TY - GEN
T1 - Modified dendrite morphological neural network applied to 3D object recognition
AU - Sossa, Humberto
AU - Guevara, Elizabeth
PY - 2013
Y1 - 2013
N2 - In this paper a modified dendrite morphological neural network (DMNN) is applied for recognition and classification of 3D objects. For feature extraction, the first two Hu's moment invariants are calculated based on 2D binary images, as well as the mean and the standard deviation obtained on 2D grayscale images. These four features were fed into a DMNN for classification of 3D objects. For testing, COIL-20 image database and a generated dataset were used. A comparative analysis of the proposed method with MLP and SVM is presented and the results reveal the advantages of the modified DMNN. An important characteristic of the proposed recognition method is that because of the simplicity of calculation of the extracted features and the DMNN, this method can be used in real applications.
AB - In this paper a modified dendrite morphological neural network (DMNN) is applied for recognition and classification of 3D objects. For feature extraction, the first two Hu's moment invariants are calculated based on 2D binary images, as well as the mean and the standard deviation obtained on 2D grayscale images. These four features were fed into a DMNN for classification of 3D objects. For testing, COIL-20 image database and a generated dataset were used. A comparative analysis of the proposed method with MLP and SVM is presented and the results reveal the advantages of the modified DMNN. An important characteristic of the proposed recognition method is that because of the simplicity of calculation of the extracted features and the DMNN, this method can be used in real applications.
KW - 3D object recognition
KW - Dendrite morphological neural network
KW - classification
KW - efficient training
UR - http://www.scopus.com/inward/record.url?scp=84884952368&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38989-4_32
DO - 10.1007/978-3-642-38989-4_32
M3 - Contribución a la conferencia
SN - 9783642389887
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 314
EP - 324
BT - Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings
T2 - 5th Mexican Conference on Pattern Recognition, MCPR 2013
Y2 - 26 June 2013 through 29 June 2013
ER -