Computer image processing and recognition
Image formation and perception. Representation. Ebhancement and restoration. Reconstruction from projections. Digital television, encoding, and data compression. Scene understanding. Scene matcching and recognition.
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Image Enhancement and Restoration
Scene Matching and Recognition
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average Bandwidth Compression binary brightness channel circulant matrix coding color components computed computed tomography considered contrast convolution coordinates correlation corresponding covariance matrix curve defined density described determine Digital Image discrete discrete Fourier transform distance distribution edge encoding enhancement entropy equal equation estimate example Figure Fourier transform given gray level high-pass high-pass filter histogram Huang IEEE IEEE Trans illustrated Image Processing input invariant inverse linear low-pass filter luminance mapping mean squared error measure method modulation neural noise nonlinear object obtained optical original image parameters Pattern Recognition perception picture elements Picture Processing pixels probability problem Proc produced projection quantization random rate distortion theory receptors reconstruction region response retinal rotation sampled scene matching sequence shown in Fig signal solution spatial frequency spectral techniques theorem three-dimensional threshold tion transfer function transmission tristimulus two-dimensional values variance vector visual system York