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Vector image is a type of image composed of many geometric primitives. Compared with bitmaps, vector images have the ability to save memory as well as to enlarge without distortion. Meanwhile, it has been commonly adopted in data visualization (image data) because it can be scaled to multiple sizes to fit different scenes. For instance, it can be applied for the illustrations in newspapers and magazines, the logo on the web, the background for poster, the design of text, and traffic signs. However, transforming a bitmap to vector image is still a challenging problem because of the complicated content of a bitmap, which tends to consist of more than just simple geometry. Aiming at this issue, there is a new approach proposed to transform from bitmaps to vector images, which is based on triangle units and consists of three steps. In detail, firstly, there is an initial mesh constructed for one image in pixel level after detecting features. Then, the initial mesh will be simplified by collapsing two vertices as the initial mesh is too dense to represent one image. Specifically, there are two main parts in this step, which are collapse conditions and collapse influences. In the step of collapsing, issues such as overlap and sharp triangles can be conquered by a sort-edge method (which will be illustrated specifically later). The final step is to select one color for each triangle, since it is helpful to save the memory and speed up the process of this method. In addition, one color will represent one triangle; hence, in the final step, the four-triangle sample method will be applied in order to prevent a vector image from generating too large color discontinuity. Once the pretest proceeds without mistake, the method above is able to work for the general bitmaps. Note that our method can be applied to information security and privacy, since one image can be encoded to some triangles and colors.

The pedestrian detection is a hot research topic in computer recognition. It involves not only the pedestrian location information but also the intrusion detection function, which has wide prospects in the application of vehicle traffic, campus monitoring, and building guard. However, the identification accuracy and recognition speed play an important role in the pedestrian detection, which calls for a fast pedestrian detection approach. The general pedestrian detection implementation, based on the integral channel features method and soft cascade classifier, is the popular technique in the current business application since its better speed and accuracy. Thus, this method uses the feature approximation technique and multiple classifiers to achieve the feature computing, which speeds up the detection without resizing image. To this end, this paper is motived to propose a multi-scale handling method for the fast pedestrian detection, using the tactics detection from sparse to dense. Our pedestrian detection method consists of four parts functions, mainly pedestrian statistics and intrusion detection, pedestrian tracking and pedestrian flow statistics. All these modules are introduced with its details about design and implementation. In Addition, the proposed multi-scale handling method can be applied into most of object detectors to improve their recognition speed. In conclusion, our proposed approach has a good potential application prospect in the video surveillance system.

In this paper, by using the improved fingerprint pattern, the fingerprint preprocessing effect of using Gabor filtering have been effectively improved. Then, by combining the advantages of the direction-based binarization algorithm and the local threshold algorithm, this paper can effectively get the fingerprint image binarization and remains the topological structures of the image. Finally, the experiments prove that the fingerprint image enhancement and improved binarization algorithm can improve the accuracy of the fingerprint feature extraction. Keywords-Image enhancement, Binarization, Recursive algorithm