In this paper, a text vectorization method is proposed using OCR (Optical Character Recognition) and character stroke modeling. This is based on the observation that for a particular character, its font glyphs may have different shapes, but often share same stroke structures. Like many other methods, the proposed algorithm contains two procedures, dominant point determination and data fitting. The first one partitions the outlines into segments and second one fits a curve to each segment. In the proposed method, the dominant points are classified as “major” (specifying stroke structures) and “minor” (specifying serif shapes). A set of rules (parameters) are determined offline specifying for each character the number of major and minor dominant points and for each dominant point the detection and fitting parameters (projection directions, boundary conditions and smoothness). For minor points, multiple sets of parameters could be used for different fonts. During operation, OCR is performed and the parameters associated with the recognized character are selected. Both major and minor dominant points are detected as a maximization process as specified by the parameter set. For minor points, an additional step could be performed to test the competing hypothesis and detect degenerated cases.
Current image vectorization techniques mainly deal with images with simple and plain colors. For full-color
photographs, many difficulties still exist in object segmentation, feature line extraction, and color distribution
In this paper, we propose a high-efficiency image vectorization method based on importance sampling and triangulation.
A set of blue-noise sampling points is first generated on the image plane by an improved error-diffusion sampling
method. The point set well preserves the features in the image. Then after triangulation on this point set, color
information can be recorded on the mesh vertices to form a vector image. After certain image editing, e.g. scaling or
transforming, the whole image can be reconstructed by color interpolating inside each triangle.
Experiments show that the method has high performing efficiency and abilities in feature-preserving. It will bring
benefits to many applications, e.g. image compressing, editing, transmitting and resolution enhancement.
The methods of image registration and fusion are very important basis of cylindrical panorama creating. There are many relevant algorithms been developed, but many of them usually have limitations on their qualities and efficiency. In this paper, an automatic approach for creating cylindrical panorama is introduced. Compared with many commonly used algorithms, this algorithm is simple and effective. In our method, we adopted an FFT-based phase-correlation algorithm to register images, and we also employed a multi-resolution pyramid algorithm to perform image fusion. By preprocessing the input images using such methods as histogram equalization, we further increased the accuracy and robustness of image registration. Our experiments show that the method is simple, efficient and effective.