Encyclopedia of Computer Science and Technology

pattern recognition 363Turbo Pascal. This compiler used direct compilation ratherthan P-Code, sacrificing portability for speed and efficiency.It included an integrated programming environment thatmade development much cheaper and easier than withexisting “bulky” and expensive compilers such as thosefrom Microsoft. Turbo became very popular and eventuallyincluded language extensions that supported objectorientedprogramming. But Pascal became best known inits role as a first language for teaching programming and forexpressing algorithms.However, by 1990 the tide had clearly turned in favorof C and C++. These languages used a more cryptic syntaxthan Pascal and lacked the latter’s rigorous data typingmechanism. Systems programmers in particular preferredC’s ability to get “close to the machine” and manipulatememory directly without being confined by type definitions.C had also received a big boost because its developerswere also among the key developers of UNIX, a very popularoperating system in campus computing environments.During the 1990s, C, C++, and Java even began to supplantPascal for computer science instruction. Nevertheless,by encouraging structured programming concepts and helpingeducate a generation of computer scientists, Pascal madea lasting impact on the computer field. Wirth continued hiswork with the development of Modula-2 and Oberon, whichwere confined mainly to the academic world. However, Pascalalso was a major influence on the development of Ada, alanguage endorsed by the U.S. federal government that combinesstructured programming with object-oriented featuresand the ability to manage extensive packages of routines(see Ada).Further ReadingFree Online Pascal and Delphi Tutorials and Documentation.Available online. URL: http://www.thefreecountry.com/documentation/onlinepascal.shtml. Accessed August 17, 2007.Free Pascal Compiler. http://www.freepascal.org/Jensen, Kathleen, Niklaus Wirth, and A. Mickel. Pascal UserManual and Report: ISO Pascal Standard. 4th ed. New York:Springer-Verlag, 1991.Koffman, Elliot B. Turbo Pascal. 5th update ed. Reading, Mass.:Addison-Wesley, 1997.Rachele, Warren. Learn Object Pascal with Delphi. Plano, Tex.:Wordware Publishing, 2000.Wirth, Niklaus. Programming in Modula-2. 3rd, corr. ed. New York:Springer-Verlag, 1985.pattern recognitionAfter many years of effort researchers have been able tocreate systems that can recognize particular human faces(see computer vision). On the other hand, any normalsix-month-old child can effortlessly recognize familiarfaces (such as parents). The fundamental task of turningraw data (whether from senses, instruments, or computerfiles) into recognizable objects or drawing inferences iscalled pattern recognition. Pattern recognition is at theheart of many areas of research and application in computing(see artificial intelligence and data mining).Despite the challenge in getting machines to do whatcomes naturally for biological organisms, the potentialpayoffs are immense.A pattern-recognition system begins with data, whetherstored or real-time (such as from a robot’s camera). The firsttask in turning potentially billions of bytes of data intomeaningful objects is to extract features from what is likelya high proportion of redundant or irrelevant data. (Withvisual images, this often involves finding edges that defineshapes.) The extracted features are then classified to determinewhat objects they might represent. This can be doneby comparing structures to templates or previously classifieddata or by applying statistical analysis to determine thelikely correlation of the new data to existing patterns (seeBayesian analysis).Pattern recognition often includes learning algorithmsas well; indeed, the field is often considered to be a subtopicof machine learning. For example, classification systemscan be refined by “training” them and reinforcing successfuldeterminations (see neural network).ApplicationsThere are numerous applications of pattern recognition,often as part of intelligent systems used in such areas as linguistics(see language translation software), communications,intelligence and surveillance, identity verification(see biometrics), and the analysis of credit card transactionpatterns for signs of fraud. Some examples are shown in thefollowing table:Data Procedures Resultsspeech phonemes, transition textruleshandwritten character classification identifiedaddresspostal addresshandwritten character classification identify amount ofcheck (ATM)depositgeneral text grammar and syntax structure andmeaninge-mail identify characteristics spam detectionBayesian filterfacial image feature templates, identified personstatisticsbiometric feature extraction and verified identity(retina, finger- template comparisonprint, etc.)Further ReadingBishop. Christopher M. Pattern Recognition and Machine Learning.New York: Springer, 2006.Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern Classification.New York: Wiley, 2001.International Association of Pattern Recognition. Available online.URL: http://www.iapr.org/. Accessed November 3, 2007.Pattern Recognition. American Association for Artificial Intelligence.Available online. URL: http://www.aaai.org/AITopics/html/pattern.html. Accessed November 4, 2007.Recognition Technology and Pattern Analysis. Available online.URL: http://alumnus.caltech.edu/~dave/pattern.html. AccessedNovember 4, 2007.