Buhmann Joachim M, Lange Tilman, Ramacher Ulrich
Swiss Federal Institute of Technology, CH-8092 Zurich, Switzerland.
Neural Comput. 2005 May;17(5):1010-31. doi: 10.1162/0899766053491913.
A network of leaky integrate-and-fire (IAF) neurons is proposed to segment gray-scale images. The network architecture with local competition between neurons that encode segment assignments of image blocks is motivated by a histogram clustering approach to image segmentation. Lateral excitatory connections between neighboring image sites yield a local smoothing of segments. The mean firing rate of class membership neurons encodes the image segmentation. A weight modification scheme is proposed that estimates segment-specific prototypical histograms. The robustness properties of the network implementation make it amenable to an analog VLSI realization. Results on synthetic and real-world images demonstrate the effectiveness of the architecture.
提出了一种基于泄漏整合-激发（IAF）神经元的网络来分割灰度图像。该网络架构中，对图像块的分割分配进行编码的神经元之间存在局部竞争，其灵感来源于一种用于图像分割的直方图聚类方法。相邻图像位置之间的横向兴奋性连接使分割区域具有局部平滑性。类别归属神经元的平均发放率对图像分割进行编码。提出了一种权重修改方案，用于估计特定分割的原型直方图。该网络实现的鲁棒特性使其适用于模拟超大规模集成电路（VLSI）实现。在合成图像和真实世界图像上的结果证明了该架构的有效性。
