Relevant bibliographies by topics / Color vision

Academic literature on the topic 'Color vision'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Color vision"

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Bensinger, Richard E. "Color vision and color vision testing." Current Opinion in Ophthalmology 3, no. 1 (February 1992): 108–10. http://dx.doi.org/10.1097/00055735-199202000-00015.

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Jenny, Bernhard, and Nathaniel Vaughn Kelso. "Color Design for the Color Vision Impaired." Cartographic Perspectives, no. 58 (September 1, 2007): 61–67. http://dx.doi.org/10.14714/cp58.270.

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Eight percent of men are affected by color vision impairment – they have difficulties distinguishing between colors and thus confuse certain colors that the majority of people see readily. Designers of maps and information graphics cannot disregard the needs of this relatively large group of media consumers. This article discusses the most common forms of color vision impairment, and introduces Color Oracle, a new software tool that assists the designer in verifying color schemes. Color Oracle filters maps and graphics in real-time and efficiently integrates with existing digital workflows. The paper also discusses color combinations and alternative visual variables for map symbology that those with color vision impairments can distinguish unambiguously. The presented techniques help the cartographer produce maps that are easy to read for those with color vision impairments and can still look good for those with normal color vision.
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Bornstein, Marc H. "Selective vision." Behavioral and Brain Sciences 20, no. 2 (June 1997): 180–81. http://dx.doi.org/10.1017/s0140525x97231420.

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The physics of color and the psychology of color naming are not isomorphic. Physically, the spectrum is continuous with regard to wavelength – one point in the spectrum differs from another only by the amount of wavelength difference. Psychologically, hue is categorical – colors change qualitatively from one wavelength region to another. The psychological characterization of hue that characterizes color vision has been revealed in a series of modern psychophysical studies with human adults and infants and with various infrahuman species, including vertebrates and invertebrates. These biopsychological data supplant an older psycholinguistic and anthropological literature that posited that language and culture alone influence perceptual processes; language and culture may modify color naming beyond basic categorizations.
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Rossi, Michael. "Color vision." Science 369, no. 6501 (July 16, 2020): 259–60. http://dx.doi.org/10.1126/science.abd3644.

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LENNIE, PETER. "COLOR VISION." Optics and Photonics News 2, no. 8 (August 1, 1991): 10. http://dx.doi.org/10.1364/opn.2.8.000010.

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Boynton, R. M. "Color Vision." Annual Review of Psychology 39, no. 1 (January 1988): 69–100. http://dx.doi.org/10.1146/annurev.ps.39.020188.000441.

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SWANSON, W., and J. COHEN. "Color vision." Ophthalmology Clinics of North America 16, no. 2 (June 2003): 179–203. http://dx.doi.org/10.1016/s0896-1549(03)00004-x.

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DeValois, Karen, and Michael Webster. "Color vision." Scholarpedia 6, no. 4 (2011): 3073. http://dx.doi.org/10.4249/scholarpedia.3073.

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Liggins, Eric P., and William P. Serle. "Color Vision in Color Display Night Vision Goggles." Aerospace Medicine and Human Performance 88, no. 5 (May 1, 2017): 448–56. http://dx.doi.org/10.3357/amhp.4605.2017.

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Hartman, Dorothy K. "Color Vision and Tests Used to Assess Color Vision." American Orthoptic Journal 36, no. 1 (January 1986): 165–73. http://dx.doi.org/10.1080/0065955x.1986.11981717.

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Dissertations / Theses on the topic "Color vision"

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Kwok, Pui-yan Veronica, and 郭沛殷. "Learning new color names produces lateralized categorical color perception: a training study." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B49858592.

