Статті в журналах: "Model of the color vision"

1

Yoder, Lane. "Relative absorption model of color vision." Color Research & Application 30, no. 4 (2005): 252–64. http://dx.doi.org/10.1002/col.20121.

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2

김하나, 이지영, and 이지호. "Color Model Development of Color Conversion Technology for Color Vision Defectives." Journal of Korea Society of Color Studies 28, no. 2 (May 2014): 49–58. http://dx.doi.org/10.17289/jkscs.28.2.201405.49.

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3

Massof, Robert W. "Color-vision theory and linear models of color vision." Color Research & Application 10, no. 3 (1985): 133–46. http://dx.doi.org/10.1002/col.5080100302.

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4

BONNARDEL, VALÉRIE. "Color naming and categorization in inherited color vision deficiencies." Visual Neuroscience 23, no. 3-4 (May 2006): 637–43. http://dx.doi.org/10.1017/s0952523806233558.

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Dichromatic subjects can name colors accurately, even though they cannot discriminate among red-green hues (Jameson & Hurvich, 1978). This result is attributed to a normative language system that dichromatic observers developed by learning subtle visual cues to compensate for their impoverished color system. The present study used multidimensional scaling techniques to compare color categorization spaces of color-vision deficient (CVD) subjects to those of normal trichromat (NT) subjects, and consensus analysis estimated the normative effect of language on categorization. Subjects sorted 140 Munsell color samples in three different ways: a free sorting task (unlimited number of categories), a constrained sorting task (number of categories limited to eight), and a constrained naming task (limited to eight basic color terms). CVD color categories were comparable to those of NT subjects. For both CVD and NT subjects, a common color categorization space derived from the three tasks was well described by a three-dimensional model, with the first two dimensions corresponding to reddish-greenish and yellowish-bluish axes. However, the third axis, which was associated with an achromatic dimension in NTs, was not identified in the CVD model. Individual differences multidimensional scaling failed to reveal group differences in the sorting tasks. In contrast, the personal color naming spaces of CVD subjects exhibited a relative compression of the yellowish-bluish dimension that is inconsistent with the typical deutan-type color spaces derived from more direct measures of perceptual color judgments. As expected, the highest consensus among CVDs (77%) and NTs (82%) occurred in the naming task. The categorization behaviors studied in this experiment seemed to rely more on learning factors, and may reveal little about CVD perceptual representation of colors.

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5

Jetsu, Tuija, Yasser Essiarab, Ville Heikkinen, Timo Jaaskelainen, and Jussi Parkkinen. "Color classification using color vision models." Color Research & Application 36, no. 4 (November 8, 2010): 266–71. http://dx.doi.org/10.1002/col.20632.

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6

Chittka, Lars. "BEE COLOR VISION IS OPTIMAL FOR CODING FLOWER COLOR, BUT FLOWER COLORS ARE NOT OPTIMAL FOR BEING CODED—WHY?" Israel Journal of Plant Sciences 45, no. 2-3 (May 13, 1997): 115–27. http://dx.doi.org/10.1080/07929978.1997.10676678.

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Model calculations are used to determine an optimal color coding system for identifying flower colors, and to see whether flower colors are well suited for being encoded. It is shown that the trichromatic color vision of bees comprises UV, blue, and green receptors whose wavelength positions are optimal for identifying flower colors. But did flower colors actually drive the evolution of bee color vision? A phylogenetic analysis reveals that UV, blue, and green receptors were probably present in the ancestors of crustaceans and insects 570 million years ago, and thus predate the evolution of flower color by at least 400 million years. In what ways did flower colors adapt to insect color vision? The variability of flower color is subject to constraint. Flowers are clustered in the bee color space (probably because of biochemical constraints), and different plant families differ strongly in their variation of color (which points to phylogenetic constraint). However, flower colors occupy areas of color space that are significantly different from those occupied by common background materials, such as green foliage. Finally, models are developed to test whether the colors of flowers of sympatric and simultaneously blooming species diverge or converge to a higher degree than expected by chance. Such effects are indeed found in some habitats.

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7

Valberg, Arne, and Thorstein Seim. "Neurophysiological correlates of color vision: A model." Psychology & Neuroscience 6, no. 2 (2013): 213–18. http://dx.doi.org/10.3922/j.psns.2013.2.09.

