Color character recognition method based on human perception

Yamaba, Kazuo; Miyake, Yoichi

doi:10.1117/12.60072

Cited by 12 publications

(10 citation statements)

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“…Other definitions of opponent colors have been proposed in the literature, as for example, disregarding the term |R − G| in the computation of the yellow [24,30], using logarithmic differences and color ratios [2,9], or minimizing the correlation between the color components [17,20,25]. We tested all these definitions, and found the adopted formulations (1)-(4) better than the others for our system.…”

Section: Opponent Colorsmentioning

confidence: 93%

Detecting salient cues through illumination-invariant color ratios

Todt

Torras

2004

Robotics and Autonomous Systems

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Section: Opponent Colorsmentioning

confidence: 93%

Detecting salient cues through illumination-invariant color ratios

Todt

Torras

2004

Robotics and Autonomous Systems

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“…Optical correlation methods have been widely used in polychromatic pattern recognition in recent years. [1][2][3][4][5][6][7][8][9][10][11] The purpose of introducing color in optical pattern recognition is twofold: improvement of the recognition process and an increase in processing capacity. The capacity is increased because different wavelengths of light do not interfere with each other and can process different information.…”

Section: Introductionmentioning

confidence: 99%

“…Another approach involves preprocessing of RGB components on the basis of human perception. 9,11 In this paper we suggest an approach to the synthesis of color component transformations that is directly connected to the correlators' discrimination capability, and we investigate several simple transformations that can be regarded as simplified implementations of an optimal process that guarantees the highest discrimination capability of the correlators in color pattern recognition. 15 We consider optoelectronic systems as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Color component transformations for optical pattern recognition

et al. 1997

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Several elementwise component transformations performed over primary color image components (RGB) before optical multichannel correlations are proposed to improve real-time multispectral pattern recognition. The first transformation is deduced from the theory of the optimal filter for object location and recognition extended to multispectral images. Several modifications of this transformation are studied. We investigate these transformations in terms of noise robustness and discrimination capability. Computer simulation with noisy input images for various kinds of correlation filter are presented to illustrate improvement of color pattern recognition by using the proposed transformations. Experimental results are also presented. © 1997 Optical Society of America [S0740-3232(97)01510-X] 2656

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“…1 -4 Other problems are the recognition of a given shape regardless of the color distribution of the object and the identif ication of the color of the diverse zones that compose the object. 5,6 Millán et al 5 proposed a recognition strategy that involves the signals used to generate the matched filters and the decision criteria applied to the RGB multichannel correlation results. Yamaba and Miyake 6 proposed a transformation of the RGB components provided by the camera that considers the Hurvich-Jameson color vision model 7 with some modifications.…”

mentioning

confidence: 99%

“…5,6 Millán et al 5 proposed a recognition strategy that involves the signals used to generate the matched filters and the decision criteria applied to the RGB multichannel correlation results. Yamaba and Miyake 6 proposed a transformation of the RGB components provided by the camera that considers the Hurvich-Jameson color vision model 7 with some modifications. Their recognition system is based on the projected weight values obtained from a learning process by a neural board.…”

mentioning

confidence: 99%

Optical pattern recognition based on color vision models

et al. 1995

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A channel transformation based on opponent-color theory of the color vision models is applied to optical pattern recognition so that the conventional red, green, and blue (RGB) channels are transformed into bright -dark, red -green, and yellow -blue (ATD) channels. Matched f iltering and correlation are performed over the new components of the target and the scene in the ATD system. The proposed transformation allows us to reduce the number of channels commonly used in color pattern recognition, passing from the three RGB channels to the two red -green and yellow -blue opponent-color channels. © 1995 Optical Society of AmericaThe introduction of color information in pattern recognition is especially useful when the contours and the intensity distribution do not provide enough information to permit the discrimination of images. The most extensive way to incorporate color into pattern recognition is through a trichromatic decomposition of the image into red, green, and blue (RGB) components that are independently analyzed. The RGB multichannel decomposition has been associated with matched filtering and optical correlation techniques for the performance of optical color pattern recognition. One of the methods used for the RGB decomposition is the illumination of a color transparency containing the target by three monochromatic wavelengths separately. 1,2The three wavelengths, belonging to the red, green, and blue regions, must be distributed in the visible spectrum. For the illumination of the optical correlator it has been proposed that the same wavelengths be used for the generation of the filters 2 or that a white-light source combined with a grating in the scene plane be used. 3Another method used for RGB decomposition is the acquisition and digitization of the image by a color camera. In this case the channels are defined by the response of the camera in a wide bandwidth. The RGB components of the scene are displayed in the correlator either on an achromatic spatial light modulator 3 or on a color spatial light modulator. 4 This method permits digital preprocessing of the acquired image before it is displayed.Different problems in polychromatic pattern recognition have been studied in Refs. 1-6. The most common problem is the recognition of a given object, taking into account both the shape and the color distribution. 1 -4 Other problems are the recognition of a given shape regardless of the color distribution of the object and the identif ication of the color of the diverse zones that compose the object. 5,6 Millán et al. 5 proposed a recognition strategy that involves the signals used to generate the matched filters and the decision criteria applied to the RGB multichannel correlation results. Yamaba and Miyake 6 proposed a transformation of the RGB components provided by the camera that considers the Hurvich-Jameson color vision model 7 with some modifications. Their recognition system is based on the projected weight values obtained from a learning process by a neural board.Color human vision models ba...

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Color character recognition method based on human perception

Cited by 12 publications

References 0 publications

Detecting salient cues through illumination-invariant color ratios

Detecting salient cues through illumination-invariant color ratios

Color component transformations for optical pattern recognition

Optical pattern recognition based on color vision models

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