Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
A new design method for the complex-valued multistate Hopfield associative memory
IEEE Transactions on Neural Networks
Neighborhood dependent approximation by nonlinear embedding for face recognition
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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In this paper, we propose the concept of a manifold of color perception through empirical observation that the center-surround properties of images in a perceptually similar environment define a manifold in the high dimensional space. Such a manifold representation can be learned using a novel recurrent neural network based learning algorithm. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represent memory as a nonlinear line of attraction. The region of convergence around the nonlinear line is defined by the statistical characteristics of the training data. This learned manifold can then be used as a basis for color correction of the images having different color perception to the learned color perception. Experimental results show that the proposed recurrent neural network learning algorithm is capable of color balance the lighting variations in images captured in different environments successfully.