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8

Guth, S. Lee. "Model for color vision and light adaptation." Journal of the Optical Society of America A 8, no. 6 (June 1, 1991): 976. http://dx.doi.org/10.1364/josaa.8.000976.

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9

Fry, Glenn A. "Color vision model of macLeod and Boynton." Color Research & Application 14, no. 3 (June 1989): 152–56. http://dx.doi.org/10.1002/col.5080140309.

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10

Ohkoba, Minoru, Tomoharu Ishikawa, Shoko Hira, Sakuichi Ohtsuka, and Miyoshi Ayama. "Analysis of Hue Circle Perception of Congenital Red-green Congenital Color Deficiencies Based on Color Vision Model." Color and Imaging Conference 2020, no. 28 (November 4, 2020): 105–8. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.15.

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To investigate individual property of internal color representation of congenital red-green color-deficient observers (CDOs) and color-normal observers (CNOs) precisely, difference scaling experiment using pairs of primary colors was carried out for protans, deutans, and normal trichromats, and the results were analyzed using multi-dimensional Scaling (MDS). MDS configuration of CNOs showed circular shape similar to hue circle, whereas that of CNO showed large individual differences from circular to U- shape. Distortion index, DI, is proposed to express the shape variation of MDS configuration. All color chips were plotted in the color vision space, (L, r/g, y/b), and the MDS using a non-linear conversion from the distance in the color vision space to perceptual difference scaling was successful to obtain U-shape configuration that reflects internal color representation of CDOs.

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11

BROWN, ANGELA M., and DELWIN T. LINDSEY. "Infant color vision and color preferences: A tribute to Davida Teller." Visual Neuroscience 30, no. 5-6 (July 24, 2013): 243–50. http://dx.doi.org/10.1017/s0952523813000114.

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AbstractAlmost 40 years ago, Davida Teller developed the forced-choice preferential looking method for studying infant visual capabilities and used it to study infant color vision. About 10 years ago, she used infant looking preferences to study infant color perception. Here, we examine four data sets in which the infant looking preference was measured using a wide range of saturated colors. Three of those data sets, from papers by Marc Bornstein and by Davida Teller and Anna Franklin and their respective collaborators, were fit successfully using MacLeod and Boynton’s model of the equiluminant plane in color space, in spite of the varied luminances used in those studies. A fourth data set, from a paper by Zemach, Chang, and Teller, was less well fit by that model. Apparently, infants are able to ignore luminance, and pay attention just to the color of stimuli. These results are discussed in the context of Davida Teller’s work on the philosophy of vision science.

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12

Sakurai, M., and M. Ayama. "A model for color appearance in peripheral vision." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 84, Appendix (2000): 197. http://dx.doi.org/10.2150/jieij1980.84.appendix_197.

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13

Guth, S. Lee. "Model for color vision and light adaptation: erratum." Journal of the Optical Society of America A 9, no. 2 (February 1, 1992): 344. http://dx.doi.org/10.1364/josaa.9.000344.

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14

Tao, Linmi, and Guangyou Xu. "A new color constancy model for machine vision." Journal of Computer Science and Technology 16, no. 6 (November 2001): 567–73. http://dx.doi.org/10.1007/bf02943241.

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15

Shin, JaeChul, Naoki Matsuki, Hirohisa Yaguchi, and Satoshi Shioiri. "A Color Appearance Model Applicable in Mesopic Vision." Optical Review 11, no. 4 (July 2004): 272–78. http://dx.doi.org/10.1007/s10043-004-0272-3.

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16

Ebner, Marc. "A Computational Model for Color Perception." bams 8, no. 4 (December 2012): 387–415. http://dx.doi.org/10.1515/bams-2012-0028.

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ABSTRACT Color is not a physical quantity of an object. It cannot be measured. We can only measure reflectance, i.e. the amount of light reflected for each wavelength. Nevertheless, we attach colors to the objects around us. A human observer perceives colors as being approximately constant irrespective of the illuminant which is used to illuminate the scene. Colors are a very important cue in everyday life. They can be used to recognize or distinguish different objects. Currently, we do not yet know how the brain arrives at a color constant or approximately color constant descriptor, i.e. what computational processing is actually performed by the brain. What we need is a computational description of color perception in particular and color vision in general. Only if we are able to write down a full computational theory of the visual system then we have understood how the visual system works. With this contribution, a computational model of color perception is presented. This model is much simpler compared to previous theories. It is able to compute a color constant descriptor even in the presence of spatially varying illuminants. According to this model, the cones respond approximately logarithmic to the irradiance entering the eye. Cells in V1 perform a change of the coordinate system such that colors are represented along a red-green, a blue-yellow and a black-white axis. Cells in V4 compute local space average color using a resistive grid. The resistive grid is formed by cells in V4. The left and right hemispheres are connected via the corpus callosum. A color constant descriptor which is presumably used for color based object recognition is computed by subtracting local space average color from the cone response within a rotated coordinate system.

