Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

Geometric Aspects of Visual Object Recognition

This thesis presents there important results in visual object recognition based on shape. (1) A new algorithm (RAST; Recognition by Adaptive Sudivisions of Tranformation space) is presented that has lower average-case complexity than any known recognition algorithm. (2) It is shown, both theoretically and empirically, that representing 3D objects as collections of 2D views (the "View-Based Approximation") is feasible and affects the reliability of 3D recognition systems no more than other commonly made approximations. (3) The problem of recognition in cluttered scenes is considered from a Bayesian perspective; the commonly-used "bounded-error errorsmeasure" is demonstrated to correspond to an independence assumption. It is shown that by modeling the statistical properties of real-scenes better, objects can be recognized more reliably.

Memory Usage Inference for Object-Oriented Programs

We present a type-based approach to statically derive symbolic closed-form formulae that characterize the bounds of heap memory usages of programs written in object-oriented languages. Given a program with size and alias annotations, our inference system will compute the amount of memory required by the methods to execute successfully as well as the amount of memory released when methods return. The obtained analysis results are useful for networked devices with limited computational resources as well as embedded software.

Review of bio-particle manipulation using dielectrophoresis

During the last decade, large and costly instruments are being replaced by system based on microfluidic devices. Microfluidic devices hold the promise of combining a small analytical laboratory onto a chip-sized substrate to identify, immobilize, separate, and purify cells, bio-molecules, toxins, and other chemical and biological materials. Compared to conventional instruments, microfluidic devices would perform these tasks faster with higher sensitivity and efficiency, and greater affordability. Dielectrophoresis is one of the enabling technologies for these devices. It exploits the differences in particle dielectric properties to allow manipulation and characterization of particles suspended in a fluidic medium. Particles can be trapped or moved between regions of high or low electric fields due to the polarization effects in non-uniform electric fields. By varying the applied electric field frequency, the magnitude and direction of the dielectrophoretic force on the particle can be controlled. Dielectrophoresis has been successfully demonstrated in the separation, transportation, trapping, and sorting of various biological particles.