Three-dimensional color object visualization and recognition using multi-wavelength computational holography

In this paper, we address 3D object visualization and recognition with multi- wavelength digital holography. Color features of 3D objects are obtained by the multiple- wavelengths. Perfect superimposition technique generates reconstructed images of the same size. Statistical pattern recognition techniques: principal component analysis and mixture discriminant analysis analyze multi- spectral information in the reconstructed images. Class- conditional probability density functions are estimated during the training process. Maximum likelihood decision rule categorizes unlabeled images into one of trained- classes. It is shown that a small number of training images is sufficient for the color object classification. (c) 2007 Optical Society of America.