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Indexed by:期刊论文

Date of Publication:2017-01-01

Journal:JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

Included Journals:SCIE、EI、Scopus

Volume:33

Issue:6

Page Number:3397-3404

ISSN No.:1064-1246

Key Words:Semantic segmentation; disparity map; convolutional neural network

Abstract:In recent years, traditional machine learning algorithms have been gradually replaced by deep learning algorithms. In the field of computer vision, convolutional neural network is considered to be the most successful deep learning model. Based on convolutional neural network, the accuracy of image classification has been greatly improved. In this paper, a method for semantic image segmentation based on convolutional neural network is proposed. Firstly, the disparity map is introduced to improve the segmentation accuracy. To obtain the disparity map with more continuous disparity values, an image smoothing method is used to optimize the disparity map. Then, based on the AlexNet network, a fully convolutional network architecture is proposed for semantic image segmentation. The unpooling operation is employed to restore the extracted features to their original sizes. The experimental results demonstrate that the network can achieve high pixel-wise prediction accuracy and that using RGB-D image as the input of the network can reduce the noisy segmentation outputs.