Optimizing Digital Hardware Perceptrons for Multi-Spectral Image Classification
Journal of Mathematical Imaging and Vision
Recognition of Patterns Without Feature Extraction by GRNN
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Projection-Field-Type VLSI Convolutional Neural Networks Using Merged/Mixed Analog-Digital Approach
Neural Information Processing
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
IEEE Transactions on Neural Networks
Application of DCT blocks with principal component analysis for face recognition
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A convolutional neural network tolerant of synaptic faults for low-power analog hardware
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Optimization of image processing techniques using neural networks: a review
WSEAS Transactions on Information Science and Applications
Pixel-based machine learning in medical imaging
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Multiscale convolutional neural networks for vision: based classification of cells
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Convolutional neural networks provide an efficient method to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. This network topology has been applied in particular to image classification when sophisticated preprocessing is to be avoided and raw images are to be classified directly. In this paper two variations of convolutional networks-neocognitron and a modification of neocognitron-are compared with classifiers based on fully connected feedforward layers with respect to their visual recognition performance. For a quantitative experimental comparison with standard classifiers two very different recognition tasks have been-chosen: handwritten digit recognition and face recognition. In the first example, the generalization of convolutional networks is compared to fully connected networks; in the second example human face recognition is investigated under constrained and variable conditions, and the limitations of convolutional networks are discussed