Computer vision has been regarded as one of the most complex and computationally intensive -problems. An integrated vision system (lVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g, object recognition). An IVS normally A multiprocessor architecture for IVSs (called NETRA) is presented. NETRA is highly flexible without the use of complex interconnection schemes. NETRA is recursively defined hierarchical architecture whose leaf nodes consist of clusters processors connected with a programmable crossbar with a selective broadcast capability. Hence, it is easily scalable from small to large systems. Homogeneity of NETRA permits fault tolerance and graceful degradation under faults. Several refinements in the architecture over the original design are also proposed.Performance of several vision algorithms when they are mapped on one cluster is presented. It is shown that SIMD, MIMD and systolic algorithms can be easily mapped onto processor clusters, and almost linear speedups are possible. For some algorithms, analytical performance results are compared with those obtained using an implementation. It is observed that the analysis is very accurate.An extensive analysis of inter-cluster communication strategies in NETRA is presented. A methodology to evaluate performance of algorithms on NETRA is described. Performance analysis of parallel algorithms when mapped across clusters is presented. The parameters are derived from the characteristics of the parallel algorithms, which are then, used to evaluate the alternative communication strategies in NETRA. The effects of communication interference on the performance of algorithms are studied. It is observed that if communication speeds are matched with the computation speeds, almost linear speedups are possible when algorithms are mapped across clusters.Finally, several techniques to perform data decomposition, and static and dynamic load balancing for IVS afgorithms are described. These techniques can be used to perform load balancing for intermediate and high level, data dependent vision algorithms. These techniques are novel because they use knowledge about the data when it is produced and use knowledge about the computation in the next task to perform load balancing in an integrated environment. They are shown to perform well, using them on an implementation of a motion estimation system on a hypercube mUltiprocessor. attempting to define and solve pans of the problems for many years. However, to say that computer vision is in its infancy today is a correct judgment of the state of the an in artificial vision. Furthermore, nobody knows the answer to the question of whether it is possible to make artificial vision as powerful and general as human vision. One of the many reasons for not knowing the answer is that little is understood about human vision itself.
LIST OF TABLESThere are several approaches to tackling the computational problems in computer vision. One of the approaches, which is also th...
