基于颜色和纹理特征的道路图像分割

针对移动机器人的视觉导航需求,研究彩色道路图像的精确分割问题.为解决图像中的干扰因素对道路分割精度所带来的负面影响,首先对道路图像进行了基于颜色特征的过分割,对颜色过分割结果再进行基于小波纹理特征的区域融合.通过考察区域空间位置的相邻性以及小波纹理特征的相似性,该算法将过分割区域进行了再融合,实现了复杂环境下对道路区域及环境区域的精确分割.实验结果表明,所提出的区域分割算法快速、有效、实时性好,并对复杂环境具有良好的鲁棒性,可以满足移动机器人实际运行的要求.

Segmentation of Full Vision Images Based on Colour and Texture Features

Vision guidance is one of the key techniques for autonomous robot navigation, which allows a robot to find a valid path and recognize the environment. This paper analyses and investigates the problems of image-based road detection and understanding. Based on the colour features of the road, an improved region-growing algorithm is used to segment the images. Due to the influence of environment illumination and disturbances, some lane regions may be lost by over segmentation in the image. To improve the accuracy of lane segmentation, wavelet-based texture features and space adjacencies are employed to retrieve the lost lane regions. The experimental results demonstrate that the proposed method can achieve full-image segmentation and is of high precision, robust and reliability for real-time road segmentation and detection.