NDLI: Level set based shape prior and deep learning for image segmentation

Content Provider	IET Digital Library
Author	Han, Yongming Zhang, Shuheng Geng, Zhiqing Wei, Qin Ouyang, Zhi
Abstract	Deep convolutional neural network can effectively extract hidden patterns in images and learn realistic image priors from the training set. And fully convolutional networks (FCNs) have achieved state-of-the-art performance in the image segmentation. However, these methods have the disadvantages of noise, boundary roughness and no prior shape. Therefore, this study proposes a level set with the deep prior method for the image segmentation based on the priors learned by FCNs. The FCNs can learn high-level semantic patterns from the training set. Also, the output of the FCNs represents the high-level semantic information as a probability map and the global affine transformation can obtain the optimal affine transformation of the intrinsic prior shape. Moreover, the improved level set method integrates the information of the original image, the probability map and the corrected prior shape to achieve the image segmentation. Compared with the traditional level set method of simple scenes, the proposed method solves the disadvantage of FCNs by using the high-level semantic information to segment images of complex scenes. Finally, Portrait data set are used to verify the effectiveness of the proposed method. The experimental results show that the proposed method can obtain more accurate segmentation results than the traditional FCNs.
Starting Page	183
Ending Page	191
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6622
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Affine Transforms Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Corrected Prior Shape Deep Learning Deep Prior Method FCN Fully Convolutional Network High-level Semantic Information High-level Semantic Pattern Image Segmentation Improved Level Set Method Intrinsic Prior Shape Knowledge Engineering Technique Learning in AI Neural Computing Technique Optical, Image And Video Signal Processing Portrait Data Set Realistic Image Priors Segmented Image Specific Image Training Set
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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