TY - JOUR
T1 - Multiclass pattern recognition using adaptive correlation filters with complex constraints
AU - Diaz-Ramirez, Victor H.
AU - Campos-Trujillo, Oliver G.
AU - Kober, Vitaly
AU - Aguilar-Gonzalez, Pablo M.
N1 - Funding Information:
The research reported in this paper was supported by "Con-sejo Nacional de Ciencia y Tecnología" (CONACYT) and "Secretaria de Investigación y Posgrado" (SIP-IPN), Mexico, through projects CONACYT-0130504 and SIP20110110.
PY - 2012/3
Y1 - 2012/3
N2 - An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.
AB - An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.
KW - Adaptive correlation filters
KW - Object classification
KW - Opto-digital correlators
UR - http://www.scopus.com/inward/record.url?scp=84887006600&partnerID=8YFLogxK
U2 - 10.1117/1.OE.51.3.037203
DO - 10.1117/1.OE.51.3.037203
M3 - Artículo
SN - 0091-3286
VL - 51
JO - Optical Engineering
JF - Optical Engineering
IS - 3
M1 - 037203
ER -