{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T02:44:14Z","timestamp":1649126654656},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[1997,9]]},"abstract":"<jats:p> In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG \u2014 Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene\u2013views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application of the FCRG classifier to the problem of learning 3D objects from 2D intensity images, the study of the usefulness of sparse depth data in regards to recognition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects. <\/jats:p>","DOI":"10.1142\/s021800149700041x","type":"journal-article","created":{"date-parts":[[2003,10,6]],"date-time":"2003-10-06T11:00:00Z","timestamp":1065438000000},"page":"909-931","source":"Crossref","is-referenced-by-count":8,"title":["Learning to Recognize 3D Objects using Sparse Depth and Intensity Information"],"prefix":"10.1142","volume":"11","author":[{"given":"Brendan","family":"MccCane","sequence":"first","affiliation":[{"name":"Department of Computer Science,  University of Otago, Dunedin, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terry","family":"Caelli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Curtin University of Technology, GPO Box U 1987 Perth,  W.A., Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"de Vel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, James Cook University of North Queensland, Townsville,  Qld, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021800149700041X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T02:14:01Z","timestamp":1565144041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S021800149700041X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1997,9]]},"references-count":0,"journal-issue":{"issue":"06","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1997,9]]}},"alternative-id":["10.1142\/S021800149700041X"],"URL":"https:\/\/doi.org\/10.1142\/s021800149700041x","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[1997,9]]}}}