Real-time vision-based system for textile fabric inspection
Real-Time Imaging
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Wood inspection with non-supervised clustering
Machine Vision and Applications
Convolutional Neural Networks for Face Recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Defects Detection in Continuous Manufacturing by means of Convolutional Neural Networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
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Adaptive learning is an important neural network characteristic; this means that they learn how to take care of difficult tasks by learning through illustrative samples of the problem to solve. Since neural networks can learn to tell the difference among many patterns by samples and training, there is no need to elaborate an a priori model, neither to develop specific probability distribution functions. This work presents the application results of a new architecture based on convolutional neural networks, named Convolutional Downward Spiral Architecture (CDSA), that generates digital filters automatically, which can be applied in a wide range of inspection systems.