{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T10:26:12Z","timestamp":1648895172349},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2007,2]]},"abstract":" A theory, called the Kinematic Theory of Rapid Human Movement, was proposed a few years ago to analyze rapid human movements, called the Kinematic Theory of Rapid Human Movements, based on a delta-lognormal equation that globally describes the basic properties of the velocity profiles of an end-effector using seven parameters. This realistic model has been very useful for proposing original solutions to various pattern recognition problems (signature segmentation and verification, handwriting analysis and synthesis, etc.). Most of these applications rely on the use of an efficient algorithm to extract the delta-lognormal parameters from real data with the best possible fit. In this paper, we compare two such algorithms: a deterministic one, based on nonlinear regression, and a Breeder Genetic algorithm. The performance of these two algorithms and of their combinations are compared using the same artificial database, composed of analytical delta-lognormal profiles and their noisy versions (20 dB SNR). In the free-noise case, the analysis of the experimental results shows that the deterministic approach leads to better results than the evolutionary one, while under the extremely noisy conditions selected, the evolutionary approach seems to be less sensitive to noise, but is nevertheless less successful than the deterministic search. <\/jats:p>","DOI":"10.1142\/s0218001407005284","type":"journal-article","created":{"date-parts":[[2007,2,27]],"date-time":"2007-02-27T13:47:09Z","timestamp":1172584029000},"page":"21-41","source":"Crossref","is-referenced-by-count":4,"title":["DETERMINISTIC AND EVOLUTIONARY EXTRACTION OF DELTA-LOGNORMAL PARAMETERS: PERFORMANCE COMPARISON"],"prefix":"10.1142","volume":"21","author":[{"given":"MOUSSA","family":"DJIOUA","sequence":"first","affiliation":[{"name":"D\u00e9partement de G\u00e9nie \u00c9lectrique, Laboratoire Scribens, \u00c9cole Polytechnique de Montr\u00e9al, P.O. Box 6079, Station Centre-Ville, Montr\u00e9al QC Canada H3C 3A7, Canada"}]},{"given":"R\u00c9JEAN","family":"PLAMONDON","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9nie \u00c9lectrique, Laboratoire Scribens, \u00c9cole Polytechnique de Montr\u00e9al, P.O. Box 6079, Station Centre-Ville, Montr\u00e9al QC Canada H3C 3A7, Canada"}]},{"given":"ANTONIO","family":"DELLA CIOPPA","sequence":"additional","affiliation":[{"name":"Natural Computation Laboratory, Dipartimento di Ingegneria dell'Informazione ed Ingegneria Elettrica, Universit\u00e0 di Salerno, Via Ponte don Melillo 1, 84084 Fisciano (SA), Italy"}]},{"given":"ANGELO","family":"MARCELLI","sequence":"additional","affiliation":[{"name":"Natural Computation Laboratory, Dipartimento di Ingegneria dell'Informazione ed Ingegneria Elettrica, Universit\u00e0 di Salerno, Via Ponte don Melillo 1, 84084 Fisciano (SA), Italy"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf4","doi-asserted-by":"crossref","unstructured":"C.\u00a0De Stefano, A.\u00a0Della Cioppa and A.\u00a0Marcelli, Pattern Recognition (Elsevier, Pergamon, 2002)\u00a0pp. 1025\u20131037.","DOI":"10.1016\/S0031-3203(01)00091-7"},{"key":"rf6","unstructured":"M.\u00a0Djioua, Advances in Graphonomics, eds. 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