dc.contributor.author | Mohd Yusof, Mashor, Prof. Dr. | |
dc.contributor.author | M. K., Osman | |
dc.contributor.author | R., Arshad | |
dc.date.accessioned | 2010-11-01T01:43:09Z | |
dc.date.available | 2010-11-01T01:43:09Z | |
dc.date.issued | 2007-08 | |
dc.identifier.citation | p.8-9 | en_US |
dc.identifier.issn | 1823-9633 | |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/10134 | |
dc.description.abstract | Computer vision recognition
typically involves sensors, a model
database containing information
about the object representations and
decision making or categorization.
Sensors gather images that are
processed to represent it as the
database models and a recognition
algorithm is applied to find the
model to which the object best
matches, known as model-based
object recognition system. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Perlis (UniMAP) | en_US |
dc.relation.ispartofseries | Explore | en_US |
dc.relation.ispartofseries | August 2007 | en_US |
dc.subject | Explore -- Penerbitan universiti | en_US |
dc.subject | UniMAP -- Publications | en_US |
dc.subject | UniMAP -- Research and development | en_US |
dc.subject | 3D objects | en_US |
dc.subject | Recognition system | en_US |
dc.title | 3D object recognition: Views, moment invariants & perceptron networks | en_US |
dc.type | Article | en_US |
dc.publisher.department | Pejabat Timbalan Naib Canselor (Penyelidikan dan Inovasi) | en_US |