Voting strategies for anatomical landmark localization using the implicit shape model

We address the problem of anatomical landmark localization using monocular camera information only. For person detection the Implicit Shape Model (ISM) is a well known method. Recently it was shown that the same local features that are used to detect persons, can be used to give rough estimates for anatomical landmark locations as well. Though the landmark localization accuracy of the original ISM is far away from being optimal. We show that a direct application of the ISM to the problem of landmark localization leads to poorly localized vote distributions. In this context, we propose three alternative voting strategies which include the use of a reference point, a simple observation vector filtering heuristic, and an observation vector weight learning algorithm. These strategies can be combined in order to further increase localization accuracy. An evaluation on the UMPM benchmark shows that these new voting strategies are able to generate compact and monotonically decreasing vote distributions, which are centered around the ground truth location of the landmarks. As a result, the ratio of correct votes can be increased from only 9.3% for the original ISM up to 42.1% if we combine all voting strategies.