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Previous behavioral and neuroimaging findings (Drivonikou, et al., 2007; Gilbert, et al., 2006; Tan, et al., 2008; Siok, et al., 2009) indicate that reaction times to targets in visual search are faster in the right than the left visual field when the target and distractor colors straddle a category boundary. This phenomenon is known as the lateralized categorical color perception, which supports the weaker form of Whorf’s hypothesis that linguistic information shapes color perception. Yet, these studies did not demonstrate a definite cause and effect relation between language and perception. The observed lateralized category effect of color perception may either rely on the individual’s innate color categories or his linguistic experience. In the present study, we used an intensive training method to study categorical perception (CP) of color. We aimed to show a definite causal relation between language and perception. In Experiment 1, 37 native Mandarin speakers were tested with a color discrimination task. We taught 20 participants four new linguistic items for the four stimulus colors which were initially from the same lexical category (two blues and two greens) whilst other participants did not learn any new color names. Performances between the two groups were compared before and after training. Experiment 2 was based on Zhou et al.’s (2010) behavioral study, in which we used the same training procedure and measured and contrasted 19 participants’ brain structure before and after training. In experiment 1, participants exhibited lateralized Whorf effect when performing the visual search task at the pre-training phase. After training, the experimental group successfully acquired the new color names, reflected by overall shorter reaction time and higher task accuracy, while the control group did not show significant difference in the performance across two phases. The improved performance of experimental group implicated that the newly learned categories altered participants’ color perception pattern. However, we failed to show lateralized Whorf effect at the post-training phase due to several experimental flaws. In Experiment 2, gray matter density is found to increase in color region of the left visual cortex after a short-term training (less than two hours). The data provided strong structural evidence for newly-learned categorical color perception and also suggested structural plasticity of the human brain. The results from this study indicate that language experience shapes perception, both functionally and structurally, after a period of learning that is much shorter than previously established (Draganski, 2004; Carreiras, et al., 2009; Trachtenberg, 2002).
published_or_final_version
Linguistics
Master
Master of Philosophy
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Skaff, Sandra. "Spectral models for color vision." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66750.

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This thesis introduces a maximum entropy approach to model surface reflectance spectra. A reflectance spectrum is the amount of light, relative to the incident light, reflected from a surface at each wavelength. While the color of a surface can be in 3D vector form such as RGB, CMY, or YIQ, this thesis takes the surface reflectance spectrum to be the color of a surface. A reflectance spectrum is a physical property of a surface and does not vary with the different interactions a surface may undergo with its environment. Therefore, models of reflectance spectra can be used to fuse camera sensor responses from different images of the same surface or multiple surfaces of the same scene. This fusion improves the spectral estimates that can be obtained, and thus leads to better estimates of surface colors. The motivation for using a maximum entropy approach stems from the fact that surfaces observed in our everyday life surroundings typically have broad and therefore high entropy spectra. The maximum entropy approach, in addition, imposes the fewest constraints as it estimates surface reflectance spectra given only camera sensor responses. This is a major advantage over the widely used linear basis function spectral representations, which require a prespecified set of basis functions. Experimental results show that surface spectra of Munsell and construction paper patches can be successfully estimated using the maximum entropy approach in the case of three different surface interactions with the environment. First, in the case of changes in illumination, the thesis shows that the spectral models estimated are comparable to those obtained from the best approach which computes spectral models in the literature. Second, in the case of changes in the positions of surfaces with respect to each other, interreflections between the surfaces arise. Results show that the fusion of sensor responses from interreflection
Cette thèse introduit une approche par entropie maximale pour la modélisation des spectres de réflectance de surface. Un spectre de réflectance est la quantité de lumière, relative à la lumière incidente, réfléchie d'une surface à chaque longueur d'onde. Bien que la couleur d'une surface puisse prendre la forme d'un vecteur 3D tel que RGB, CMY ou YIQ, cette thèse prend le spectre de réflectance de surface comme étant la couleur d'une surface. Un spectre de réflectance est une propriété physique d'une surface et ne varie pas avec les différentes interactions que peut subir une surface avec son environnement. Par conséquent, les modèles de spectres de réflectance peuvent être utilisés pour fusionner les réponses de senseurs de caméra provenant de différentes images d'une même surface ou de multiples surfaces de la même scène. Cette fusion améliore les estimés spectraux qui peuvent être obtenus et mène donc à de meilleurs estimés de couleurs de surfaces.La motivation pour l'utilisation d'une approche par entropie maximale provient du fait que les surfaces observées dans notre environnement habituel ont typiquement un spectre large et donc à haute entropie. De plus, l'approche par entropie maximale impose le moins de contraintes puisqu'elle estime les spectres de réflectance de surface à l'aide seulement des réponses de senseurs de caméra. Ceci est un avantage majeur par rapport aux très répandues représentations spectrales par fonctions de base linéaires qui requièrent une série pré-spécifiée de fonctions de base.Les résultats expérimentaux montrent que les spectres de surface de taches de surface de Munsell et de papier de construction peuvent être estimés avec succès en utilisant l'approche par entropie maximal dans le cas de trois différentes interactions de surfaces avec l'environnement. D'abord, dans le cas de changements dans l'illumination, la t
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Gilbert, B. John. "Color Vision in the Bovine." DigitalCommons@USU, 1985. https://digitalcommons.usu.edu/etd/4117.

