A novel shape matching descriptor for real-time hand gesture recognition

Abstract: In object recognition applications in computer vision, feature information extractedfrom images is used to dierentiate between objects. One of the mostimportant features that can be extracted from an object is its shape. ShapeMatching is a subset of object recognition methodology and is an area that hasattracted a lot of research interest over the years. Shape matching has showngreat potential in scenarios where images lack texture and colour informationor cannot be trained by learning models of feature combinations. However, themost accurate shape matching algorithms found in literature, such as matchingwith Shape Context, tend to be computationally inecient having polynomialcomputational complexities. This thesis addresses the problem of real-timehand gesture recognition and investigates whether shape matching can providea robust and computationally ecient method for real-time hand gesture recognition.Several shape matching algorithms were investigated and a novel shapematching algorithm is proposed as the optimal solution for this problem.