Feature binding pulse-coupled neural network model using a double colour space

The feature binding problem is one of the central issues in cognitive science and neuroscience. To implement a bundled identification of colour and shape for a colour image, a double-space vector feature binding PCNN (DVFB-PCNN) model was proposed based on the traditional pulse-coupled neural network (PCNN). In this model, the method of combining RGB colour space with HSI colour space successfully solved the problem that all colours cannot always be separated completely. Through the use of the first pulse emission time of the neurons, the different characteristics were successfully separated. Through the colour sequence produced by this process, the different characteristics belonging to the same perceived object were bound together. Experiments showed that the model can successfully achieve separation and binding of image features and will be a valuable tool for PCNN in the feature binding of colour images.