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PubMed1992

Color selectivity of neurons in the inferior temporal cortex of the awake macaque monkey.

We tested the color selectivity of neurons in the inferior temporal (IT) cortex of two awake macaque monkeys. A color stimulus was presented at the center of the visual field while the animal performed a fixation task. The responses of single units to various colors were recorded and were plotted in a color space. The color space was based on the CIE (Commission Internationale de l'Eclairage) chromaticity diagram. Quantitative analysis of the color selectivity was performed using a standard set of colors that were evenly distributed in the color space. Sixty-five neurons recorded from eight guide tubes implanted in anterior part of IT cortex were tested quantitatively, and their color selectivity was statistically evaluated. Forty-six of them (71%) were classified as color selective. Color-selective cells responded to some colors but not to others, and we called the responsive region in the color space the "color field." The location and the size of a cell's color field were largely independent of the luminance of the stimulus. About 80% of the color-selective neurons had color fields consisting of a single responsive region that were restricted in some part of the color space, and the remaining cells had more than one responsive region within the color space. Preferred hues of the color-selective neurons differed from cell to cell. The population of cells together covered nearly all of the color space. There was a tendency for more color-selective cells to be less sensitive to white and desaturated cyans. Some of the cells with color fields that consisted of more than one responsive region were more sensitive to saturation of the stimulus than to its hue. Although previous electrophysiological studies in IT cortex emphasized the pattern selectivity of the neurons, our results suggest that color is an important factor in the role that IT cortex plays in the object recognition.

Analysis of Variance↗
PubMed2006

Colorimetric characterization for comparative analysis of fungal pigments and natural food colorants.

Exogenous pigments produced by ascomycetous filamentous fungi belonging to the genera Penicillium, Epicoccum, and Monascus, preselected based on chemotaxonomic knowledge, have been extracted and characterized by quantitative colorimetry. The color characteristics of the fungal extracts were compared to water soluble natural colorants derived from sources currently in use. The tested fungal extracts also included some commercially available Monascus colorants. The a values for the fungal extracts were found to be both positive and negative, the b values were found to be positive, while the hue angles of the fungal color extracts ranged from 40 to 110 indicating the color distribution of fungal extracts over the red-orange-yellow region of the CIELAB color space. The fungal extracts exhibited additional color hues in the red spectrum and similar hues in the yellow spectrum as compared to the reference natural colorants. They were also found to be similar or brighter in terms of chroma to some of the reference natural colorants. Principal component analysis was performed to group and distinguish different colors based on the a and b values. The fungal color extracts could be grouped in accordance with the similarity or difference in the color to those of the existing natural colorants. The diversity of colors was not only found among different fungal genera and/or species but also within the same species on changing the media. There was a marked change in the color composition of the extracts resulting in relatively different hues. Our results, thus, indicate that there exists pigment-producing genera of ascomycetous fungi other than Monascus that produce color shades in the red and the yellow spectra in addition or similar to reference colorants. These color shades could add to the color palette of the natural colorants currently in use. In addition, the multivariate approach in distinguishing and classifying the colorants was shown to be a very useful tool in colorimetric comparison of colorants.

Analysis of Variance↗
PubMed2005

Color clustering and learning for image segmentation based on neural networks.

An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L*u* v* color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.

Algorithms↗