Abstract
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.
Highlights
Visualization of image data tends to be spread into many aspects in today’s industries such as the illustrations in magazines, the logo on the web, and the images of posters
Xia et al [1] introduced a vectorization algorithm, in which they constructed the initial mesh in the subpixel level and used to fit color. in-Plate Splines (TPS) [4] to do the color fitting
The representing feature in subpixel can pinpoint the location of features; in-Plate Splines (TPS) seem to perform well on the surface smooth
Summary
Received 5 July 2020; Revised 22 September 2020; Accepted 21 October 2020; Published 2 November 2020. Vector images have the ability to save memory as well as to enlarge without distortion. It has been commonly adopted in data visualization (image data) because it can be scaled to multiple sizes to fit different scenes. 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. Note that our method can be applied to information security and privacy, since one image can be encoded to some triangles and colors
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