Versatile Neural Network Method for Recovering Shape from Shading by Model Inclusive Learning
Versatile Neural Network Method for Recovering Shape from Shading by Model Inclusive Learning
复制标题
通过模型包容学习从阴影中恢复形状的多功能神经网络方法
DOI：
10.1109/ijcnn.2011.6033644
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Yasuaki Kuroe and Hajimu Kawakami
中科院分区：
文献类型：
--
作者：
Hitoshi Iima ;Yasuaki Kuroe ;Kazuo Emoto;Yasuaki Kuroe and Hajimu Kawakami
从阴影中恢复形状的问题在计算机视觉和机器人技术中非常重要。在本文中，我们提出了一个通用的方法来解决这个问题的神经网络。我们引入一个数学模型，我们称之为“成像模型”，表达的过程中，图像是从一个物体表面形成。我们把这个问题归结为一个模型包容性的神经网络学习问题，并提出了一种解决方法，该方法将图像形成模型包含在神经网络的学习循环中。所提出的方法是通用的，在这个意义上，它可以解决在各种情况下的问题。通过在各种情况下进行的实验，所提出的方法的有效性。
The problem of recovering shape from shading is important in computer vision and robotics. In this paper, we propose a versatile method of solving the problem by neural networks. We introduce a mathematical model, which we call `image-formation model', expressing the process that the image is formed from an object surface. We formulate the problem as a model inclusive learning problem of neural networks and propose a method to solve it. In the proposed learning method, the image-formation model is included in the learning loop of neural networks. The proposed method is versatile in the sense that it can solve the problem in various circumstances. The effectiveness of the proposed method is shown through experiments performed in various circumstances.