牌照定位是牌照识别系统中的关键技术。目前在多数牌照定位方法中考虑到了牌照的颜色和纹理特征，但对不同环境的适应性不强。为解决这方面的问题，首先从机动车牌照具有固定颜色搭配的特点出发构造颜色搭配掩模矩阵,并利用此掩模矩阵对原边缘检测图像进行条件约束，得到约束二值边缘图像；然后应用具有去噪能力的形态学结构元，形成牌照粗定位候选区域；最后依据牌照的纹理特征从候选区域中提取出真正的牌照。采用了BP神经网络获得强适应性的HSI空间牌照颜色识别方法，并且只在边缘点邻域内实现颜色空间转换运算，能极大地缩减定位周期。经实验表明，该方法能在复杂的环境和不同光照条件下快速地实现不同牌照的精确定位。

License plate location is the key technology of license plate recognition. At present license plate color and texture feature were considered in the most license plate location methods, however, these methods had weak adaptability in different environment. In order to solve the problem, firstly, since the fixed color matches of the vehicle license plate, a color matched template matrix was constructed, which was used to restrict the initial edge detected image, so a restricted binary edge image could be obtained. Then, some morphologic operators which have the ability of eliminating noise were applied to form initial localization candidate regions. Finally, according to the texture feature of the license plate, the real license plate area was chosen from candidate regions. BP neural network was adopted to attain the strongly adaptive method which was used to recognize different colors in HSI space. And for shortening the period of location, only the color space in the neighborhood of edge pixels were conversed. The experiment showed that the accurate location of the license plate could be achieved in the complicated environment and different illumination, making use of this method.