Pajares Gonzalo
Departamento de Sistemas Informáticos y Programación, Facultad de Informática, Universidad Complutense, Madrid 28040, Spain.
IEEE Trans Neural Netw. 2006 Sep;17(5):1250-64. doi: 10.1109/TNN.2006.875978.
This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed. An energy function is derived, so that the network converges to stable states. The analog Hopfield's model allows each node to take on analog state values. Unlike most widely used approaches, where binary labels (changed/unchanged) are assigned to each pixel, the analog property provides the strength of the change. The main contribution of this paper is reflected in the customization of the analog Hopfield neural network to derive an automatic image change detection approach. When a pixel is being processed, some existing image change detection procedures consider only interpixel relations on its neighborhood. The main drawback of such approaches is the labeling of this pixel as changed or unchanged according to the information supplied by its neighbors, where its own information is ignored. The Hopfield model overcomes this drawback and for each pixel allows a tradeoff between the influence of its neighborhood and its own criterion. This is mapped under the energy function to be minimized. The performance of the proposed method is illustrated by comparative analysis against some existing image change detection methods.
本文概述了一种基于模拟霍普菲尔德神经网络（HNN）的优化松弛方法，用于解决两幅图像之间的图像变化检测问题。通过逐像素相减两幅图像来获得差异图像。构建网络拓扑结构，使得差异图像中的每个像素都是网络中的一个节点。每个节点由其状态表征，该状态决定一个像素是否发生了变化。推导了一个能量函数，以便网络收敛到稳定状态。模拟霍普菲尔德模型允许每个节点采用模拟状态值。与大多数广泛使用的方法不同，在那些方法中，为每个像素分配二进制标签（已更改/未更改），而模拟特性提供了变化的强度。本文的主要贡献体现在对模拟霍普菲尔德神经网络进行定制，以得出一种自动图像变化检测方法。当处理一个像素时，一些现有的图像变化检测程序仅考虑其邻域内的像素间关系。此类方法的主要缺点是根据其邻居提供的信息将该像素标记为已更改或未更改，而忽略了其自身信息。霍普菲尔德模型克服了这一缺点，对于每个像素，允许在其邻域的影响与其自身准则之间进行权衡。这在要最小化的能量函数下进行映射。通过与一些现有的图像变化检测方法进行对比分析，说明了所提方法的性能。
