Module Overview

Aims apply your programming skills to an applied study of Digital Image Processing, Digital Signal Processing and Neural Networks investigate commercial image and speech processing systems facilitate research of the subject and prepare you for postgraduate study develop the mathematical skills required to understand and apply the concepts of digital processing of images and signals

Assessment One assignment (2 nd half of sem 1) Assignment part research part implementation Assignment weighted at 30% 3 hour exam weighted at 70%

Engineers and scientists would normally use Matlab – but Software Engineers need to be able to write the code (someone wrote Matlab). Done mainly in lab and assignment Top-down approach: requested by previous years’ students One research / practical based assignment and one 3 hour exam 2 hours per week lec / lab / tut as required

Objective To provide an overview of the operation of novel input / output technologies, underpinned with some theory, then applied by programming Some maths is needed (not much) and will be dealt with as and when required

Some input technologies Vision –Retinal scan –Eye trackers –Image scanning / photo Limbs –Fingerprint recognition –Gesture interpretation –Lip reading –Handwriting Voice –Speech recognition –Voice identification Others… ?

Some output technologies Audible –Speech synthesis –Spatial awareness Visual –Graphical display Others…?

General systems We live in an analogue world so generally need to convert from analogue to digital for input and digital to analogue for output During or immediately after capturing the input, systems generally perform some form of pre-processing or signal conditioning (e.g. digital filtering) to get the data into a workable format Often, it is features or characteristics that are required, particularly in recognition and verification systems so a feature extraction section often follows the pre-processor (often work in the frequency domain so may require FFT etc) Features are likely to be fed into a recognition or identification module (e.g. hidden markov model or neural network). These are generally imprecise so need to use a thresholder (a confidence level to determine whether the reliability of the result) Recognition / verification needs to be packaged in an acceptable form to output (e.g. D to A conversion, PWM, text)

We will look at the basic operation of some of these technologies For input devices, we will look in more detail at speech recognition as this is a typical recognition problem and image processing as this is typically necessary for visual input processing For output devices, we will look at the graphical and speech output A comparison of the technological solutions will be made with the physiological equivalents (e.g. eye and ear)