A computer vision system consists of many stages, depending on its application. Feature extraction and segmentation are two key stages of a typical computer vision system and hence developments in feature extraction and segmentation are significant in improving the overall performance of a computer vision system. There are many inherent problems associated with feature extraction and segmentation processes of a computer vision system. In this thesis, I propose novel solutions to some of these problems in feature extraction and segmentation.

First, I explore manifold learning, which is a non-linear dimensionality reduction technique for feature extraction in high dimensional data. The classical manifold learning techniques perform dimensionality reduction assuming that original data lie on a single low dimensional manifold. However, in reality, data sets often consist of data belonging to multiple classes, which lie on their own manifolds. Thus, I propose a multi-manifold learning technique to simultaneously learn multiple manifolds present in a data set, which cannot be achieved through classical single manifold learning techniques.

Secondly, in image segmentation, when the number of segments of the image is not known, automatically determining the number of segments becomes a challenging problem. In this thesis, I propose an adaptive unsupervised image segmentation technique based on spatial and feature space Dirichlet tessellation as a solution to this problem. Skin segmentation is an important as well as a challenging problem in computer vision applications. Thus, thirdly, I propose a novel skin segmentation technique by combining the multi-manifold learning-based feature extraction and Vorono"{i} region-based image segmentation.

Finally, I explore hand gesture recognition, which is a prevalent topic in intelligent human computer interaction and demonstrate that the proposed improvements in the feature extraction and segmentation stages improve the overall recognition rates of the proposed hand gesture recognition framework. I use the proposed skin segmentation technique to segment the hand, the object of interest in hand gesture recognition and manifold learning for feature extraction to automatically extract the salient features. Furthermore, in this thesis, I show that different instances of the same dynamic hand gesture have similar underlying manifolds, which allows manifold-matching based hand gesture recognition.