EVALUATING STATISTICAL SHAPE MODELS FOR AUTOMATIC LANDMARK GENERATION ON A CLASS OF HUMAN HANDS

摘要

In this article, we present an evaluation of the application of statistical shape models for automatic landmark generation from a training set of deformable shapes and in particular, from a class of human hands models. The human hand is a dynamic object with considerable changes over time and variations in pose. A human being can easily recognize a hand despite its variations (e.g. skin tone, accessories, etc.) and put it in the context of an entire person. It is a visual task that human beings can do effortlessly, but in computer vision, this task is a complicated one. While a number of different techniques have been proposed, ranging from simple edge-detection algorithms to neural networks and statistical approaches, the development of a robust hand extraction algorithm is still a difficult task in computer vision. Human hand extraction is the first step in hand recognition systems, with the purpose of localizing and extracting the hand region from a complex and unprepared environment. This paper presents work in progress toward the segmentation and automatic identification of a set of landmark points. The landmarks are used to train statistical shape models known as Point Distribution Models (PDMs). Our goal is to enable automatic landmark identification using a context free approach of human hands' grey-scale still images held in a database. Our method is a combination of previously applied methods in shape recognition. In this paper we describe the motivation of our work, the results of our method applied on still images of examples of human hands and the extension of the method for building Active Appearance Model (AAM) using automatically extracted data for the recognition of deformable models in augmented reality systems.