Lihong Zheng

Personal profile

Currently Involved in research at Charles Sturt University.

Fingerprint Dive into the research topics where Lihong Zheng is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Blood vessels Engineering & Materials Science

Gesture recognition Engineering & Materials Science

Support vector machines Engineering & Materials Science

Image enhancement Engineering & Materials Science

Neural networks Engineering & Materials Science

Medium access control Engineering & Materials Science

Time series Engineering & Materials Science

Classifiers Engineering & Materials Science

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Research Output 2003 2019

274 Citations
10 h-Index
39 Conference paper
18 Article
2 Poster
1 Chapter

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Soomro, T., Afifi, A. J., Zheng, L., Soomro, S., Gao, J., Hellwich, O. & Paul, M., 03 Jun 2019, In : IEEE Access. 7, p. 71696-71717 22 p.

Research output: Contribution to journal › Article

Blood vessels

Image analysis

Ophthalmology

Computer vision

Artificial intelligence

Developing a mobile App to estimate grape volume and colour for harvest

Rahaman, M., Paul, M., Schmidtke, L., Zheng, L. & Oczkowski, A., Jul 2019.

Research output: Other contribution to conference › Poster

File

grapes

color

small fruits

lighting

artificial intelligence

1 Citation (Scopus)

Retinal blood vessels extraction of challenging images

Soomro, T. A., Gao, J., Lihong, Z., Afifi, A. J., Soomro, S. & Paul, M., 16 Feb 2019, Data Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers. Zhao, Y., Warwick, G., Stirling, D., Li, C-T., Koh, Y. S., Islam, R. & Islam, Z. (eds.). Springer-Verlag London Ltd., p. 347-359 13 p. (Communications in Computer and Information Science; vol. 996).

Research output: Book chapter/Published conference paper › Conference paper

Image enhancement

Blood Vessels

Blood vessels

Image Enhancement

Vessel

Sequential deep learning for action recognition with synthetic multi-view data from depth maps

Liang, B., Zheng, L. & Li, X., 2019, Data Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers. Zhao, Y., Warwick, G., Stirling, D., Li, C-T., Koh, Y. S., Islam, R. & Islam, Z. (eds.). Springer-Verlag London Ltd., p. 360-371 12 p. (Communications in Computer and Information Science; vol. 996).

Research output: Book chapter/Published conference paper › Conference paper

Action Recognition

Depth Map

Recurrent neural networks

Neural networks

Motion