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17

Lv, Jingqin, and Jiangxiong Fang. "A Color Distance Model Based on Visual Recognition." Mathematical Problems in Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/4652526.

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In computer vision, Euclidean Distance is generally used to measure the color distance between two colors. And how to deal with illumination change is still an important research topic. However, our evaluation results demonstrate that Euclidean Distance does not perform well under illumination change. Since human eyes can recognize similar or irrelevant colors under illumination change, a novel color distance model based on visual recognition is proposed. First, we find that various colors are distributed complexly in color spaces. We propose to divide the HSV space into three less complex subspaces, and study their specific distance models. Then a novel hue distance is modeled based on visual recognition, and the chromatic distance model is proposed in line with our visual color distance principles. Finally, the gray distance model and the dark distance model are studied according to the natures of their subspaces, respectively. Experimental results show that the proposed model outperforms Euclidean Distance and the related methods and achieves a good distance measure against illumination change. In addition, the proposed model obtains good performance for matching patches of pedestrian images. The proposed model can be applied to image segmentation, pedestrian reidentification, visual tracking, and patch or superpixel-based tasks.

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18

Chen, Ching Yi, and Chi Chiang Ko. "Designing FIRA Medium-Sized Soccer Robot Vision System Using Particle Swarm Optimization." Applied Mechanics and Materials 764-765 (May 2015): 675–79. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.675.

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Enabling FIRA medium-sized soccer robots to recognize target objects according to color information requires that competing teams manually set the range of colors according to ambient lighting conditions prior to games. This color information is used to differentiate features of target objects, such as the ball, the goals, and the field. Constructing a color-feature model such as this is extremely time-consuming and the resulting model is unable to adapt dynamically to changes in lighting conditions. This study applied a look-up table method to execute RGB-HSV color space conversion to accelerate video processing. A particle swarm optimization (PSO) scheme was developed to detect the color-feature parameters of the target objects in the HSV color space. This enables the automatic completion of color-feature modeling and the construction of the knowledge model required by the robot for object recognition. Experiment results demonstrate that the proposed method is capable of enhancing the robustness of the robot vision system in determining changes in lighting conditions. In addition, the manpower and time required to set robot parameters prior to games were reduced significantly.

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19

Wray, Jonathan, and Gerald M. Edelman. "A Model of Color Vision Based on Cortical Reentry." Cerebral Cortex 6, no. 5 (1996): 701–16. http://dx.doi.org/10.1093/cercor/6.5.701.

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20

Backhaus, W., and R. Menzel. "Color distance derived from a receptor model of color vision in the honeybee." Biological Cybernetics 55, no. 5 (February 1987): 321–31. http://dx.doi.org/10.1007/bf02281978.

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21

Backhaus, W., and R. Menzel. "Color distance derived from a receptor model of color vision in the honeybee." Biological Cybernetics 55, no. 5 (February 1987): 321–31. http://dx.doi.org/10.1007/bf00320544.

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22

Smet, Kevin A. G., Michael A. Webster, and Lorne A. Whitehead. "Color appearance model incorporating contrast adaptation—Implications for individual differences in color vision." Color Research & Application 46, no. 4 (January 27, 2021): 759–73. http://dx.doi.org/10.1002/col.22620.

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23

de Almeida, Vasco, Jorge Santos, João Linhares, Catarina João, and Sérgio Nascimento. "Predicting visual search for abnormal color vision with perceptual models of color deficient vision." Journal of Vision 15, no. 12 (September 1, 2015): 1311. http://dx.doi.org/10.1167/15.12.1311.

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24

Buluswar, Shashi D., and Bruce A. Draper. "Color Models for Outdoor Machine Vision." Computer Vision and Image Understanding 85, no. 2 (February 2002): 71–99. http://dx.doi.org/10.1006/cviu.2001.0950.

