Objects

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This is the sample implementation and benchmark dataset of the 3D object registration algorithm described in the following papers:

1. Attila Tanacs and Zoltan Kato. Fast Linear Registration of 3D Objects Segmented from Medical Images. In Proceedings of International Conference on BioMedical Engineering and Informatics, Shanghai, China, pages 299--303, October 2011. IEEE.
2. Attila Tanacs, Joakim Lindblad, Natasa Sladoje and Zoltan Kato. Estimation of Linear Deformations of 3D Objects. In Proceedings of International Conference on Image Processing, Hong Kong, China, pp. 153-156, September 2010. IEEE.

Affine Registration of Planar Shapes

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This is the sample implementation and benchmark dataset of the binary image registration algorithm described in the following paper:

1. Csaba Domokos and Zoltan Kato. Parametric Estimation of Affine Deformations of Planar Shapes. Pattern Recognition, 43(3):569--578, March 2010.

Supervised Color Image Segmentation in a Markovian Framework

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This is the sample implementation of a Markov random field based color image segmentation algorithm described in the following paper:

1. Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters, 22(3-4):309--321, March 2001.

Supervised Image Segmentation Using Markov Random Fields

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This is the sample implementation of a Markov random field based image segmentation algorithm described in the following papers:

1. Mark Berthod, Zoltan Kato, Shan Yu, and Josiane Zerubia. Bayesian Image Classification Using Markov Random Fields. Image and Vision Computing, 14:285--295, 1996. Keyword(s): Bayesian image classification, Markov random fields, Optimisation.


2. Zoltan Kato, Josiane Zerubia, and Mark Berthod. Satellite Image Classification Using a Modified Metropolis Dynamics. In Proceedings of International Conference on Acoustics, Speech and Signal Processing, volume 3, San-Francisco, California, USA, pages 573-576, March 1992. IEEE.


3. Zoltan Kato. Mod?isations markoviennes multir?olutions en vision par ordinateur. Application a` la segmentation d'images SPOT. PhD Thesis, INRIA, Sophia Antipolis, France, December 1994. Note: Available in French (follow the URL link) and English. Keyword(s): computer vision, early vision, Markovian model, multiscale model, hierarchical model, parallel combinatorial optimization algorithm, multi-temperature annealing, parameter estimation.