Computer Recognition Systems 3
We performpatternrecognitionallthe time inourdailylives,withoutalways being aware of it. We ?rstly observe the world around us by using all our senses(weextractfeaturesfromalargesetofdata).Wesubsequentlyperform pattern recognition by grouping together similar features and giving them a common label. We can identify similar, non-identical events or objects in an e?cient way. We can, for example, recognise whether complete strangers are smiling at us or not. This is a computationally demanding task, yet is seemingly trivial for humans. We can easily understand the meaning of printed texts even if the letters belong to a font that is new to us, so long as the new font is “similar” to ones we already know. Yet making machines responsive to “similarity notions” can be singularly problematic. Recognition is strongly linked with prediction: distinguishing between a smile and an angry face may be critical to our immediate future action. The same principle applies to driving in heavy tra?c or dealing with many social situations. The successful automation of recognition tasks is not only a major ch- lenge,it is inextricably linkedto the future of ourmodern world.Recognizing tra?c ?ow and tra?c behaviour (be it roadtra?c, air tra?c or internet tr- ?c)canleadtogreatere?ciencyandsafetyinnavigationgenerally.Recogn- ing biosignals(such asECG or EMG) anddiseasesase?ciently aspossible is critical for e?ective medical treatment. Modern warfare is not covered here, but its development in the 21st century will also depend critically on newer, faster, more robust recognition systems.
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