论文标题
对数形态神经网络对照明变化
Logarithmic Morphological Neural Nets robust to lighting variations
论文作者
论文摘要
形态神经网络可以了解所需的输出图像的结构功能的权重。但是，这些网络对具有光学原因的图像（例如光强度的变化）的照明变化本质上并不具有鲁棒性。在本文中，我们介绍了一个形态神经网络，该网络具有对照明变化的稳健性。它基于对数数学形态（LMM）的最新框架，即使用对数图像处理（LIP）模型定义的数学形态。该模型具有一种唇添加定律，该定律在图像中模拟光强度的变化。我们特别了解LMM操作员对这些变化的鲁棒性的结构化功能，即：唇addive Asplund距离的地图。图像中的结果表明，我们的神经网络验证了所需的属性。
Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of Logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic Image Processing (LIP) model. This model has a LIP additive law which simulates in images a variation of the light intensity. We especially learn the structuring function of a LMM operator robust to those variations, namely : the map of LIP-additive Asplund distances. Results in images show that our neural network verifies the required property.
