MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||5|
|Section||Modeling, Analysis, and Simulation of Intelligent Manufacturing Processes|
|Published online||19 June 2018|
Shape Modeling Based on Convolutional Restricted Boltzmann Machines
School of Computer Science, ShaanXi Normal University, 710119 Xi'an, ShaanXi, China
* Corresponding author : email@example.com
This paper proposes a kind of shape model based on convolutional restricted Boltzmann machines(CRBM), which can be used to assist the task of image target detection and classification. The CRBM is a generative model that can model shapes through the generative capabilities of the model. This paper presents the visual representation, construction process and training method of the model construction. This paper does experiments on the Weizmann Horse dataset. The results show that, compared with RBM, although the training time of this model is slightly longer, the test time of the model is similar, and it can better shape modeling, modeling of the details of the shape can be well expressed. The samples generated from CRBM look more realistic. The difference between the shape and the original shape generated by Euclidean distance measurement shows that the model has a strong ability to model shapes.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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