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The concept of Machine Vision (MV) originates in the early 1950’s and practical MV applications appeared in the early 1980’s. Early theoretical work suggested serious limitations in computational abilities as the main reason for inefficient use of MV. MV is a ‘simple’ processing system which receives and combines signals from cameras by manipulating images at the pixel level to extract information that can be used in a decision making activity to produce the output required. MV has several advantages over systems utilising conventional technologies. The attention span of human operators is relatively short (Butler, 1980). Camera images are detailed and therefore contain a large amount of information. This combined with the computer power available provides a huge potential for MV applications. The chapter will provide an overview on the developments and historical evolution of the concept of MV applications. This chapter will concentrate on MV application to automatic detection of objects with ill defined shape, size, and colour with high variability. Objects that change with time have no fixed structure; can present extra problems on application of automatic detection using MV. For this type of application, current manual detection requires highly specialised and highly trained operators. The application of MV will facilitate the detection of these objects with the advantage of fast response. It is easy to use, cost effective with consistent and reliable results. The detection of micro-organisms and the detection of suspicious activity in humans fall into this category. The first example examines development of an automatic system for microscopic examination of the recovered deposit for the detection and enumeration of the microorganism Cryptosporidium. The second example addresses the application of MV to the task of intruder monitoring within the context of visual security systems. The chapter will present these two applications to illustrate problems encountered in this type of detection.

Machine Vision Application to Automatic Detection of Living Cells/Objects