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25

Gouinaud, Hélène, and Lara Leclerc. "COLOR SEGMENTATION OF MGG COLORED CYTOLOGICAL IMAGES USING NON LINEAR OPPONENT COLOR SPACES." Image Analysis & Stereology 32, no. 3 (October 28, 2013): 167. http://dx.doi.org/10.5566/ias.v32.p167-174.

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This paper presents a color image segmentation method for the quantification of viable cells from samples obtained after cytocentrifugation process and May Grunwald Giemsa (MGG) coloration and then observed by optical microscopy. The method is based on color multi-thresholding and mathematical morphology processing using color information on human visual system based models such as CIELAB model, LUX (Logarithmic hUe eXtension) model and CoLIP (Color Logarithmic Image Processing) model, a new human color vision based model also presented in this article. The results show that the CoLIP model, developed following each step of the human visual color perception, is particularly well adapted for this type of images.

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26

Yaguchi, Hirohisa. "Color Management Systems, Its Trend and Standardization. 2. Color Vision Model and Colorimetry for Color Management System." Journal of the Institute of Image Information and Television Engineers 53, no. 6 (1999): 780–85. http://dx.doi.org/10.3169/itej.53.780.

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27

Machado, G. M., M. M. Oliveira, and L. Fernandes. "A Physiologically-based Model for Simulation of Color Vision Deficiency." IEEE Transactions on Visualization and Computer Graphics 15, no. 6 (November 2009): 1291–98. http://dx.doi.org/10.1109/tvcg.2009.113.

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28

Jetsu, Tuija, Ville Heikkinen, Anahit Pogosova, Timo Jaaskelainen, and Jussi Parkkinen. "Comparison of color vision models based on spectral color representation." Color Research & Application 34, no. 5 (October 2009): 341–50. http://dx.doi.org/10.1002/col.20504.

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29

Mackin, Robert D., Ruth A. Frey, Carmina Gutierrez, Ashley A. Farre, Shoji Kawamura, Diana M. Mitchell, and Deborah L. Stenkamp. "Endocrine regulation of multichromatic color vision." Proceedings of the National Academy of Sciences 116, no. 34 (August 5, 2019): 16882–91. http://dx.doi.org/10.1073/pnas.1904783116.

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Vertebrate color vision requires spectrally selective opsin-based pigments, expressed in distinct cone photoreceptor populations. In primates and in fish, spectrally divergent opsin genes may reside in head-to-tail tandem arrays. Mechanisms underlying differential expression from such arrays have not been fully elucidated. Regulation of human red (LWS) vs. green (MWS) opsins is considered a stochastic event, whereby upstream enhancers associate randomly with promoters of the proximal or distal gene, and one of these associations becomes permanent. We demonstrate that, distinct from this stochastic model, the endocrine signal thyroid hormone (TH) regulates differential expression of the orthologous zebrafish lws1/lws2 array, and of the tandemly quadruplicated rh2-1/rh2-2/rh2-3/rh2-4 array. TH treatment caused dramatic, dose-dependent increases in abundance of lws1, the proximal member of the lws array, and reduced lws2. Fluorescent lws reporters permitted direct visualization of individual cones switching expression from lws2 to lws1. Athyroidism increased lws2 and reduced lws1, except within a small ventral domain of lws1 that was likely sustained by retinoic acid signaling. Changes in lws abundance and distribution in athyroid zebrafish were rescued by TH, demonstrating plasticity of cone phenotype in response to this signal. TH manipulations also regulated the rh2 array, with athyroidism reducing abundance of distal members. Interestingly, the opsins encoded by the proximal lws gene and distal rh2 genes are sensitive to longer wavelengths than other members of their respective arrays; therefore, endogenous TH acts upon each opsin array to shift overall spectral sensitivity toward longer wavelengths, underlying coordinated changes in visual system function during development and growth.

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Haider, Tazeem, Muhammad Shahid Farid, Rashid Mahmood, Areeba Ilyas, Muhammad Hassan Khan, Sakeena Tul-Ain Haider, Muhammad Hamid Chaudhry, and Mehreen Gul. "A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves." Agriculture 11, no. 8 (August 11, 2021): 766. http://dx.doi.org/10.3390/agriculture11080766.

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Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.

