Deep Image Segmentation Using a Morphological Edge Operator

被引:0
|
作者
Zhang M. [1 ,2 ]
Xu B. [1 ]
Wen J. [1 ]
机构
[1] Department of Information Engineering, Information Institute, GUI Zhou University of Finance and Economics, Guiyang
[2] Guizhou Key Laboratory of Big Data Statistical Analysis (No. [2019]5103), Guiyang
关键词
CNN; depth image; edge extraction; image segmentation; Morphological edge operator; skeletonizing;
D O I
10.2174/2666255815666220513163140
中图分类号
学科分类号
摘要
Background: Segmentation of deep images is a difficult, persistent problem in the computer vision field. This paper aimed to address the defects of traditional segmentation methods with deep images, presenting a deep image segmentation algorithm based on a morphological edge operator. Methods: Deep image edge features were first extracted using three traditional edge operators; the edge and tip type jump edges were then extracted via a morphological edge operator, which was used to make the boundary connection; finally, to obtain more accurate segmentation results, skeletonizing was used to refine the image. Results: Compared with traditional segmentation algorithms, the improved algorithm obtained smooth and continuous boundaries, protected edge information from blurring, and was slightly more efficient. When Mickey Mouse depth images were used as experimental subjects, the computational time was reduced by 12.62 seconds; when rabbit depth images were used, computational time was reduced by 17.53 seconds. Conclusion: Morphological edge operator algorithm proposed in this paper is much more effective than traditional edge detection operators algorithms for deep image segmentation; it can clearly divide Mickey Mouse's ears, eyes, pupils, nose, and mouth. © 2023 Bentham Science Publishers.
引用
收藏
页码:96 / 102
页数:6
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