Segmentation Based on Spiking Neural Network Using Color Edge Gradient for Extraction of Corridor Floor

In this paper, for the purpose of obstacle avoidance for blind men in the environment of indoor corridor, a corridor ground segmentation algorithm is proposed using image processing mechanism of the human visual system combined with the existing segmentation algorithms in robot visual navigation techniques. The segmentation algorithm is based on a spiking neural network. First, three color image gradient maps are generated utilizing a spiking neural network. The best gradient map is generated from three color components to extract the effective and useful image edges. Then threshold segmentation method is used to eliminate unwanted gradient to identify the boundary of floor. Finally, the corridor ground is extracted. The experimental results show that the algorithm works efficiently and the boundary of floor can be extracted accurately for corridor images with certain noise textured and nontextured. The algorithm has the practicality and robustness for identification of ground floor in blind navigation.