Real-time Systems for Computer Vision Applications
Despite the remarkable advancement in processing speed of conventional computers, the processing power of these conventional computers do not satisfy the demand for high throughput rates of data-intensive computer vision applications. On the other hand, application specific integrated circuits (ASIC) can be designed to solve the processing speed concerns for any particular computer vision applications; however, these ASIC devices are not very flexible that they can be used to support a variety of computer vision algorithms. In this project, we attempt to design an massive parallel architecture that will combine the high processing speed of an ASIC device and the flexibility of a general purpose processor. The parallel architecture design can achieve much higher processing rates than conventional computers because they exploit inherent parallelism – the ability to carry out many operations simultaneously – in computer vision applications. A set of special SIMD instructions which especially take advantage of the inherent parallelism in the applications will be developed to support the massive parallel architecture. The introduction of the special instruction set would provide the flexibility to perform various image processing algorithms. The designed architecture is expected to process high resolution images (typical size of 2 Mpixels) in real-time (at least 30 frames per second). Some of the applications that would be supported by the designed architectures are developed within the same laboratory and those applications include image enhancement, skin extraction, face detection, face tracking and face recognition.
