LBF model is one of the well-known region-based active contour models. LBF模型是一个著名的基于区域的活动轮廓模型。
A cascade neural network composed of double-layer radial basis function ( RBF) and linear basis function ( LBF) is proposed for pattern classification. 提出了由两层径基函数(RBF)和两层线性基本函数(LBF)网络组成的串联神经网络模式分类方法。
The result of the emulation proves that the improved algorithm is much better than LEACH in lifetime and LBF. 最后用Mat-lab对LEACH算法和改进后的算法进行仿真,证实改进后的算法在网络生存时间和簇负载平衡程度上比LEACH算法有了很大提高。
Both oxyacetylene line heating forming ( OLHF) and laser bending forming ( LBF) have some defects, but plasma arc has higher average energy conversion factor, lower operation cost and some other advantages. 水火弯曲成形和激光弯曲成形均存在一定局限性,而等离子体弧具有平均能量转换效率高、成本较低等优点。
LBF models often fail to correct the non-uniform object segmentation to the side of the contour curve, when the initial level set curve is away from the object or cross the border. 当初始水平集曲线远离目标边界或者与目标边界发生交叉时,LBF模型往往不能正确的把非均匀目标分割到活动轮廓曲线一侧。
Simulation results show that LBF reduces the number of nodes that broadcast packets and reduces the energy consumption of data distribution. 仿真结果显示,层次泛洪策略有效地减少了网络中广播数据包的节点数量,降低了数据包扩散过程中的能量消耗。
In LBF, the whole network is divided into several levels according to distance ( hop) between sensor nodes and sink node. 在该策略中,整个网络根据传感器节点到汇聚节点的跳数分成不同的层次。
When calculating routing table, the routing metric is composed of LCF, LBF and path hop, thus the routing protocol could support Quality of service ( Qos) and realize load balance. 在计算路由表的时候,LCF、LBF与路径跳数一起构成路由判据,使得路由协议能够提供一定的Qos支持,达到负载均衡的目的。
In contrast to C-V models, LBF model introduces a local binary fitting ( LBF) energy with a Gaussian kernel function. 与C-V模型不同,LBF模型引入了一个以高斯函数为核函数的局部二值拟合(localbinaryfitting,LBF)能量。
However, LBF model is sensitive to initial contour curve, due to local property. 然而,LBF模型的局部特性使得该模型对初始轮廓曲线的位置较为敏感。
Because the LBF energy enables the extraction of accurate local image information, LBF model can address the segmentation of images with intensity inhomogeneity, to which C-V models are not applicable. 因为LBF能量能够获取图像的局部信息,所以LBF模型解决了PC模型不能处理灰度不均一图像的分割问题。