Enabling Intelligent Vision Systems in a Configurable Multi-algorithm Pipeline

作     者：Cotter, Matthew Joseph 

作者单位：PennState University Libraries 

学位级别：Doctor of Philosophy

授予年度：2015年

主      题：Configurable Systems Vision Algorithms Intelligent Vision Hardware Accelerators 

摘      要：The machine vision community has expended tremendous effort in the research and development of algorithms in an effort to develop a system that is capable of seeing the world as humans do. These algorithms often focus on the accomplishment of specific tasks analogous to human vision such as scene awareness, object detection, object recognition, and object tracking. Joining forces with cognitive neuroscientists has steered much of this research towards the development of algorithms that not only accomplish the required tasks, but endeavor to do so in a biologically inspired fashion. Still, development and evaluation of these so-called neuromorphic algorithms is often done in isolation, with little regard given to the rest of the system necessary to make this human-like system a reality. This dissertation provides a framework for the current and future development of complex and highly integrated multi-algorithm vision systems. This framework not only enables the composition of such systems, but enables seamless development and integration of improved algorithmic modules. In addition to this high-level system composition framework, the Cerebrum tool, targeted at development of hardware-accelerated architectures is detailed in this work. This tool enables the creation of such hardware-based accelerators by researchers and engineers without specific or detailed knowledge of the target hardware *** addition to the framework and tools, this dissertation also details the analysis, development and evaluation of hardware accelerators for HMAX object recognition and AIM saliency detection. Armed with this intelligent framework and algorithmic accelerators, demonstrations of vision systems that leverage multiple algorithms are constructed and *** object classification, leveraging the benefits of Exemplar SVM and accelerated HMAX is shown to provide performance superior to either algorithm in isolation. Furthermore, a more complex system, targeting th