论文标题
通过结合霍夫变换和质地的特征来改善路标检测性能
Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture
论文作者
论文摘要
随着智能系统在不同领域的大量用途，为了提高驾驶员和行人的安全，道路和交通标志识别系统一直是一个具有挑战性的问题，并且是多年来的重要任务。但是，在对阿拉伯环境感兴趣的图像中对交通标志的检测和识别领域进行的研究仍然不足。对现场存在的道路标志的检测是交通标志检测和认可的主要阶段之一。在本文中，已经制定了一种有效的解决方案，以增强道路标志检测，包括阿拉伯语环境，基于颜色分割的性能，随机的霍夫变换以及Zernike Moments和Haralick功能的组合。分割阶段对于确定图像中的感兴趣区域（ROI）很有用。随机的霍夫变换（RHT）用于检测圆形和八角形状。通过提取Haralick特征和Zernike时刻的提取，这一阶段得到了改善。此外，我们将其用作基于SVM的分类器的输入。实验结果表明，提出的方法使我们能够执行测量精度。
With the large uses of the intelligent systems in different domains, and in order to increase the drivers and pedestrians safety, the road and traffic sign recognition system has been a challenging issue and an important task for many years. But studies, done in this field of detection and recognition of traffic signs in an image, which are interested in the Arab context, are still insufficient. Detection of the road signs present in the scene is the one of the main stages of the traffic sign detection and recognition. In this paper, an efficient solution to enhance road signs detection, including Arabic context, performance based on color segmentation, Randomized Hough Transform and the combination of Zernike moments and Haralick features has been made. Segmentation stage is useful to determine the Region of Interest (ROI) in the image. The Randomized Hough Transform (RHT) is used to detect the circular and octagonal shapes. This stage is improved by the extraction of the Haralick features and Zernike moments. Furthermore, we use it as input of a classifier based on SVM. Experimental results show that the proposed approach allows us to perform the measurements precision.
