Occluded Leaf Matching with Full Leaf Databases Using Explicit Occlusion Modelling

Matching an occluded contour with all the full contours in a database is an NP-hard problem. We present a suboptimal solution for this problem in this paper. We demonstrate the efficacy of our algorithm by matching partially occluded leaves with a database of full leaves. We smooth the leaf contours using a beta spline and then use the Discrete Contour Evaluation (DCE) algorithm to extract feature points. We then use subgraph matching, using the DCE points as graph nodes. This algorithm decomposes each closed contour into many open contours. We compute a number of similarity parameters for each open contour and the occluded contour. We perform an inverse similarity transform on the occluded contour. This allows the occluded contour and any open contour to be overlaid". We that compute the quality of matching for each such pair of open contours using the Frechet distance metric. We select the best eta matched contours. Since the Frechet distance metric is computationally cheap to compute but not always guaranteed to produce the best answer we then use an energy functional that always find best match among the best eta matches but is considerably more expensive to compute. The functional uses local and global curvature String Context descriptors and String Cut features. We minimize this energy functional using the well known GNCCP algorithm for the eta open contours yielding the best match. Experiments on a publicly available leaf image database shows that our method is both effective and efficient significantly outperforming other current state-of-the-art leaf matching methods when faced with leaf occlusion.