Singh R, Cherkassky V, Papanikolopoulos N
Artificial Intelligence, Robotics, and Vision Laboratory, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
IEEE Trans Neural Netw. 2000;11(1):241-8. doi: 10.1109/72.822527.
This paper presents a method for computing the skeleton of planar shapes and objects which exhibit sparseness (lack of connectivity), within their image regions. Such sparseness in images may occur due to poor lighting conditions, incorrect thresholding or image subsampling. Furthermore, in document image analysis, sparse shapes are characteristic of texts faded due to aging and/or poor ink quality. Due to the lack of pixel level connectivity, conventional skeletonization techniques perform poorly on such (sparse) shapes. Given the pixel distribution for a shape, the proposed method involves an iterative evolution of a piecewise-linear approximation of the shape skeleton by using a minimum spanning tree-based self-organizing map (SOM). By constraining the SOM to lie on the edges of the Delaunay triangulation of the shape distribution, the adjacency relationships between regions in the shape are detected and used in the evolution of the skeleton. The SOM, on convergence, gives the final skeletal shape. The skeletonization is invariant to Euclidean transformations. The potential of the method is demonstrated on a variety of sparse shapes from different application domains.
本文提出了一种计算平面形状和物体骨架的方法，这些形状和物体在其图像区域内呈现稀疏性（缺乏连通性）。图像中的这种稀疏性可能是由于光照条件差、阈值设置不正确或图像子采样导致的。此外，在文档图像分析中，稀疏形状是由于老化和/或墨水质量差而褪色的文本的特征。由于缺乏像素级连通性，传统的骨架化技术在处理此类（稀疏）形状时效果不佳。给定一个形状的像素分布，该方法通过使用基于最小生成树的自组织映射（SOM）对形状骨架的分段线性近似进行迭代演化。通过约束SOM位于形状分布的德劳内三角剖分的边上，检测形状中区域之间的邻接关系并将其用于骨架的演化。SOM收敛时给出最终的骨架形状。骨架化对欧几里得变换具有不变性。该方法的潜力在来自不同应用领域的各种稀疏形状上得到了证明。