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Eight heifers were trained using operant conditioning to press a plate to receive a feed reward. Different wavelengths of light were presented as correct and incorrect stimuli. Positive and negative responses to the stimuli were registered electronically. Daily sessions of 17 minutes were conducted in a chamber with external light being excluded. The duration of the stimulus was fixed at 17 seconds after which stimuli were randomly presented. Only presses on the plate when the correct stimulus was presented were reinforced with feed. A 75% correct choice was the criterion used as acceptable discrimination. Ratios of correct to incorrect responses were computed. A stability of response was judged to occur when the median of these ratios over 5 days did not differ by more than .05 from the median of the ratios from the previous 5 sessions. Three colors i.e. green {535nm), red {610nm), and blue {450nm) were compared pairwise during 8 trials. Trial 7 was a repeat trial of green vs red and trial 8 was a comparison of green vs green. Heifers gave random response to green vs green. Red was distinguished from blue by five of the heifers: 1, 2, 3, 4, and 5 at 76%, 91%, 78%, 88%, and 81% correct choice respectively. Blue was distinguished from green by three of the heifers: 1, 2, and 5 at 89%, 88%, and 85% correct choice respectively. Green was distinguished from red by three of the heifers: 1, 5, and 7 at 90%, 84%, and 85% correct choice respectively. These last discriminations we r e made in the repeat trial of green vs red after heifers failed to do so in the first trial of green vs red. Color discrimination and discrimination learning have been demonstrated by these results.
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Voraphani, Natthapongs. "Color vision screening using eye movements." [Ames, Iowa : Iowa State University], 2007.

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Shayeghpour, Omid. "Improving information perception from digital images for users with dichromatic color vision." Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-101984.

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Color vision deficiency (CVD) is the inability or limited ability to recognize colors and discriminate between them. A person with this condition perceives a narrower range of colors compared to a person with a normal color vision. A growing number of researchers are striving to improve the quality of life for CVD patients. Finding cure, making rectification equipment, providing simulation tools and applying color transformation methods are among the efforts being made by researchers in this field. In this study we concentrate on recoloring digital images in such a way that users with CVD, especially dichromats, perceive more details from the recolored images compared to the original image. The main focus is to give the CVD user a chance to find information within the picture which they could not perceive before. However, this transformed image might look strange or unnatural to users with normal color vision. During this color transformation process, the goal is to keep the overall contrast of the image constant while adjusting the colors that might cause confusion for the CVD user. First, each pixel in the RGB-image is converted to HSV color space in order to be able to control hue, saturation and intensity for each pixel and then safe and problematic hue ranges need to be found. The method for recognizing these ranges was inspired by a condition called “unilateral dichromacy” in which the patient has normal color vision in one eye and dichromacy in another. A special grid-like color card is designed, having constant saturation and intensity over the entire image, while the hue smoothly changes from one block to another to cover the entire hue range. The next step is to simulate the way this color card is perceived by a dichromatic user and finally to find the colors that are perceived identically from two images and the ones that differ too much. This part makes our method highly customizable and we can apply it to other types of CVD, even personalize it for the color vision of a specific observer. The resulting problematic colors need to be dealt with by shifting the hue or saturation based on some pre-defined rules. The results for the method have been evaluated both objectively and subjectively. First, we simulated a set of images as they would be perceived by a dichromat and compared them with simulated view of our transformed images. The results clearly show that our recolored images can eliminate a lot of confusion from user and convey more details. Moreover, an online questionnaire was created and 39 users with CVD confirmed that the transformed images allow them to perceive more information compared to the original images.
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Stokman, Harro. "Robust photometric invariance in machine color vision." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2000. http://dare.uva.nl/document/56969.

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Wright, Anne R. (Anne Renée). "A low-cost color vision tracking system." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10896.

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Neuschwanger, Christina Mary. "The development of color and motion processing /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3096419.

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Martinez, Elizabeth. "Bisection of cognitive color space : do individual "midpoint" judgements reveal the dimensional structure of suprathreshold color differences /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3112197.

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Szmajda, Brett A. "Subcortical pathways for colour vision /." Connect to thesis, 2006. http://eprints.unimelb.edu.au/archive/00003165.

