Texture Segmentation of Road Environment Scene Using SfM Module and HLAC Features

Abstract

This paper presents a new image segmentation method for the recognition of texture-based objects in a road environment scene. Using the proposed method, we can classify texture-based objects three dimensionally using the SfM (Structure from Motion) module and the HLAC (Higher-order Local Autocorrelation) features. By estimating the vehicle's ego-motion, the SfM module can reconstruct the three dimensional structure of the road scene. Texture features of input images are extracted from HLAC functions according to their depth, as obtained using the SfM module. The proposed method can effectively recognize texture-based objects of a road scene by considering their three-dimensional structure in a perspective 2D image. Experimental results show that the proposed method can not only effectively classify the texture patterns of structures in a 2D road scene, but also represent classified texture patterns as three-dimensional structures. The proposed system can revolutionize a three-dimensional scene understanding system for vehicle environment perception.