Terrain identification in grayscale images with recurrent neural networks
Mahmoud Abou-Nasr · 2010
This paper presents an approach for terrain identification in grayscale images based on recurrent neural networks. The network in this work has 16 inputs that represent 16, horizontally contiguous pixels from the grayscale image. The network is trained as a binary classifier that classifies the input pixels while being scanned from the top to the bottom of the image. Experiments were performed on grayscale images of a road in natural surroundings of grass, some trees and falling tree leaves. The trained network classifier in generalization testing experiments has managed to classify pixels representing the road as they are being scanned with accuracy of ~ 89 % and pixels representing falling tree leaves with accuracy of ~ 88 %.