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The purpose of this chapter is to describe methods that can locate and recognize particular objects. Imagine an image containing a number of coins and faced with the question of designing an algorithm that can find all the big coins in the image. The approach is to first binarize the image using a method from a previous chapter. Each object (coin) in the binary image is now defined as a group of connected white pixels, a so-called BLOB. This process is known as BLOB extraction and a grass-fire inspired algorithm for this purpose is described in this chapter. The next step is to extract a number of characteristics, denoted features, for each BLOB. In the case of the coin, the relevant features would be size, roundness, and center of gravity. Lastly the features of each BLOB are compared with the features of a prototype model of the object the system is looking for (big coin) and a classification is performed.
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Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, New York (2001).
- BLOB Analysis
Thomas B. Moeslund
- Springer London
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