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Thesis (Ph.D.)--University of Melbourne, The National Vision Research Institute of Australia and Dept. of Optometry & Vision Sciences, 2007.
Typescript. Includes bibliographical references (leaves 95-111).
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Books on the topic "Color vision"

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Lamouline, Roger. Voir, nommer et figurer les couleurs: Du cercle de Newton aux pixels tricolores. Méolans-Revel: Atelier Perrousseaux, 2006.

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Kremers, Jan, Rigmor C. Baraas, and N. Justin Marshall, eds. Human Color Vision. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44978-4.

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M, Boynton Robert, ed. Human color vision. 2nd ed. Washington, DC: Optical Society of America, 1996.

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Valberg, Arne. Light Vision Color. New York: John Wiley & Sons, Ltd., 2006.

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Illumination, International Commission on. Improvement to industrial colour-difference evaluation. Vienna, Austria: CIE Central Bureau, 2001.

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Burner, Alan McManus. Color choreography: Foundational studies, investigations, and discourse in color theory. 4th ed. Mason, OH, USA: Cengage Learning, 2008.

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Bill, Franklin, and American Association of Physics Teachers., eds. Teaching about color and color vision. College Park, MD: American Association of Physics Teachers, 1996.

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Gevers, Theo, Arjan Gijsenij, Joost van de Weijer, and Jan-Mark Geusebroek. Color in Computer Vision. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118350089.

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Cohen, Jozef. Visual color and color mixture: The fundamental color space. Urbana, Ill: University of Illinois Press, 2001.

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Birch, Jennifer. Diagnosis of defective colour vision. Oxford: OUP, 1993.

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Book chapters on the topic "Color vision"

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Beyerer, Jürgen, Fernando Puente León, and Christian Frese. "Color." In Machine Vision, 163–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47794-6_5.

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Daw, Nigel W. "Color Vision." In Sensory System I, 10–11. Boston, MA: Birkhäuser Boston, 1988. http://dx.doi.org/10.1007/978-1-4899-6647-6_7.

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Skalicky, Simon E. "Color Vision." In Ocular and Visual Physiology, 343–53. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-846-5_24.

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Lucassen, By Marcel P. "Color Vision." In Color in Computer Vision, 11–25. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118350089.ch2.

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Rossing, Thomas D., and Christopher J. Chiaverina. "Color Vision." In Light Science, 173–202. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-0-387-21698-0_8.

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Park, Jong-Ho. "Color Vision." In Primary Eye Examination, 37–45. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-6940-6_4.

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Rossing, Thomas D., and Christopher J. Chiaverina. "Color Vision." In Light Science, 229–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27103-9_9.

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Shevell, Steven K. "Color vision." In Encyclopedia of psychology, Vol. 2., 182–86. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10517-069.

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Hathibelagal, Amithavikram R. "Color Vision." In Ophthalmic Diagnostics, 101–12. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0138-4_9.

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Tominaga, Shoji. "Color Model." In Computer Vision, 1–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_449-1.

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Conference papers on the topic "Color vision"

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Post, David L., and Christopher S. Calhoun. "Color-Name Boundaries for Color Coding." In Applied Vision. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/av.1989.pd1.

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One of the main problems that arises when designing color codes for electronic visual displays involves color selection. The colors must be distinctive and immediately recognizable as corresponding with the color names they represent. Otherwise, their meanings may be ambiguous, thereby defeating the code's purpose. We are approaching this problem by mapping the relationship between location on the CIE 1976 uniform chromaticity-scale (UCS) diagram and population stereotypes for color naming. This information should simplify the color selection process by helping the designer avoid, for example, specifying a "red" that actually appears orange. Thus, our project can be characterized as an attempt to improve on the Kelly (1943) color boundaries and is similar with an earlier effort by Haeusing (1976). It is also related to Boynton and Olson's (1987) work on focal colors. This paper describes our method, provides an overview of six experiments we have performed, and shows some representative results.
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Uchikawa, Keiji. "Categorical Characteristics of Color Discrimination in Memory." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.sac1.

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Colors can be systematically represented, based on their appearance, in a three-dimensional color space. They change continuously in any direction in the color apace so that we could discriminate a million of colors when two color are simultaneously compared in juxtaposed fields. It is unlikely, however, that we can utilize so many colors in our everyday situations where some color memory seems to be necessarily involved.1-5 Color appearance may not be precisely retained in memory, and colors may be categorically organized in memory.6,7,8 In the present paper, I will focus on color discrimination or identification tasks using memory. Some of our recent studies, which indicate categorical influences on color discrimination in memory, are described here.
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Momma, C., S. Honma, H. Yaguchi, H. Haneishi, and Y. Miyake. "Color Appearance and Color Reproduction for Mesopic Vision." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.sab10.

