Skirting Line Estimation Using Sparse to Dense Deformation

Automating the process of fleece contaminant removal has the potential to drastically improve the quality of wool leaving the farm gate. Towards this goal, we present a method to automatically extract skirting lines, i.e., the separations between clean and contaminated wool of a fleece using RGB images. We propose a learning-based sparse-to-dense approach for estimating the non-rigid deformation of fleeces in order to estimate the skirting lines. Our method is bootstrapped from a set of sparse inlier feature correspon-dences, which are heavily filtered through a set of strict criteria. The inlier correspondences are then greedily expanded by adding correspondences from a denser set through a filtering process. This process is based on a learning approach that takes as inputs the pixel similarity and the consistency with their inlier neighbours. Each greedy iteration is initialised with a non-rigid deformation using as-rigid-as-possible as a prior to the filtering process. The proposed method outperforms both a rigid deformation baseline and optic flow deep learning approach, as evidenced by the quantitative evaluation of pixel location error in controlled experiments. To further prove its practicality, we demonstrate qualitative results comparing the predicted skirting line from various methods on images of skirted fleeces collected from several wool sheds.