Browsing by Author "Yao, Junfeng"

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    DMAT: Deformable Medial Axis Transform for Animated Mesh Approximation
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Yang, Baorong; Yao, Junfeng; Guo, Xiaohu; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Extracting a faithful and compact representation of an animated surface mesh is an important problem for computer graphics. However, the surface-based methods have limited approximation power for volume preservation when the animated sequences are extremely simplified. In this paper, we introduce Deformable Medial Axis Transform (DMAT), which is deformable medial mesh composed of a set of animated spheres. Starting from extracting an accurate and compact representation of a static MAT as the template and partitioning the vertices on the input surface as the correspondences for each medial primitive, we present a correspondence-based approximation method equipped with an As-Rigid-As-Possible (ARAP) deformation energy defined on medial primitives. As a result, our algorithm produces DMAT with consistent connectivity across the whole sequence, accurately approximating the input animated surfaces.
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    Superpixel Generation by Agglomerative Clustering With Quadratic Error Minimization
    (© 2019 The Eurographics Association and John Wiley & Sons Ltd., 2019) Dong, Xiao; Chen, Zhonggui; Yao, Junfeng; Guo, Xiaohu; Chen, Min and Benes, Bedrich
    Superpixel segmentation is a popular image pre‐processing technique in many computer vision applications. In this paper, we present a novel superpixel generation algorithm by agglomerative clustering with quadratic error minimization. We use a quadratic error metric (QEM) to measure the difference of spatial compactness and colour homogeneity between superpixels. Based on the quadratic function, we propose a bottom‐up greedy clustering algorithm to obtain higher quality superpixel segmentation. There are two steps in our algorithm: merging and swapping. First, we calculate the merging cost of two superpixels and iteratively merge the pair with the minimum cost until the termination condition is satisfied. Then, we optimize the boundary of superpixels by swapping pixels according to their swapping cost to improve the compactness. Due to the quadratic nature of the energy function, each of these atomic operations has only (1) time complexity. We compare the new method with other state‐of‐the‐art superpixel generation algorithms on two datasets, and our algorithm demonstrates superior performance.Superpixel segmentation is a popular image pre‐processing technique in many computer vision applications. In this paper, we present a novel superpixel generation algorithm by agglomerative clustering with quadratic error minimization. We use a quadratic error metric (QEM) to measure the difference of spatial compactness and colour homogeneity between superpixels. Based on the quadratic function, we propose a bottom‐up greedy clustering algorithm to obtain higher quality superpixel segmentation. There are two steps in our algorithm: merging and swapping. First, we calculate the merging cost of two superpixels and iteratively merge the pair with the minimum cost until the termination condition is satisfied. Then, we optimize the boundary of superpixels by swapping pixels according to their swapping cost to improve the compactness. Due to the quadratic nature of the energy function, each of these atomic operations has only O(1) time complexity. We compare the new method with other state‐of‐the‐art superpixel generation algorithms on two datasets, and our algorithm demonstrates superior performance.