A neurocomputing model for ganglion cell's color opponency mechanism and its application in image analysis

The vision system of primates could process colorful scenes very efficiently. This is because, in biological retina, there are three types of cone cells and several types of ganglion cells that possess highly complicated receptive fields. The central and the surrounding areas of a receptive field are usually composed of different types of cones. Typically, they form two classes, namely the red-green opponency and the blue-yellow opponency. In order to develop a new representation schema for colorful images, we simulated some physiological mechanisms in retina, such as the opponent color theory. Based on anatomical and electrophysiological findings of ganglion cells, we proposed a bio-inspired color processing method. We designed a neural network simulating retinal ganglion cells (GCs) and their classical receptive fields (CRF), and also raised a dynamic procedure to control receptive field's self-adjustment according to the characteristics of an image. A great number of experiments were conducted on natural images. The results showed that this new method could reserve crucial structural information of an image and suppress trivial information at the same time. Depending on these new representations, some upcoming processing, such as image segmentation, could be improved significantly. Image segmentation is very critical to ultimate image understanding. However, actual image stimuli are a little bit far from biological studies. Our work integrated them together and explained how the physiological opponent-color theory could facilitate image processing in real applications.