The bottlenecks in human letter recognition: a computational model
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We have implemented two machine-learning models of object recognition by human observers. Both models capture three hallmarks of human performance that cannot be accounted for by template matching: (1) spatial frequency channels, (2) crowding, (3) effects of letter complexity. One model is a Convolutional Neural Network (ConvNet), and the other is a texture statistics model followed by a linear classifier. With appropriate hyper-parameters and training, both models account for spatial-frequency channels, crowding, and effects of letter complexity.
[1] Eero P. Simoncelli,et al. A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.
[2] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[3] D. Pelli,et al. Feature detection and letter identification , 2006, Vision Research.