基于SIFT的车标识别算法

针对车标识别过程中匹配阈值难、识别速度慢的问题,提出一种基于尺度不变特征变换(SIFT)的特征匹配车标识别算法.利用SIFT算子对图像的视角、平移、放射、亮度、旋转等不变特性进行提取,并采用BP神经网络算法自主选取车标图像特征进行分类、匹配和识别.仿真实验结果表明,简单车标和复杂车标的识别率平均值均达90%以上,该算法识别速度较快、识别率较高,能满足实际应用的需要.

Vehicle Logo Recognition Algorithm Based on SIFT

Aiming at the problems that the matching threshold was difficult and the recognition speed was slow in the process of vehicle logo recognition, we proposed a vehicleKG-*4]\|logo recognition algorithm based on feature matching of scale invariant feature transformation (SIFT). The SIFT operator was used to extract the invariant features of the image, such as viewing angle, translation, radiation, brightness and rotation, and the BP neural network algorithm was used to autonomously select vehicle logo image features for classification,matching and recognition. The results of simulation experiment show that the mean values of recognition rate for simple vehicle logos and complex vehicle logos are all more than 90%, the algorithmhas faster recognition speed and higher recognition rate, which can meet the needs of practical application.