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We also notice noise or variations in the 3 We set up a camera, take some sample images and begin to note some physical diﬀerences between the two types of ﬁsh - length, lightness, width, number and shape of ﬁns, position of the mouth, and so on - and these suggest features to explore for use in our classiﬁer.

As a pilot project it is decided to try to separate sea bass from salmon using optical sensing. Suppose that a ﬁsh packing plant wants to automate the process of sorting incoming ﬁsh on a conveyor belt according to species. To illustrate the complexity of some of the types of problems involved, let us consider the following imaginary and somewhat fanciful example. For some applications, such as speech and visual recognition, our design eﬀorts may in fact be inﬂuenced by knowledge of how these are solved in nature, both in the algorithms we employ and the design of special purpose hardware. Moreover, in solving the myriad problems required to build such systems, we gain deeper understanding and appreciation for pattern recognition systems in the natural world - most particularly in humans. From automated speech recognition, ﬁngerprint identiﬁcation, optical character recognition, DNA sequence identiﬁcation and much more, it is clear that reliable, accurate pattern recognition by machine would be immensely useful. It is natural that we should seek to design and build machines that can recognize patterns.

Pattern recognition - the act of taking in raw data and taking an action based on the “category” of the pattern - has been crucial for our survival, and over the past tens of millions of years we have evolved highly sophisticated neural and cognitive systems for such tasks. With which we recognize a face, understand spoken words, read handwritT hetenease characters, identify our car keys in our pocket by feel, and decide whether an apple is ripe by its smell belies the astoundingly complex processes that underlie these acts of pattern recognition. 1.3 The Sub-problems of Pattern Classiﬁcation 1.3.1 Feature Extraction.

Contents 1 Introduction 1.1 Machine Perception.

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