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Region Labeling in Natural Scene Images
http://hdl.handle.net/20.500.12678/0000007727
http://hdl.handle.net/20.500.12678/0000007727a21f7998-ae50-447d-a4f9-71cde5544f1a
b83eadba-e936-4a74-9f17-d4481c3a4e12
Name / File | License | Actions |
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Region Labeling in Natural Scene Images.pdf (237 KB)
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Journal article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Region Labeling in Natural Scene Images | |||||
Language | en | |||||
Publication date | 2016-10-11 | |||||
Authors | ||||||
Kyawt Kyawt Htay | ||||||
Nyein Aye | ||||||
Description | ||||||
Semantic region labeling is important in the fields of low, mid and high level computer vision. This paper proposes an approach for region labeling in natural scene images using over-segmented regions as a basic unit. This system consists of three phases: segmentation, feature extraction and labeling. The paper uses Marker-controlled Watershed Segmentation (MCWS) algorithm for segmented regions generation. The aim of this system focuses labeling on nine semantic concept classes using color, texture features and 3-layer Feed Forward Neural Network (FFNN) classifier. The system performance is evaluated on the use of public MSRC 9class dataset. | ||||||
Keywords | ||||||
Marker-controlled Watershed Segmentation, Color, Texture, Feed Forward Neural Network (FFNN) | ||||||
Journal articles | ||||||
IEEE-Explore Digital Library | ||||||
2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) | ||||||
Pages 746-747 | ||||||
Volume 7 |