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An Analysis of Strategies to Reduce Computational Complexity and Processing Time in Industrial Optical Data Processing and Analysis

In this paper we present a framework for the classification and analysis of optical and image processing and analysis techniques and architectures. This framework centres on the complexity analysis of such techniques,. embracing both the hardware which is used to sense, acquire, and pre-process images and software-based image processing and analysis. The key to this approach is the complexity analysis and, in particular, the inter-dependence of the time-complexity and the data-complexity. In addition, we treat attentive processes (which are context-dependent and cannot be predicted a priori) which are not strictly feed-forward and sequential but also exhibit re-entrant or recursive structure. In all cases, the number of data elements, n, which forms the argument of the process’s order of complexity function will be bounded by the data order of complexity of the process which is instantiated immediately prior to it in the optical data processing sequence (this applies in the case of both time and data complexity). In the case of attentive (or adaptive) processes, this bound is a function of the data order of complexity of a subsequent process and requires the use of a (calibrated) estimate of a looping parameter. Each time order of complexity can then be derived (and, equally, their upper-bound functions) and a minimum time function computed by re-introducing the constant term (which is architecture dependent) back into the expression for the time and data complexities.