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31

Xu, Xiangyang, Qiao Chen, and Ruixin Xu. "The Study of Spatial Frequency Channels for Human Visual System." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 06 (April 21, 2019): 1955007. http://dx.doi.org/10.1142/s0218001419550073.

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Similar to auditory perception of sound system, color perception of the human visual system also presents a multi-frequency channel property. In order to study the multi-frequency channel mechanism of how the human visual system processes color information, the paper proposed a psychophysical experiment to measure the contrast sensitivities based on 17 color samples of 16 spatial frequencies on CIELAB opponent color space. Correlation analysis was carried out on the psychophysical experiment data, and the results show obvious linear correlations of observations for different spatial frequencies of different observers, which indicates that a linear model can be used to model how human visual system processes spatial frequency information. The results of solving the model based on the experiment data of color samples show that 9 spatial frequency tuning curves can exist in human visual system with each lightness, R–G and Y–B color channel and each channel can be represented by 3 tuning curves, which reflect the “center-around” form of the human visual receptive field. It is concluded that there are 9 spatial frequency channels in human vision system. The low frequency tuning curve of a narrow-frequency bandwidth shows the characteristics of lower level receptive field for human vision system, the medium frequency tuning curve shows a low pass property of the change of medium frequent colors and the high frequency tuning curve of a width-frequency bandwidth, which has a feedback effect on the low and medium frequency channels and shows the characteristics of higher level receptive field for human vision system, which represents the discrimination of details.

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32

Kreslin, Rok, Pilar M. Calvo, Luis G. Corzo, and Peter Peer. "Linear Chromatic Adaptation Transform Based on Delaunay Triangulation." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/760123.

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Computer vision algorithms that use color information require color constant images to operate correctly. Color constancy of the images is usually achieved in two steps: first the illuminant is detected and then image is transformed with the chromatic adaptation transform (CAT). Existing CAT methods use a single transformation matrix for all the colors of the input image. The method proposed in this paper requires multiple corresponding color pairs between source and target illuminants given by patches of the Macbeth color checker. It uses Delaunay triangulation to divide the color gamut of the input image into small triangles. Each color of the input image is associated with the triangle containing the color point and transformed with a full linear model associated with the triangle. Full linear model is used because diagonal models are known to be inaccurate if channel color matching functions do not have narrow peaks. Objective evaluation showed that the proposed method outperforms existing CAT methods by more than 21%; that is, it performs statistically significantly better than other existing methods.

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Ropelewska, Ewa. "Effect of boiling on classification performance of potatoes determined by computer vision." European Food Research and Technology 247, no. 4 (January 30, 2021): 807–17. http://dx.doi.org/10.1007/s00217-020-03664-z.

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AbstractThe aim of this study was to evaluate the effect of potato boiling on the correctness of cultivar discrimination. The research was performed in an objective, inexpensive and fast manner using the image analysis technique. The textures of the outer surface of slice images of raw and boiled potatoes were calculated. The discriminative models based on a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z, U, V, S), textures selected for color spaces and textures selected for individual color channels were developed. In the case of discriminant analysis of raw potatoes of cultivars ‘Colomba’, ‘Irga’ and ‘Riviera’, the accuracies reached 94.33% for the model built based on a set of textures selected from all color channels, 94% for Lab and XYZ color spaces, 92% for color channel b and 92.33% for a set of combined textures selected from channels B, b, and Z. The processed potatoes were characterized by the accuracy of up to 98.67% for the model including the textures selected from all color channels, 98% for RGB color space, 95.33% for color channel b, 96.67% for the model combining the textures selected from channels B, b, and Z. In the case of raw and processed potatoes, the cultivar ‘Irga’ differed in 100% from other potato cultivars. The results revealed an increase in cultivar discrimination accuracy after the processing of potatoes. The textural features of the outer surface of slice images have proved useful for cultivar discrimination of raw and processed potatoes.

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34

FRY, GLENN A. "Dichromatic Confusion Lines and Color Vision Models." Optometry and Vision Science 63, no. 12 (December 1986): 933–40. http://dx.doi.org/10.1097/00006324-198612000-00001.

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35

Bergasa, L. M., M. Mazo, A. Gardel, M. A. Sotelo, and L. Boquete. "Unsupervised and adaptive Gaussian skin-color model." Image and Vision Computing 18, no. 12 (September 2000): 987–1003. http://dx.doi.org/10.1016/s0262-8856(00)00042-1.

