Object Contour Partition Model with Consistent Properties

Jinguang SUN; Tao LI; xiangjun DONG

doi:10.11999/JEIT200741

Volume 43 Issue 10

Oct. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(10): 2985-2992

Jinguang SUN, Tao LI, xiangjun DONG. Object Contour Partition Model with Consistent Properties[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2985-2992. doi: 10.11999/JEIT200741

Citation:

Jinguang SUN, Tao LI, xiangjun DONG. Object Contour Partition Model with Consistent Properties[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2985-2992. doi: 10.11999/JEIT200741

Jinguang SUN, Tao LI, xiangjun DONG. Object Contour Partition Model with Consistent Properties[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2985-2992. doi: 10.11999/JEIT200741

Citation:

Jinguang SUN, Tao LI, xiangjun DONG. Object Contour Partition Model with Consistent Properties[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2985-2992. doi: 10.11999/JEIT200741

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Object Contour Partition Model with Consistent Properties

doi: 10.11999/JEIT200741

Jinguang SUN^{1, 2
,
,},
Tao LI¹,
xiangjun DONG³

1.
Liaoning University of Engineering and Technology, Huludao 125105, China
2.
Liaoning Mine Safely Data Engineering Technology Research Center, Huludao 125105, China
3.
Qilu University of Technology(Shandong Academy of Sciences), Jinan 250353, China

Funds: The National Natural Science Foundation of China(61702241, 61602226)，The National Key R&D Program of China (2018YFB1402902, 2018YFB1403303)

Received Date: 2020-08-24
Rev Recd Date: 2021-03-15

Available Online: 2021-03-25

Publish Date: 2021-10-18

Abstract

Abstract

A new object contour partition model based on the fully convolutional network, combined with the idea of generative counter network and consistent attributes is proposed. Firstly, the image region partition network is used as a generator to divide the image region. Then the structural similarity is used as the reconstruction loss of regional division to supervise and guide model learning from the perspective of visual system. Finally, the global and local context discrimination networks are used as double-path similarity to supervise the reconstruction loss of regional division and guide model learning from the discriminators to distinguish the truth and falsity of the results of regional division, and a joint loss is proposed to train the supervision model in combination with the adversarial loss, so as to make the content of regional division true, natural and with attribute consistency. The instantaneity and effectiveness of the method are verified by living examples.
- Region division,
- Rock granularity analyze,
- Dilated convolution,
- Skip connection,
- Adversarial loss

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References(16)

References

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