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A new 3D segmentation approach using extreme learning machine algorithm and morphological operations
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DOI：10.1016/j.compeleceng.2020.106638.png)
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Abstract
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Segmentation is one of the most crucial steps of image processing. Because 3D images contain depth information, they have gradually gained importance for numerical systems in image analysis. In the present study, a new 3D segmentation method based on extreme learning machine and morphological operations (3DS-ELM) is proposed. The present study benefits from extreme learning machine (ELM) algorithm, which is a novel and fast learning algorithm for single-hidden layer feedforward networks (SLFNs), for training objects. Because a 3D model contains many points, direct segmentation on a 3D model is time-consuming and causes problems in the segmentation process, the proposed approach minimizes these problems and offers a quick and high-performance 3D segmentation method that can be used in various industrial fields. The proposed 3DS-ELM was compared with different approaches in order to analyze its 3D segmentation performance. Experimental studies proved that the proposed 3DS-ELM performed better than other approaches. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Extreme learning machine
Segmentation
3D segmentation
Artificial neural network
Fuzzy C-means
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