Diffusion curve-based RGBD image vectorization method

Abstract

The invention discloses a diffusion curve-based RGBD image vectorization method. The method includes the following nine steps: inputting an original RGB color image and a depth image D which are to be processed, performing multi-scale Canny edge extraction on the RGB image, coloring the acquired multi-scale binary edge image to generate a color edge image, restoring the depth image, performing depth edge extraction on the restored depth image D' to generate a depth edge image, performing subtraction on the two edge images to obtain a detail edge image, performing tracking merging on the detail edge image and the depth edge image to generate a group of broken line sections, performing color sampling and Bezier curve fitting on the broken line sections to obtain a group of diffusion curves, using colors on the curve as constraints to solve a Poisson's equation to obtain a vectorization result. The diffusion curve-based RGBD image vectorization method provided by the invention adopts RGBD images to obtain an object outline, well restores the real outline of an object, and solves the situation of multi-scale Canny invalidation in some color environments. The method has a clear algorithm and a robust result, and is suitable for vectorization of the RGBD images.

Description

technical field [0001] The invention relates to the technical fields of computer graphics and image processing, in particular to a diffusion curve-based RGBD image vectorization method. Background technique [0002] The image vectorization method provides another non-destructive expression method for traditional bitmaps, and provides convenient tools for artists to create art on ordinary bitmaps. [0003] When artists want to post-create an object in a bitmap, they need to obtain the general outline and internal details of the object. Gradient Mesh is a grid-based vectorized primitive, which can render smooth color transitions and supports the characteristics of traditional vector graphics such as infinite scaling, see Sun J, Liang L, Wen F, et al. Image vectorization using optimized gradient meshes ACM Transactions on Graphics (TOG). ACM, 2007, 26(3): 11. However, this method is not convenient for post-processing, because the process of operating the grid is very cumbersom...

AI Technical Summary

Problems solved by technology

This method uses color information and additional depth information to effectively extract object outlines, solves the problem of unclear borders in traditional vectorization methods based on RGB diffusion curves, and provides artists with a vectorization result that is convenient for later creation

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