Automatic recognition and measurement of butterfly eyespot patterns

A favorite wing pattern element in butterflies that has been the focus of intense study in evolutionary and developmental biology, as well as in behavioral ecology, is the eyespot. Because the pace of research on these bull’s eye patterns is accelerating we sought to develop a tool to automatically detect and measure butterfly eyespot patterns in digital images of the wings. We used a machine learning algorithm with features based on circularity and symmetry to detect eyespots on the images. The algorithm is first trained with examples from a database of images with two different labels (eyespot and non-eyespot), and subsequently is able to provide classification for a new image. After an eyespot is detected the radius measurements of its color rings are performed by a 1D Hough Transform which corresponds to histogramming. We trained software to recognize eyespot patterns of the nymphalid butterfly Bicyclus anynana but eyespots of other butterfly species were also successfully detected by the software.