BODY PART SEGMENTATION OF NOISY HUMAN SILHOUETTE IMAGES

In this paper we propose a solution to the problem of body part segmentation in noisy silhouette images. In developing this solution we revisit the issue of insufficient labeled training data, by investigating how synthetically generated data can be used to train general statistical models for shape classification. In our proposed solution we produce sequences of synthetically generated images, using three dimensional rendering and motion capture information. Each image in these sequences is labeled automatically as it is generated and this labeling is based on the hand labeling of a single initial image. We use shape context features and Hidden Markov Models trained based on this labeled synthetic data. This model is then used to segment silhouettes into four body parts; arms, legs, body and head. Importantly, in all the experiments we conducted the same model is employed with no modification of any parameters after initial training.