Water turbidity estimation in water sampled images
Imen Montassar, Amel Benazza‐Benyahia · 2020
This paper tackles the problem of estimating water turbidity by analyzing images. This computer-vision solution avoids to resort to use specific laboratory instruments and, hence facilitates the water characterization in situ. Our contribution consists in designing a whole image processing chain composed of pre-processing, segmentation, feature extraction and classification modules. The second originality of our work relies on comparing two dual approaches for the segmentation and feature extraction: handcrafted and deep neural network based approaches. Finally, the lack of a publicly available dataset has motivated the building of an appropriate dataset. Experimental results indicate satisfactory performances of the proposed approaches.