Landmarks Detection by Applying Deep Networks

Morphometric analysis is a general method applied to organisms and used to appreciate the covariances between the ecological factors and the organisms (shape, size, form,...) in which, landmark-based morphometry is known as one of the approaches to analyze the characteristics of organisms. Finding landmarks setting can give to biologists a comprehensive description of the organism. In this study, we propose a convolutional neural network (CNN) to predict the landmarks on beetle images. The network is designed as a pipeline of layers, it has been trained with a set of manually labeled landmarks dataset. Then, the network has been used to provide the morphometric landmarks on biological images automatically. The coordinates of predicted landmarks have been evaluated by computing their distance to the manual coordinates given by the biologists. Besides, the average of distance errors on each landmark has been also computed. The network model is implemented by Python on Lasagne framework.