论文标题
使用来自灵长类动物视觉皮层的尖峰神经网络得出的特征的显着图
Saliency map using features derived from spiking neural networks of primate visual cortex
论文作者
论文摘要
我们提出了一个受生物视觉系统启发的框架，以生成数字图像的显着图。使用了专门用于颜色和方向感知的视觉皮层区域接受区域的著名计算模型。为了建模这些区域之间的连通性，我们使用Carlsim库，该库是一个尖峰神经网络（SNN）模拟器。卡尔西姆（Carlsim）产生的尖峰，然后用作提取的特征并输入我们的显着性检测算法。描述了这种新的显着性检测方法，并应用于基准图像。
We propose a framework inspired by biological vision systems to produce saliency maps of digital images. Well-known computational models for receptive fields of areas in the visual cortex that are specialized for color and orientation perception are used. To model the connectivity between these areas we use the CARLsim library which is a spiking neural network(SNN) simulator. The spikes generated by CARLsim, then serve as extracted features and input to our saliency detection algorithm. This new method of saliency detection is described and applied to benchmark images.
