Object Recognition with Pictorial Structures

This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images.

Geometric and Algebraic Aspects of 3D Affine and Projective Structures from Perspective 2D Views

We investigate the differences --- conceptually and algorithmically --- between affine and projective frameworks for the tasks of visual recognition and reconstruction from perspective views. It is shown that an affine invariant exists between any view and a fixed view chosen as a reference view. This implies that for tasks for which a reference view can be chosen, such as in alignment schemes for visual recognition, projective invariants are not really necessary. We then use the affine invariant to derive new algebraic connections between perspective views. It is shown that three perspective views of an object are connected by certain algebraic functions of image coordinates alone (no structure or camera geometry needs to be involved).

Stimuli-responsive Novel Amphiphilic Polymers for Chemical and Biomedical Applications

Amphiphilic polymers are a class of polymers that self-assemble into different types of microstructure, depending on the solvent environment and external stimuli. Self assembly structures can exist in many different forms, such as spherical micelles, rod-like micelles, bi-layers, vesicles, bi-continuous structure etc. Most biological systems are basically comprised of many of these organised structures arranged in an intelligent manner, which impart functions and life to the system. We have adopted the atom transfer radical polymerization (ATRP) technique to synthesize various types of block copolymer systems that self-assemble into different microstructure when subject to an external stimuli, such as pH or temperature. The systems that we have studied are: (1) pH responsive fullerene (C60) containing poly(methacrylic acid) (PMAA-b-C60); (2) pH and temperature responsive fullerene containing poly[2-(dimethylamino)ethyl methacrylate] (C₆₀-b-PDMAEMA); (3) other responsive water-soluble fullerene systems. By varying temperature, pH and salt concentration, different types microstructure can be produced. In the presence of inorganic salts, fractal patterns at nano- to microscopic dimension were observed for negatively charged PMAA-b-C60, while such structure was not observed for positively charged PDMAEMA-b-C60. We demonstrated that negatively charged fullerene containing polymeric systems can serve as excellent nano-templates for the controlled growth of inorganic crystals at the nano- to micrometer length scale and the possible mechanism was proposed. The physical properties and the characteristics of their self-assembly properties will be discussed, and their implications to chemical and biomedical applications will be highlighted.