Classification of rotifers with machine vision by shape moment invariants
Review articleOpen access
Abstract:

AbstractAn automated system for the identification of rotifers under a microscope with machine vision by shape analysis has been developed, which tends to be substituted for human appraisal. A suitable image recognition algorithm was proposed and the results were discussed in detail. In this study, rotifers were classified into the exact types despite the debris, which appeared from sludge in the degraded water or from rotifer carcasses. Two stages of a discrimination model based on shape analysis were built: one was to separate debris from rotifers, and the other was to classify rotifers into three groups. A set of shape descriptors, including geometry and moment features, was extracted from the images. The set of shape descriptors had to satisfy the RST (rotation, scaling, and translation) invariance. Shape analysis was proved to be an effective approach since the classification accuracy was approx. 92%. The results from different classification approaches were also compared. The machine vision system with shape analysis and the 2-stage discrimination model had a greater effect on the reduction of manpower requirement for the classification of rotifers.

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