Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural image processing strategies applied in real-time pattern recognition
Real-Time Imaging - Special issue on natural and artificial real-time imaging and vision
Inferring sparse, overcomplete image codes using an efficient coding framework
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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We derive a recurrent neural network architecture of single cells in the primary visual cortex that dynamically improves a 2D-Gabor wavelet based representation of an image by minimizing the corresponding reconstruction error via feedback connections. Furthermore, we demonstrate that the reconstruction error is a Lyapunov function of the herein proposed recurrent network. Our model of the primary visual cortex combines a modulatory feedforward strategy and a feedback subtractive correction for obtaining an optimal coding. The fed back error is used in our system for a dynamical improvement of the feedforward Gabor representation of the images, in the sense that the feedforward redundant representation due to the non-orthogonality of the Gabor wavelets is dynamically corrected. The redundancy of the Gabor feature representation is therefore dynamically eliminated by improving the reconstruction capability of the internal representation. The dynamics therefore introduce a nonlinear correction to the standard linear representation of Gabor filters that generates a more efficient predictive coding.