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36

Tang, San. "Human Face Detection Method Based on Skin Color Model." Advanced Materials Research 706-708 (June 2013): 1877–81. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1877.

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Face detection is the first step of face recognition, and is a very active research topic in the filed of computer vision and pattern recognition. A skin color model based face detection method for chromatic images is proposed in this paper. The H-CgCr skin color model is established by taking advantage of the color pixels clustering distribution in the HSV and YCgCr color space. The noises are eliminated based on skin color segmentation, and the face candidate region is judged according to knowledge-based, finally, the position of the face area is marked by the box. The experimental results demonstrate that the proposed method is feasible and effective.

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37

Ximenes, Nathalia G., and Felipe M. Gawryszewski. "Prey and predators perceive orb-web spider conspicuousness differently: evaluating alternative hypotheses for color polymorphism evolution." Current Zoology 65, no. 5 (September 6, 2018): 559–70. http://dx.doi.org/10.1093/cz/zoy069.

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Abstract Color polymorphisms have been traditionally attributed to apostatic selection. The perception of color depends on the visual system of the observer. Theoretical models predict that differently perceived degrees of conspicuousness by two predator and prey species may cause the evolution of polymorphisms in the presence of anti-apostatic and apostatic selection. The spider Gasteracantha cancriformis (Araneidae) possesses several conspicuous color morphs. In orb-web spiders, the prey attraction hypothesis states that conspicuous colors are prey lures that increase spider foraging success via flower mimicry. Therefore, polymorphism could be maintained if each morph attracted a different prey species (multiple prey hypothesis) and each spider mimicked a different flower color (flower mimicry hypothesis). Conspicuous colors could be a warning signal to predators because of the spider’s hard abdomen and spines. Multiple predators could perceive morphs differently and exert different degrees of selective pressures (multiple predator hypothesis). We explored these 3 hypotheses using reflectance data and color vision modeling to estimate the chromatic and achromatic contrast of G. cancriformis morphs as perceived by several potential prey and predator taxa. Our results revealed that individual taxa perceive the conspicuousness of morphs differently. Therefore, the multiple prey hypothesis and, in part, the multiple predator hypothesis may explain the evolution of color polymorphism in G. cancriformis, even in the presence of anti-apostatic selection. The flower mimicry hypothesis received support by color metrics, but not by color vision models. Other parameters not evaluated by color vision models could also affect the perception of morphs and influence morph survival and polymorphism stability.

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Mahadev, Shudeish, and Ronald C. Henry. "Application of a color-appearance model to vision through atmospheric haze." Color Research & Application 24, no. 2 (April 1999): 112–20. http://dx.doi.org/10.1002/(sici)1520-6378(199904)24:2<112::aid-col6>3.0.co;2-j.

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39

Sperling, Harry G., Anthony A. Wright, and Stephen L. Mills. "Color vision following intense green light exposure: Data and a model." Vision Research 31, no. 10 (January 1991): 1797–812. http://dx.doi.org/10.1016/0042-6989(91)90027-3.

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40

Katayama, Ichiro, and Mark D. Fairchild. "Quantitative evaluation of perceived whiteness based on a color vision model." Color Research & Application 35, no. 6 (November 10, 2009): 410–18. http://dx.doi.org/10.1002/col.20551.

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41

LINHARES, JOÃO M. M., PAULO D. PINTO, and SÉRGIO M. C. NASCIMENTO. "The number of discernible colors perceived by dichromats in natural scenes and the effects of colored lenses." Visual Neuroscience 25, no. 3 (May 2008): 493–99. http://dx.doi.org/10.1017/s0952523808080620.

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The number of discernible colors perceived by normal trichromats when viewing natural scenes can be estimated by analyzing idealized color volumes or hyperspectral data obtained from actual scenes. The purpose of the present work was to estimate the relative impairment in chromatic diversity experienced by dichromats when viewing natural scenes and to investigate the effects of colored lenses. The estimates were obtained computationally from the analysis of hyperspectral images of natural scenes and using a quantitative model of dichromats' vision. The color volume corresponding to each scene was represented in CIELAB color space and segmented into cubes of unitary side. For normal trichromats, the number of discernible colors was estimated by counting the number of non-empty cubes. For dichromats, an algorithm simulating for normal observers the appearance of the scenes for dichromats was used, and the number of discernible colors was then counted as for normal trichromats. The effects of colored lenses were estimated by prior filtering the spectral radiance from the scenes with the spectral transmittance function of the lenses. It was found that in dichromatic vision the number of discernible colors was about 7% of normal trichromatic vision. With some colored lenses considerable improvements in chromatic diversity were obtained for trichromats; for dichromats, however, only modest improvements could be obtained with efficiency levels dependent on the combination of scene, lens and type of deficiency.

