Catfish feature identification via computer vision

dc.contributor.author	Jia, P	en
dc.contributor.author	Evans, MD	en
dc.contributor.author	Ghate, SR	en
dc.date.accessioned	2014-06-06T06:43:01Z
dc.date.available	2014-06-06T06:43:01Z
dc.date.issued	1996	en
dc.identifier.issn	00012351	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/957
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0030236427&partnerID=40&md5=613fb7843dc781b7617a5d09141b8017	en
dc.subject	Catfish processing	en
dc.subject	Computer vision	en
dc.subject	Fin fish processing	en
dc.subject	Image processing	en
dc.subject.other	Canny edge detection	en
dc.subject.other	Catfish feature identification	en
dc.subject.other	Fin fish processing	en
dc.subject.other	Algorithms	en
dc.subject.other	Automation	en
dc.subject.other	Computer vision	en
dc.subject.other	Edge detection	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image segmentation	en
dc.subject.other	Labeling	en
dc.subject.other	Mathematical morphology	en
dc.subject.other	Food processing	en
dc.subject.other	Ictalurus punctatus	en
dc.title	Catfish feature identification via computer vision	en
heal.type	journalArticle	en
heal.publicationDate	1996	en
heal.abstract	Computer vision algorithms for automated channel catfish (Ictalurus punctatus) processing were developed to: (1) detect the orientation of a catfish; (2) identify the head, tail, pectoral, ventral, and dorsal fins of a catfish; and (3) determine cutting lines for deheading, detailing, and definning (dorsal and ventral fins). The algorithms are invariant to translation, rotation, and scaling of a catfish and are robust to noise. They may be applied to most fin fish processing, and are not limited to catfish. Canny edge detection and a labeling and tracking algorithm were applied to locate the boundary of a catfish. A two-stage, model-based, catfish segmentation algorithm was proposed to locate each part of a catfish. A dominant point detection scheme was proposed and applied to find the points that connect each part of a catfish. Then morphological knowledge of the catfish was used to locate the feature points of each part of the catfish and to determine the cutting lines. The angle of the major axis and center of mass were used to represent the orientation of a catfish.Computer vision algorithms for automated channel catfish (Ictalurus punctatus) processing were developed to: (1) detect the orientation of a catfish; (2) identify the head, tail, pectoral, ventral, and dorsal fins of a catfish; and (3) determine cutting lines for deheading, detailing, and defining (dorsal and ventral fins). The algorithms are invariant to translation, rotation, and scaling of a catfish and are robust to noise. They may be applied to most fin fish processing, and are not limited to catfish. Canny edge detection and a labeling and tracking algorithm were applied to locate the boundary of a catfish. A two-stage, model-based, catfish segmentation algorithm was proposed to locate each part of a catfish. A dominant point detection scheme was proposed and applied to find the points that connect each part of a catfish. Then morphological knowledge of the catfish was used to locate the feature points of each part of the catfish and to determine the cutting lines. The angle of the major axis and center of mass were used to represent the orientation of a catfish.	en
heal.journalName	Transactions of the American Society of Agricultural Engineers	en
dc.identifier.issue	5	en
dc.identifier.volume	39	en
dc.identifier.spage	1923	en
dc.identifier.epage	1931	en