{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T06:54:13Z","timestamp":1648796053315},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[1987,8]]},"abstract":"<jats:p> When Pattern Recognition is aimed at human task automatization, such as the interpretation of visual data, it leads to Knowledge-Based Pattern Recognition (KBPR) systems. The design of a system, which contains a mixture of human knowledge and numerical data treatments, implies some tasks like Knowledge Acquisition (KA), dividing a problem into sub-problems or identifying different perceptual functions. Such problems cannot be solved by pure PR methods. Improvement of performances in KBPR systems is achieved through a better understanding of the cognitive processes which are involved during data interpretation and introspection which provides thinking aloud data. This paper will focus on problems concerned with knowledge as they are encountered in the design of expert systems for visual data interpretation. KA is considered as a necessary task for the system designer. Useful information is collected from the expert, however KA cannot provide a complete description of the human recognition process, directly usable for automatic numerical data interpretation. Verbal descriptions are lacking when introspection cannot reveal cognitive states such as those, for example, where perceptual adaptation is activated. The acquired knowledge consists of verbal descriptions as well as non-verbal knowledge provided by the analysis of the expert in action. The combination of acquired human knowledge and numerical data treatments is illustrated through examples where low level interpretation is achieved on task-adapted data representations. The need to design intermediate tools for making the dialogue more efficient or to define operational data representations is emphasized. <\/jats:p>","DOI":"10.1142\/s0218001487000138","type":"journal-article","created":{"date-parts":[[2004,11,29]],"date-time":"2004-11-29T02:14:37Z","timestamp":1101694477000},"page":"177-188","source":"Crossref","is-referenced-by-count":0,"title":["USING KNOWLEDGE IN SIGNAL INTERPRETATION"],"prefix":"10.1142","volume":"01","author":[{"given":"CLAUDIE","family":"FAURE","sequence":"first","affiliation":[{"name":"ENST\/UA CNRS 820, 46 rue Barrault, 75634 Paris Cedex 13, France"}]}],"member":"219","published-online":{"date-parts":[[2012,1,25]]},"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001487000138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T14:39:57Z","timestamp":1565188797000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001487000138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1987,8]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2012,1,25]]},"published-print":{"date-parts":[[1987,8]]}},"alternative-id":["10.1142\/S0218001487000138"],"URL":"https:\/\/doi.org\/10.1142\/s0218001487000138","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[1987,8]]}}}