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42

Boker, Steven M. "A Measurement of the Adaptation of Color Vision to the Spectral Environment." Psychological Science 8, no. 2 (March 1997): 130–34. http://dx.doi.org/10.1111/j.1467-9280.1997.tb00695.x.

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An exploratory factor analysis of the reflectance spectral distributions of a sample of natural and man-made objects yields a factor pattern remarkably similar to psychophysical color-matching curves. The goodness-of-fit indices from a maximum likelihood confirmatory factor model with fixed factor loadings specified by empirical trichromatic color-matching data indicate that the human visual system performs near to an optimum value for an ideal trichromatic system composed of three linear components. An unconstrained four-factor maximum likelihood model fits significantly better than a three-factor unconstrained model, suggesting that a color metric is better represented in four dimensions than in a three-dimensional space. This fourth factor can be calculated as a nonlinear interaction term between the first three factors: thus, a trichromatic input is sufficient to compute a color space of four dimensions. The visual system may exploit this nonlinear dependency in the spectral environment in order to obtain a four-dimensional color space without the biological cost of a fourth color receptor.

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Widayati, Kanthi A., Atsuko Saito, Bambang Suryobroto, Akichika Mikami, and Kowa Koida. "Color Perception in Protanomalous Female Macaca fascicularis." i-Perception 10, no. 2 (March 2019): 204166951984613. http://dx.doi.org/10.1177/2041669519846136.

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Protanomalous females with X chromosome-linked color vision deficiency exhibit mild abnormalities, whereas dichromats show a distinct deficiency in discriminating certain color pairs. Dichromats have an advantage in detecting a textured target when it is camouflaged by red-green colors, owing to their insensitivity to these colors. However, it is not certain whether protanomalous females possess a similar advantage in breaking camouflage. Here, we introduce an animal model of dichromatic macaque monkeys and protanomalous females. We examined whether protanomalous females have the same advantage in breaking color camouflage as shown by dichromatic macaques. We also tested whether they could discriminate a certain color pair that trichromats could, where the dichromats are confused. Our experiments show that protanomalous macaques can break color camouflage, similar to dichromats, and can discriminate colors similarly to trichromats. Protanomalous females are thus thought to have the combined ecological advantages of being both trichromats and dichromats.

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Pan, Zhi Bin, and Xiao Yan Wei. "Computer Vision Based Orange Grading Using SVM." Applied Mechanics and Materials 303-306 (February 2013): 1134–38. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1134.

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Fruit grading is very important for promoting its additional value. We graded oranges based on its images. Four photos were taken from different view angles for each orange. Both RGB and HSI color model were utilized. We extracted a 28-dimensional feature which can describe the size and color of them. Then support vector machine was used to grade these oranges into four levels. Experimental result shows SVM has promising performance for orange grading.

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Ohkoba, Minoru, Tomoharu Ishikawa, Shoko Hira, Sakuichi Ohtsuka, and Miyoshi Ayama. "Color representations of normals and congenital red–green color deficiencies: Estimation of individual results based on color vision model." Color Research & Application 47, no. 3 (December 13, 2021): 565–84. http://dx.doi.org/10.1002/col.22763.

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46

Shi Chunjie, 史春洁, та 朱斐 Zhu Fei. "基于色貌模型CIECAM02和CIECAM16的观察者色觉差异研究". Laser & Optoelectronics Progress 58, № 19 (2021): 1933001. http://dx.doi.org/10.3788/lop202158.1933001.

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47

VANLEEUWEN, M. T., C. JOSELEVITCH, I. FAHRENFORT, and M. KAMERMANS. "The contribution of the outer retina to color constancy: A general model for color constancy synthesized from primate and fish data." Visual Neuroscience 24, no. 3 (May 2007): 277–90. http://dx.doi.org/10.1017/s0952523807070058.

