TY - GEN
T1 - Hardware implementation of image recognition system based on morphological associative memories and discrete wavelet transform
AU - Guzmán, Enrique
AU - Alvarado, Selene
AU - Pogrebnyak, Oleksiy
AU - Fernández, Luis Pastor Sánchez
AU - Yañez, Cornelio
PY - 2007
Y1 - 2007
N2 - The implementation of a specific image recognition technique for an artificial vision system is presented. The proposed algorithm involves two steps. First, smaller images are obtained using Discrete Wavelet Transform (DWT) after four stages of decomposition and taking only the approximations. This way the volume of information to process is reduced considerably and the system memory requirements are reduced as well. Another purpose of DWT is to filter noise that could be induced in the images. Second, the Morphological Associative Memories (MAM) are used to recognize landmarks. The proposed algorithm provides flexibility, possibility to parallelize algorithms and high overall performance of hardware implemented image retrieval system. The resulted hardware implementation has low memory requirements, needs in limited arithmetical precision and reduced number of simple operations. These benefits are guaranteed due to the simplicity of MAM learning/restoration process that uses simple morphological operations, dilation and erosion, in other words, MAM calculate maximums or minimums of sums. These features turn out the artificial vision system to be robust and optimal for the use in realtime autonomous systems. The proposed image recognition system has, among others, the following useful features: robustness to the noise induced in the patter to process, high processing speed, and it can be easily adapted to diverse operation circumstances.
AB - The implementation of a specific image recognition technique for an artificial vision system is presented. The proposed algorithm involves two steps. First, smaller images are obtained using Discrete Wavelet Transform (DWT) after four stages of decomposition and taking only the approximations. This way the volume of information to process is reduced considerably and the system memory requirements are reduced as well. Another purpose of DWT is to filter noise that could be induced in the images. Second, the Morphological Associative Memories (MAM) are used to recognize landmarks. The proposed algorithm provides flexibility, possibility to parallelize algorithms and high overall performance of hardware implemented image retrieval system. The resulted hardware implementation has low memory requirements, needs in limited arithmetical precision and reduced number of simple operations. These benefits are guaranteed due to the simplicity of MAM learning/restoration process that uses simple morphological operations, dilation and erosion, in other words, MAM calculate maximums or minimums of sums. These features turn out the artificial vision system to be robust and optimal for the use in realtime autonomous systems. The proposed image recognition system has, among others, the following useful features: robustness to the noise induced in the patter to process, high processing speed, and it can be easily adapted to diverse operation circumstances.
KW - Artificial vision
KW - Discrete wavelet transform
KW - Hardware implementation
KW - Image recognition
KW - Morphological associative memories
UR - http://www.scopus.com/inward/record.url?scp=38149037821&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-77129-6_57
DO - 10.1007/978-3-540-77129-6_57
M3 - Contribución a la conferencia
SN - 9783540771289
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 664
EP - 677
BT - Advances in Image and Video Technology - Second Pacific Rim Symposium, PSIVT 2007, Proceedings
PB - Springer Verlag
T2 - 2nd Pacific Rim Symposium on Image and Video Technology, PSIVT 2007
Y2 - 17 December 2007 through 19 December 2007
ER -