Manifold learning approach to curve identification with applications to footprint segmentation

Recognition of animals via images of their footprints is a non-invasive technique recently adopted by researchers interested in monitoring endangered species. One of the challenges that they face is the extraction of features from these images, which are required for this approach. These features are points along the boundary curve of the footprints. In this paper, we propose an innovative technique for extracting these curves from depth images. We formulate the problem of identification of the boundary of the footprint as a pattern recognition problem of a stochastic process over a manifold. This methodology has other applications on segmentation of biological tissue for medical applications and tracking of extreme weather patterns. The problem of pattern identification in the manifold is posed as a shortest path problem, where the path with the smallest cost is identified as the one with the highest likelihood to belong to the stochastic process. Our methodology is tested in a new dataset of normalized depth images of tiger footprints with ground truth selected by experts in the field.