Using quantitative statistics for the construction of machine vision systems.

This paper describes a design methodology for constructing machine vision systems. Central to this is the use of empirical design techniques and in particular quantitative statistics. The approach views both the construction and evaluation of systems as one and is based upon what could be regarded as a set of self-evident propositions; Vision algorithms must deliver information allowing practical decisions regarding interpretation of an image. Probability is the only self-consistent computational framework for data analysis, and so must form the basis of all algorithmic analysis processes. The most effective and robust algorithms will be those that match most closely the statistical properties of the data. A statistically based algorithm which takes correct account of all available data will yield an optimal result. *. Machine vision research has not emphasised the need for (or necessary methods of) algorithm characterisation, which is unfortunate, as the subject cannot advance without a sound empirical base. In general this problem can be attributed to one of two factors; a poor understanding of the role of assumptions and statistics, and a lack of appreciation of what is to be done with the generated data. The methodology described here focuses on identifying the statistical characteristics of the data and matching these to the assuptions of the underlying techniques. The methodology has been developed from more than a decade of vision design and testing, which has culminated in the construction of the TINA open source image analysis/machine vision system.