Muhammad Rafiqul Islam

Computing and Mathematics

https://orcid.org/0000-0003-1760-1577

Emailmuislam@csu.edu.au

Source: Scopus
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Currently Involved in research at Charles Sturt University.

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14 Citations
1 h-Index
4 Conference paper

Rain streak removal with well-recovered moving objects from video sequences using photometric correlation
Islam, M. R., Paul, M. & Antolovich, M., 2020, Pacific-Rim Symposium on Image and Video Technology. Dabrowski, J. J., Rahman, A. & Paul, M. (eds.). 1 ed. Cham, Switzerland: Springer Nature Switzerland AG, p. 3-13 11 p. (Lecture Notes in Computer Science).
Research output: Book chapter/Published conference paper › Conference paper › peer-review
Rain streak removal from video sequence using spatiotemporal appearance
Islam, M. R. & Paul, M., Dec 2019, 2019 Digital Image Computing: Techniques and Applications: DICTA 2019. USA: IEEE, Institute of Electrical and Electronics Engineers, p. 1-7 7 p. 8946080
Research output: Book chapter/Published conference paper › Conference paper › peer-review
1 Citation (Scopus)
Sports highlights generation using decomposed audio information
Islam, M. R., Paul, M., Antolovich, M. & Kabir, A., 2019, IEEE International Conference on Multimedia and Expo. United States: IEEE Xplore, p. 579-584 6 p.
Research output: Book chapter/Published conference paper › Conference paper › peer-review
Detecting depression using K-Nearest Neighbors (KNN) classification technique
Islam, M. R., Kamal, A. R. M., Sultana, N., Islam, R., Moni, M. A. & Ulhaq, A., 20 Sep 2018, 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018. IEEE Xplore, 4 p. 394
Research output: Book chapter/Published conference paper › Conference paper › peer-review

13 Citations (Scopus)

Personal profile

Rain streak removal with well-recovered moving objects from video sequences using photometric correlation

Rain streak removal from video sequence using spatiotemporal appearance

Sports highlights generation using decomposed audio information

Detecting depression using K-Nearest Neighbors (KNN) classification technique