@article{scholars10890,
           title = {Image-Based Technique for Turbulent Flow Segmentation},
          volume = {488},
            note = {cited By 0; Conference of 4th International Conference on Computational Science and Technology, ICCST17 ; Conference Date: 29 November 2017 Through 30 November 2017; Conference Code:211169},
             doi = {10.1007/978-981-10-8276-4{$_1$}{$_2$}},
         journal = {Lecture Notes in Electrical Engineering},
       publisher = {Springer Verlag},
           pages = {119--129},
            year = {2018},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043319435&doi=10.1007\%2f978-981-10-8276-4\%5f12&partnerID=40&md5=72941a8f56a6e247b3d19d0aa8d27917},
        keywords = {Image enhancement; Nozzles; Reynolds number; Turbulent flow; Video cameras, Buoyant jets; Complex flow; Flow regions; Image-based techniques; Jet penetration; Penetration area; Segmentation techniques; Turbulent jet, Image segmentation},
        abstract = {Turbulent flow segmentation from image data is a challenging problem. This is due to the un-defined edge and the complex flow nature of turbulence. In this paper, an image-based technique is proposed for turbulent flow segmentation from image. The proposed technique segments the flow region based on enhancing the input image intensity at flow edges and by defining a thresholding value to differentiate between flow region and image background. To test the image-based segmentation technique, a turbulent buoyant jet was experimentally simulated at different nozzle flow rates which have a Reynolds numbers of 960, 1560, and 3210. Then, a video camera was used to record the jet flow data. Then, the image-based technique was applied to segment the flow region and estimate the jet penetration area. As a result, the turbulent flow region was segmented well for all cases of nozzle flow rates. Moreover, application of the image-based technique for jet penetration estimation showed a good agreement with the previous work, in which the jet propagated linearly over time. {\^A}{\copyright} 2018, Springer Nature Singapore Pte Ltd.},
          author = {Osman, A. B. and Ovinis, M. and Faye, I. and Hashim, F. M.},
            issn = {18761100},
            isbn = {9789811082757}
}