Morphological bidirectional associative memories
Neural Networks
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
Automatic Face Recognition via Wavelets and Mathematical Morphology
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Image compression algorithm based on morphological associative memories
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Object recognition using multilayer Hopfield neural network
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Foveal automatic target recognition using a multiresolution neural network
IEEE Transactions on Image Processing
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
Morphological associative memories
IEEE Transactions on Neural Networks
Nonlinear quantization on Hebbian-type associative memories
Applied Intelligence
FPGA-Based architecture for extended associative memories and its application in image recognition
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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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.