Doctor of Philosophy (Ph.D.), University of Dayton, 2017, Electrical and Computer Engineering

The texture of objects in digital images is an important property that has been utilized in many computer vision and image analysis applications, such as pattern recognition, object classification, and region segmentation. Despite its frequent usage and many attempts to describe it in general terms, the texture lacks a precise definition. This makes the development of new texture descriptors a big challenge. In addition, researchers interest has recently spread into the dynamic texture (video domain), where the problem becomes more challenging. The main goal of feature description and representation techniques is to extract features from the image that are distinct and stable under different conditions during the image acquisition process. Texture descriptors can be generally classified into structural and statistical approaches. The structural methods consider the texture as a repetition of some primitives, with a specific rule of placement, while the statistical techniques characterize the stochastic properties of the spatial distribution of gray levels in an image using the gray tone co-occurrence matrix. In this work, we propose a combination of the structural and statistical approaches that can be utilized to recognize a variety of different textures, named High Order Local Directional Pattern (HOLDP) for still image based feature extraction (static texture) as well as High Order Volumetric Directional Pattern (HOVDP) for video based feature extraction (dynamic texture). Recently, the conventional Local Directional Pattern (LDP) has received a great deal of attention in face recognition applications. However, it only describes the micro structures of the texture images because it considers only a small neighborhood size. In fact, our proposed HOLDP descriptor can capture more detailed discriminative information by not only extracting the micro structures but also the macro structures of the texture images, which can be done by the help of a pyramidal mu (open full item for complete abstract)

Committee: Vijayan Asari (Advisor); Russell Hardie (Committee Member); Eric Balster (Committee Member); Youssef Raffoul (Committee Member) Subjects: Electrical Engineering; Engineering