TY - GEN
T1 - Feature generation using the Laplacian operator with Neumann boundary condition
AU - Khabou, Mohamed A.
AU - Rhouma, Mohamed B.H.
AU - Hermi, Lotfi
PY - 2007
Y1 - 2007
N2 - The eigenvalues of the Neumann Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated features are rotation, translation, and size invariant and are shown to be tolerant of boundary deformation. The effectiveness of these features is demonstrated by using them to classify 5 types of computer generated and hand drawn shapes. The classification was done using 4 to 20 features fed to a simple feedforward neural network. Correct classification rates ranging from 94.4% to 100% were obtained on computer generated shapes and 67.5% to 95.5% on hand drawn shapes.
AB - The eigenvalues of the Neumann Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated features are rotation, translation, and size invariant and are shown to be tolerant of boundary deformation. The effectiveness of these features is demonstrated by using them to classify 5 types of computer generated and hand drawn shapes. The classification was done using 4 to 20 features fed to a simple feedforward neural network. Correct classification rates ranging from 94.4% to 100% were obtained on computer generated shapes and 67.5% to 95.5% on hand drawn shapes.
UR - http://www.scopus.com/inward/record.url?scp=34547650611&partnerID=8YFLogxK
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U2 - 10.1109/SECON.2007.343005
DO - 10.1109/SECON.2007.343005
M3 - Conference contribution
AN - SCOPUS:34547650611
SN - 1424410290
SN - 9781424410293
T3 - Conference Proceedings - IEEE SOUTHEASTCON
SP - 766
EP - 771
BT - 2007 IEEE SoutheastCon
T2 - 2007 IEEE SoutheastCon
Y2 - 22 March 2007 through 25 March 2007
ER -