{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T18:40:21Z","timestamp":1739990421761,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012]]},"abstract":"This paper presents a novel method for static gesture recognition based on three-dimensional chain codes which are computed from three-dimensional skeletons acquired by three-dimensional vision sensors, such as Microsoft KinectTM. The method has two stages: a digitization stage and a recognition stage. The digitization stage is based on the orthogonal direction change chain code, which represents the changes of direction on segments of a three-dimensional curve which was fitted in a three-dimensional grid; these changes of direction are invariant to rotation and translation. The recognition stage is based in the detection of dominant changes of direction on segments of a three-dimensional curve which was fitted in a three-dimensional grid. The experiments for testing this method of static gesture recognition involved recording the pose of the arms of a subject who was standing at increasing distances and the body was oriented in frontal and three-quarters angles; the poses were matched against a set of reference arm poses which were taken from a frontal body pose at an specific distance. The results show that the generation and matching of three-dimensional chain codes of arm poses captured on varying configurations are reliable enough for being used for gesture recognition. The gesture recognition system will be used on the robots of the Robocup@Home teams Markovito and Pumas from Mexico.<\/jats:p>","DOI":"10.3233\/978-1-61499-080-2-231","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T18:02:07Z","timestamp":1739988127000},"source":"Crossref","is-referenced-by-count":0,"title":["Recognition of Static Gestures using Three-Dimensional Chain Codes using Dominant Direction Vectors"],"prefix":"10.3233","author":[{"family":"Figueroa Jose","sequence":"additional","affiliation":[]},{"family":"Savage Jesus","sequence":"additional","affiliation":[]},{"family":"Bribiesca Ernesto","sequence":"additional","affiliation":[]},{"family":"Succar Enrique","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Ambient Intelligence and Smart Environments","Workshop Proceedings of the 8th International Conference on Intelligent Environments"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T18:13:35Z","timestamp":1739988815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=1875-4163&volume=13&spage=231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-080-2-231","relation":{},"ISSN":["1875-4163"],"issn-type":[{"value":"1875-4163","type":"print"}],"subject":[],"published":{"date-parts":[[2012]]}}}