Novel Detection Method Using PCNN Combined with Gray Scale Entropy Transform in Small Target Images

In order to conduct small target image segmentation automatically, a new method based on pulse couple neural networks(PCNN) and the gray scale entropy, is proposed for image segmentation and detection, starting from the aspect of characteristics of single small target image. Based on nonlinear gray scale entropy transform on an image with complex background and stochastic noise, this algorithm takes into account the condition that the gray scale images of gray scale entropy satisfy the object to background ratio of prior probability, and select the local region including a single small target which can be regarded as image processing part. Iterative segmentation and detection using improved PCNN is utilized under the criterion of local minimum cross-entropy. The experimental results show that the novel method not only can detect small target with the disturbance of complex background and random noise reliably, but also implement the best segmentation and detection automatically. This algorithm has stronger adaptability and performs well in target detecting.