An image-processing algorithm, applied to images of common carp (Cyprinus c
arpio), St. Peter's fish (Oreochromis sp.) and grey mullet (Mugil cephalus)
, successfully discriminated among the species. Fish images were acquired w
hile they were swimming in an aquarium with their side to the camera. The a
lgorithm was based on the method of moment-invariants (MI) coupled with geo
metrical considerations and was, therefore, insensitive to fish size, two-d
imensional orientation and location in the camera's field of view. One hund
red and forty three images (47 grey mullet, 43 St. Peter's fish and 53 carp
images) were acquired and divided into two sets: 20 grey mullet, 20 St. Pe
ter's fish and 20 carp images in one set and the rest of the images in the
other set. Each of these two sets was used as a training set for selection
of feature thresholds, which were then applied to the other set as a test c
ase (two-fold cross-validation test). Fish species identification reached 1
00, 91 and 91% for grey mullet, carp and St. Peter's fish, respectively. To
the best of our knowledge this is the first report on successful discrimin
ation among fish species in vivo. We also report the results of a prelimina
ry experiment, conducted to test the capability of fish to be trained to sw
im through a narrow Plexiglas channel which could be part of a sorting syst
em, and through which fish images could possibly be acquired. (C) 2000 Else
vier Science B.V. All rights reserved.