RS Image PCNN Automatical Segmentation Based on Information Entropy
MMIT), 2010 Second International Conference(2010)
摘要
Pulse Coupled Neural Networks has the essential differences with the traditional artificial neural network in simulating biological visual, so PCNN is widely used in image processing fields. In PCNN model, In image processing, we often use the information entropy as tools to evaluate the effect of image processing, namely the greater the value of information entropy the better the image. The cycle number under the given parameters influences directly the segmentation result. Determining the loop-interaction cycle number at the best segmentation times is a difficult problem. This paper puts forward a PCNN image segmentation algorithm based on the maximum entropy principle. The algorithm determines the cycle number with the maximum entropy in order to realizing the best image segmentation automatically based on regions.
更多查看译文
关键词
pcnn image segmentation algorithm,image processing,segmentation result,loop-interaction cycle number,pulse coupled neural network,rs image,remote sensing image,image segmentation,maximum entropy principle,pcnn automatical image segmentation,information entropy,maximum entropy methods,image processing field,pcnn automatical segmentation,artificial neural network,maximum entropy,best segmentation time,best image segmentation,pcnn model,neural nets,cycle number,artificial neural networks,algorithm design and analysis,entropy,neurofeedback,value of information
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要