start-ver=1.4
cd-journal=joma
no-vol=25
cd-vols=
no-issue=1
article-no=
start-page=1
end-page=13
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=1990
dt-pub=19901214
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=A Method of Cubic Object Feature Extraction
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=How to reduce and simplify the calculation for image recognition is a very attractive and important issue in order to realize the real time control of a robot based on the image recognition results. This paper describes a method of extracting 2 - dimensional geometrical features of cubic objects based on the normal vector distributions from the visual information obtained with the laser range finder to reduce the calculation of the image recognition. In this research a laser beam is scanned in the horizontal plane to which the cubic objects stand vertically and the laser spot is detected with a TV camera every sampling time. These spots make an intermittent locus which includes some special lines corresponding to the cubic objects. To extract the features of the cubic objects, we utilize the normal vectors formed on the locus. If some normal vectors distribute in the same direction and the origin of the normal vectors are very close to their neighbor's, these normal vectors can be classified into the same class, -the straight line class. Because the normal vectors on the neighbor surfaces of the cubic objects are vertical to each other, we use this property to determine the pair of straight lines which belong to the cubic objects. Making the histogram based on the normal vectors with the same direction, we obtain the peaks which are supported by the points on the cubic object surfaces. Then, the points can be extracted from the set of points on the whole locus inversely according to the relations with the peaks and the features of the cubic object can be extracted by applying method of least square to these extracted points. The experiments proved the availability of the proposed processing algorithm.
en-copyright=
kn-copyright=
en-aut-name=
en-aut-sei=
en-aut-mei=
kn-aut-name=GaoHong
kn-aut-sei=Gao
kn-aut-mei=Hong
aut-affil-num=1
ORCID=
en-aut-name=WadaTsutomu
en-aut-sei=Wada
en-aut-mei=Tsutomu
kn-aut-name=和田力
kn-aut-sei=和田
kn-aut-mei=力
aut-affil-num=2
ORCID=
en-aut-name=NoritsuguToshiro
en-aut-sei=Noritsugu
en-aut-mei=Toshiro
kn-aut-name=則次俊郎
kn-aut-sei=則次
kn-aut-mei=俊郎
aut-affil-num=3
ORCID=
affil-num=1
en-affil=
kn-affil=The Graduate School of Natural Science and Technology
affil-num=2
en-affil=
kn-affil=Department of Industrial and Mechanical Engineering
affil-num=3
en-affil=
kn-affil=The Graduate School of Natural Science and Technology
END