Color pattern recognition with circular component whitening

Abstract

Polychromatic object recognition based on circular whitening preprocessing of red -green -blue components and multichannel matched filtering is described. Computer simulations and experimental results are provided to facilitate recognizing a color target among objects of similar shape but with different color contents. Experimental results are obtained with an optical correlator with two spatial light modulators, one to introduce the scene and the second one to introduce the filter. © 1996 Optical Society of America Color pattern recognition based on optical correlation methods has been a subject of intensive investigations in the past few years. 1 -5 In general, a color image provides more information than a monochromatic one, and therefore additional color information could contribute in some way to a better correlation performance. Various approaches considering how to use the color contents of a signal to improve pattern recognition have been suggested. One of the simplest ways to take into account the color content of objects is to carry out correlation filtering in three red -green-blue (RGB) channels independently, then to make either arithmetic or logical elementwise operations over the correlation outputs, and finally to find signal maxima on the resulting correlation plane. Even such a simple approach using a phase-only filter 6 in the multichannel correlation procedure provides good results that one can use to recognize a target with a given shape and color combinations of an object, regardless of what the combinations are. 3 -5 Color information of a signal can be involved in the recognition process more effectively by introduction of an elementwise proprocessing of color components before optical multichannel correlations. Badiqué et al.2 have developed a means of projecting polychromatic images on a generalized color plane. This method reduces the number of components used in the correlation process and enhances the ability of the correlator to discriminate between images of different colors. Another fruitful approach involves preprocessing of the RGB components on the basis of human perception. 7 It is known that the RGB components of the original color image are strongly correlated with one another. We propose to perform component transformation by carrying out whitening operation along the color axis. 8 The transformed color components can be considered independent components in the above sense, and channel correlations can be performed independently.The purpose of this paper is to investigate the performance of color pattern recognition based on RGB component preprocessing (whitening) and multichannel correlations. We show that by using a phase-only filter in each transformed channel, and then adding correlation outputs, one can achieve a good recognition performance. In addition, the use of spatial light modulators in correlation architectures permits f lexible implementation of correlation experiments with different images and f ilters.The whitening of operation applied over a discrete ...

Color component transformations for optical pattern recognition

“…puter simulation and optical experiment that an improvement of color pattern recognition can be obtained with this operation for real-life images. 17 Note that the transformed components ͕d k (x, y)͖ have the following features:…”

Hartley transform and the use of the Whitened Hartley spectrum as a tool for phase spectral processing

“…A real signal could be exactly represented, via the short‐time discrete Hartley transform, by two separate frequency domain functions, both real. One, the magnitude spectrum which is identical to that derived via the Fourier transform and represents the square‐root of the power spectral density function of the signal, whereas the second function, rather clumsily called the ‘Whitened Hartley spectrum’, is a function of phase only [19]; the term ‘whitened’ has been used since the derivation of the ‘Whitened Hartley spectrum’ is the result of the ‘whitening’ process [20]. This latter function, unlike its Fourier counterpart, is bounded and does not suffer from wrapping ambiguities thus avoiding the difficulties introduced by the discontinuities in the discrete phase spectrum when this is derived via the Fourier transform [21].…”

<title>Color motion stereo based on adaptive correlation matching</title>

“…By repeating the procedure described above, we obtain a set of matrices { S' (i) ; i-U I-i ). Finally, the depth map can be computed as /jj=ARGkMIN (s,')), (12) where i=O I-i,j=O f-i.…”