Classification of rotifers with machine vision by shape moment invariants

An automated system for the identification of rotifers under a microscope w ith 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 fr om sludge in the degraded water or from rotifer carcasses. Two stages of a discrimination model based on shape analysis were built: one: was to separa te debris from rotifers,and the other was to classify rotifers into three g roups. 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 a pprox. 92%. The results from different classification approaches were also compared. The machine vision system with shape analysis and the 2-stage dis crimination model had a greater effect on the reduction of manpower require ment for the classification of rotifers. (C) 2000 Elsevier Science B.V. All rights reserved.