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In this paper, we use Unit-linking PCNN (Pulse Coupled Neural Network), the simplified model of PCNN consisting of spiking neurons, to code a 2-dimensional image into a 1-dimensional time sequence called global Unit-linking PCNN image icon or time signature, including features of the original image and having the translation, rotation, and scale invariance. Dividing an image into multiple parts can obtain local Unit-linking PCNN image icons corresponding to the image’s local regions, which can reflect the local changes of the image. In the meantime, the global and the local Unit-linking PCNN image icons are used in navigation, object detection, and image authentication. In navigation, global Unit-linking PCNN image icon shows qualified performance especially in non-stationary-video navigation. Object detection using global Unit-linking PCNN image icon, is independent of variances of translation, rotation, and scale, and object segmentation is avoided. In image authentication, using local Unit-linking PCNN image icon can authenticate correctly some juggled images failed to authenticate by using local histogram or local mean intensity, and can locate the juggled positions in the juggled images with some accuracy.  相似文献
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