Name
Abstract | ||
|---|---|---|
The digital image segmentation algorithm based on deep learning plays an important role in the monitoring of seabed mineral resources. The traditional segmentation algorithm has insufficient performance in the face of adhesion, and the segmentation boundary is fuzzy. For this reason, an improved segmentation algorithm by learning a deep convolution network is proposed. A typical encoder-decoder structure is used to construct the network model, and the decoder part is up-sampled at different scales to obtain the final segmentation map. The performance of the algorithm is tested on the gray scale electron microscopy (EM) image dataset and the seabed mineral image dataset. The experimental shows that the Rand theoretic score can achieve 0.916 on EM image dataset, and a better segmentation result on the seabed mineral image dataset than the original U-net Convolutional Network. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2923753 | IEEE ACCESS |
Keywords | Field | DocType |
Image segmentation,deep learning,convolutional networks,EM images,seabed mineral images | Pattern recognition,Convolution,Computer science,Segmentation,Convolutional neural network,Digital image,Image segmentation,Artificial intelligence,Deep learning,Cluster analysis,Grayscale,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Song | 1 | 0 | 1.01 |
| Nan Zheng | 2 | 11 | 4.24 |
| Xiangchun Liu | 3 | 0 | 0.34 |
| Lirong Qiu | 4 | 60 | 12.70 |
| Rui Zheng | 5 | 106 | 16.04 |