Liu X, Wang D L
Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210, USA.
IEEE Trans Neural Netw. 1999;10(3):564-73. doi: 10.1109/72.761713.
A locally excitatory globally inhibitory oscillator network (LEGION) is constructed and applied to range image segmentation, where each oscillator has excitatory lateral connections to the oscillators in its local neighborhood as well as a connection with a global inhibitor. A feature vector, consisting of depth, surface normal, and mean and Gaussian curvatures, is associated with each oscillator and is estimated from local windows at its corresponding pixel location. A context-sensitive method is applied in order to obtain more reliable and accurate estimations. The lateral connection between two oscillators is established based on a similarity measure of their feature vectors. The emergent behavior of the LEGION network gives rise to segmentation. Due to the flexible representation through phases, our method needs no assumption about the underlying structures in image data and no prior knowledge regarding the number of regions. More importantly, the network is guaranteed to converge rapidly under general conditions. These unique properties may lead to a real-time approach for range image segmentation in machine perception.
构建了一种局部兴奋性全局抑制性振荡器网络（LEGION）并将其应用于距离图像分割，其中每个振荡器与其局部邻域内的振荡器具有兴奋性横向连接以及与全局抑制剂的连接。一个由深度、表面法线以及平均曲率和高斯曲率组成的特征向量与每个振荡器相关联，并从其相应像素位置的局部窗口进行估计。应用了一种上下文敏感方法以获得更可靠和准确的估计。两个振荡器之间的横向连接基于它们特征向量的相似性度量来建立。LEGION网络的涌现行为导致了分割。由于通过相位进行灵活表示，我们的方法无需对图像数据中的底层结构进行假设，也无需关于区域数量的先验知识。更重要的是，该网络在一般条件下保证能快速收敛。这些独特的特性可能会导致一种用于机器感知中距离图像分割的实时方法。