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Color constancy is one of the most impressive features of color vision systems. Although the phenomenon has been studied for decades, its underlying neuronal mechanism remains unresolved. Literature indicates an early, possibly retinal mechanism and a late, possibly cortical mechanism. The early mechanism seems to involve chromatic spatial integration and performs the critical calculations for color constancy. The late mechanism seems to make the color manifest. We briefly review the current evidence for each mechanism. We discuss in more detail a model for the early mechanism that is based on direct measurements of goldfish outer retinal processing and induces color constancy and color contrast. In this study we extrapolate this model to primate retina, illustrating that it is highly likely that a similar mechanism is also present in primates. The logical consequence of our experimental work in goldfish and our model is that the wiring of the cone/horizontal cell system sets the reference point for color vision (i.e., it sets the white point for that animal).

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Shi, Chaoyi, Fuliang Dai, Cuiping Lu, Shaohui Yu, Meina Lu, Xianhe Gao, Zhongma Wang, and Sheng Zhang. "Color Recognition of Transparent Plastic Based on Multi-Wavelength Transmission Spectrum." Applied Sciences 12, no. 10 (May 13, 2022): 4948. http://dx.doi.org/10.3390/app12104948.

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Plastic recycling is the most effective way to solve plastic pollution. Color recognition of plastic is a key step in the plastic recycling process, which is very important for improving the utilization rate of waste plastic. At present, the manual recognition method is inefficient and cannot meet the requirements of large-scale production, while the existing color recognition based on machine vision has problems such as low recognition accuracy of similar colors, complicated algorithm and high system cost. According to the above problems, a color recognition method based on multi-wavelength transmission spectrum was proposed in this paper for the color recognition of colorless, light blue, light green and apple green transparent plastic. For each transparent plastic, the transmission spectra at 381 nm, 439 nm, 620 nm and white transmission light source were obtained, and the ratios of R, G and B wavebands of the transmission spectra were used to set up a color feature, which was then used for color recognition. A color recognition model was established based on the color features and naive Bayes model. The 10-fold cross-validation results showed that the recognition accuracy of the four colors of transparent plastic reached 100%.

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Zhang, Zhuo, Xin Nan Fan, Xue Wu Zhang, Rui Yu Liang, and Shan Ming Lin. "Traffic Sign Recognition Based on Vision Bionics." Applied Mechanics and Materials 190-191 (July 2012): 746–51. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.746.

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Inspired by the research of human visual system in neuroanatomy and psychology, the paper proposes an road traffic sign identification model based on vision bionics.The model combines data-driven and task-driven visual attention mechanism to focus on traffic sign target rapidly and accuractly.Firstly,It simulates the Itti attention model to obtain “what” information and uses the priori knowledge of positional distribution of traffic sign as “where” information.Then,it adjusts saliency map according to “what” and “where” stream so as to select traffic sign focus preferentially.Secondly,it measures the similarity of shape and color features between traffic sign and attention region to get interested region.Finally, it segments traffic sign based on color characteristics and classify shape of traffic sign based on the Support Vector Machine method. The experimental results demonstrate that the feasibility and effectiveness of the proposed model; Furthermore, the average accuracy rate of shape classification on DtBs matrix reaches 98%.

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ŽUKAUSKAS, ARTŪRAS, and RIMANTAS VAICEKAUSKAS. "LEDs IN LIGHTING WITH TAILORED COLOR QUALITY." International Journal of High Speed Electronics and Systems 20, no. 02 (June 2011): 287–301. http://dx.doi.org/10.1142/s012915641100660x.

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Polychromatic lighting sources that are composed of at least four different colored light-emitting diodes (LEDs) offer versatility in color quality of illumination. In this paper, different methods of assessment of color quality of white light are discussed and a general approach to the solution of the color-mixing problem by means of optimization in respect of several color rendition characteristics is considered. Spectral power distributions of model tetrachromatic solid-state sources obtained by maximizing various figures of merit, such as color rendering index, gamut area index, color quality scale, and indices based on the statistical analysis of the just perceivable chromaticity differences for a large number of test color samples, are demonstrated. A concept tetrachromatic lighting source that can be operated within a dynamical trade-off between two opposing color rendition characteristics, the ability to render colors with high fidelity and the ability to render colors with increased chromatic saturation, is introduced. Such "smart" sources with tailored color quality can meet individual needs and preferences of color vision and find numerous applications in lighting.

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