关键词: 边缘检测, 二维局部均值分解, 多尺度图像分析, Delaunay三角剖分, 骨骼化

Abstract: For optimizing two crucial time-consuming processes in bidimensional local mean decomposition (BLMD): adaptive window-based search method and terminal criterion, this paper proposes two schemes, and then puts forward a new image edge detection algorithm based on this optimized bidimensional local mean decomposition. Delaunay triangulation is employed to get an ideal triangular mesh, then the local adjacent extrema and the moving average window size can be achieved from the mesh. Also, this paper presents a new convergence condition of iteration. Experiments on the synthetic and real images prove that these optimizations can make BLMD be much faster and have an identical or even better performance. Histogram equalization and binarization are applied to the first PF for achieving raw edge, then the disjoint or unconnected and undesired edges can be removed from binary image by a removal operation controlled by a threshold parameter, finally morphological thinning or skeleton operation is applied to get the final single-pixel width edge image. The proposed method is compared with several standard edge detection techniques, the experimental results show that the proposed method can successfully generate the desired edge map and has a better performance. In addition, benefitting from BLMD, the proposed method can relieve false edges produced by illumination and the final edge image is more consistent with the human visual inspection.

Key words: edge detection, bidimensional local mean decomposition, multiscale image analysis, Delaunay triangulation, skeleton