A multiple-SIMD architecture for image and tracking analysis

The computational requirements for real-time image based applications are such as to warrant the use of a parallel architecture. Commonly used parallel architectures conform to the classifications of Single Instruction Multiple Data (SIMD), or Multiple Instruction Multiple Data (MIMD). Each class of architecture has its advantages and dis-advantages. For example, SIMD architectures can be used on data-parallel problems, such as the processing of an image. Whereas MIMD architectures are more flexible and better suited to general purpose computing. Both types of processing are typically required for the analysis of the contents of an image. This thesis describes a novel massively parallel heterogeneous architecture, implemented as the Warwick Pyramid Machine. Both SIMD and MIMD processor types are combined within this architecture. Furthermore, the SIMD array is partitioned, into smaller SIMD sub-arrays, forming a Multiple-SIMD array. Thus, local data parallel, global data parallel, and control parallel processing are supported. After describing the present options available in the design of massively parallel machines and the nature of the image analysis problem, the architecture of the Warwick Pyramid Machine is described in some detail. The performance of this architecture is then analysed, both in terms of peak available computational power and in terms of representative applications in image analysis and numerical computation. Two tracking applications are also analysed to show the performance of this architecture. In addition, they illustrate the possible partitioning of applications between the SIMD and MIMD processor arrays. Load-balancing techniques are then described which have the potential to increase the utilisation of the Warwick Pyramid Machine at run-time. These include mapping techniques for image regions across the Multiple-SIMD arrays, and for the compression of sparse data. It is envisaged that these techniques may be found useful in other parallel systems

Parallel architectures for image analysis

This thesis is concerned with the problem of designing an architecture specifically for the application of image analysis and object recognition. Image analysis is a complex subject area that remains only partially defined and only partially solved. This makes the task of designing an architecture aimed at efficiently implementing image analysis and recognition algorithms a difficult one. Within this work a massively parallel heterogeneous architecture, the Warwick Pyramid Machine is described. This architecture consists of SIMD, MIMD and MSIMD modes of parallelism each directed at a different part of the problem. The performance of this architecture is analysed with respect to many tasks drawn from very different areas of the image analysis problem. These tasks include an efficient straight line extraction algorithm and a robust and novel geometric model based recognition system. The straight line extraction method is based on the local extraction of line segments using a Hough style algorithm followed by careful global matching and merging. The recognition system avoids quantising the pose space, hence overcoming many of the problems inherent with this class of methods and includes an analytical verification stage. Results and detailed implementations of both of these tasks are given