【正文】 e license plate. This method has two advantages. One is that it does not strictly restrict the lighting condition, surroundings and other factors. Another one is that when the license plates are located, character regions, namely, MSER, are segmented simultaneously. The detailed processes are described below.A. Candidate Character Regions ExtractionThe character regions can be considered as regions of reasonable size, and gray levels of pixels in such regions are similar at the same time. In [7], many thresholds are set to get many binary images, and then candidate character regions are extracted from these binary images using connected ponents analysis. However, these gray level thresholds rely on the lighting condition, so extracted connected ponents are sensitive with illumination. In order to overe such disadvantage, we employ a region feature detector, MSER (Maximally Stable Extremal Region) detector, to extract the region feature in images as candidate character regions. Originally, MSER was proposed to settle the matching problem in wide baseline stereo vision in [8] with the benefit of their excellent affinity invariance. This characteristic can also help with the candidate character region extraction in the license plate localization algorithm. The MSER detection process is similar with the watershed algorithm. Generally speaking, the detection is to use some different thresholds to binarize the image, and then the binarized regions with the most stable area variation are defined as MSER. Exactly, we use thresholds from 0 to 255 to binarize the image first of all. It is supposed that the pixels with gray level lower than the threshold are set to white and those with higher gray level are set to black. Then we can get a series of black and white regions called Extremal Regions, which can be denoted as . Extremal means that all the pixels within those regions have the gray level higher or lower than the gray level of pixels in region’s boundary. If the area of an extremal region is stable in a wide range of gray level, then this region is a maximal stable extremal region (MSER). Using mathematical expression, that means if and only if ，gets the local minimum, MSER is obtained. The symbol denotes the step length of the gray level threshold in this formulation. Considering that gray levels can be adjusted in two opposite directions, after these two operations we can get two kinds of extremal regions respectively, ., bright extremal regions and dark extremal regions. The difference of these two regions also contributes to the inference of license plate location, as described in next subsection. The standard MSER detection is described above. In our implementation, linear time MSER detection [9] is utilized to increase the efficiency of MSER extraction. Compared with other region features, MSER has many advantages and can get better performance in most applications [10]. In our task of detecting characters, the main superiority is MSER’s invariance to lighting change. As long as the luminance in the image changes monotonously, the MSER can keep stable even illumination changes from daytime to nighttime. In order to detect blobs even in some lowcontrast image regions and ensure any MSER not tobe left, we slightly adjust the MSER detection process. First, we set the threshold step as a minimum value of 1. Then the diversity of extremal regions’ area in the same position is set to a very small value, so that we can get as many extremal regions as possible. Afterward, we only save the extremal regions with the minimum area variation among all regions in the same position as the final MSER. Meanwhile, the MSER are restricted by some preset license plate parameters. The remaining MSER can be considered as candidate license plate characters. An example of MSER extraction result in a test image is shown in Fig. 4. B. License Plate Location InferenceIf the extracted MSERS just are the characters in a license plate, we can infer the exact location of the license plate easily. However, in practical applications, just as the result shown in Fig. 4, some characters may not be detected as MSERS. The reason is various, such as the unequal illumination on license plate or the joint of characters with the license plate boundary. In addition, there are a large number of MSERS that meet the restriction in the background. Therefore, after we detect the MSERS in the image, we must check them and find those having similar layout with characters in standard license plates. The process of inference and analysis is described below.The concepts of nodes and edges in the graphical model are introduced in our inference. We intend to set the candidate characters as nodes and build an edge between two nodes which meet the geometric relationship and gray level relationship of two adjacent characters. First of all, we calculate the value of geometric relation and gray level relation between every two MSERS that were extracted before. Hence, the MSERS relationship matrix is built. The geometric relationship includes Euclidean distance, horizontal distance, vertical distance between two MSERS, difference of height and width between their bounding boxes, etc. The gray level relationship means that whether both blobs are bright or dark MSERS. Next we deal with each MSER to search in the left direction for another one that satisfies the geometric relation between two adjacent characters according to the value in the relationship matrix. If such is not found due to some undetected characters, then we search for two MSERS that have space of one character. Besides, the pair of matched MSERS must be the uniform bright or dark MSERS simultaneously. Then such two blobs can be set as nodes, and an edge is built between them. Meanwhile, the type of edge is labeled as either adjacent or at interval. The result of setting nodes and edges of Fig. 4 can be seen in Fig. 5, where yellow dots denote nodes and yel