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Color vision at an intermediate luminance or illuminance level between photopic and scotopic range is called mesopic vision. The color of objects in mesopic vision is differently perceived from that in photopic vision, because not only cones but also rods contribute to the visual response. In general, red becomes dimmer than blue with decreasing illuminance level, which is known as the Purkinje phenomenon. Although many studies concerning the brightness in mesopic vision have been reported, there are very few experimental data dealing with color appearance at the mesopic level.
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Mausfeld, Rainer J., and Reinhard M. Niederée. "On increments-decrement differences in multiplicative gain control." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.sab13.

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Center-surround stimuli evoke color appearances resembling surface colors which cannot be produced by a single homogeneous spot of light alone (e.g. brown, grey). Although this seems of great impact to a general theory of color (incl. color constancy), the psychophysics of these ‘minimal relational stimuli’ is still less well understood than often assumed. On the basis of empirical as well as theoretical observations concerning center-surround-type stimuli we introduce a relational model of color coding. The proposed model takes into account results on “discounting the background” mechanisms by Whittle, Walraven and Shevell and it is closely related to ratio-based relational concepts (Wallach, Land) and certain opponent color theories. Comprising the Young-Helmholtz, respectively Grassmann theory of color vision as a special case, this model provides an analogue to the classical distinction between light and object colors as well.
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Wu, Duan, Peng Gao, and Ying Zhang. "Optimization of the Emergency Evacuation Sign's Color Cognition for Users with Color Vision Deficiency." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001607.

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Color has the characteristics of fast reading and fast recognition, with this reason, information in environments needs color to help fast communication, especially in the situation of emergency evacuation. The colour and graphic symbols on emergency evacuation signs(EES) help direct people to safety and provide emergency information quickly.(Barry Gray. 2012)But according to statistics, about 8% of the world population are suffered by color vision deficiency（CVD）. While they are not resolved all colors, just easy to confuse some color. Today, different countries or organizations have different standard for EES, and many research shows, the color recognition of EES still has the phenomenon of uneven benefits of different groups of people, which means there are significant differences in the color recognition efficiency of EES between CVD and normal vision groups, especially deuteranomalous vision group (G, Landini, G. Perryer.2009).While the appropriate color selection can substantially improve CVD groups’ color recognition and at the same time not affecting the normal users’ color recognition rate. Therefore, to explore appropriate EES color design optimization for the CVD population has the social and scientific significance.With this background, this research intends to study the EES color recognition of CVD people and try to build optimize EES color model for this group of users. The research start with different selections of EES color standard among countries and organizations. Through the comparison of these standard colors, some color samples are sorted out with the help of the recognition models of CVD people. Then totally 57 CVD people participated the research as experimental volunteers to test the recognition of selected samples. The final ranking of samples were influenced by both the color hue and also the color lightness contrast between EES background and the icon or text. The objective of the research is to build a more inclusive practical color model for improving EES and other safety sign design. The result of this research could assist color design optimization and help the EES design to select appropriate color, without affecting the recognition rate of normal color vision people, while greatly improving the recognition of CVD group. The research conforms to the design thinking of universal design, inclusive design and human-centred design. The results could be used to optimize or review EES and other signage color design, could also apply to other visual information communication field.
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Daley, Michael L., George A. Burghen, David Meyer, and Paul Maisky. "Racial Difference of Blue-Sensitive Mechanism." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fb9.

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Acquired loss of color vision is associated with diseases which produce vascular complications that affect the eye.1,2 In particular, the early loss of color vision associated with diabetes mellitus initially influences the blue-sensitive mechanism. Generally, as the disease progresses, loss of green vision becomes evident, and finally, red vision is involved.3 The patho-physiological mechanisms which produce the visual loss appear to be related to optical and neural changes within the eye.4,5 The optical loss may be caused by light scattering produced by plasma proteins which leak into the retina through an altered blood-retinal barrier.4,5
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Okajima, Katsunori, Masanori Takase, and Sumio Takahashi. "Perception of translucent colors with binocular parallax." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.sab7.

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There are some kinds of color appearance mode: luminous color mode, surface color mode and so on. Translucent color mode is also a kind of them. For example, when we look a colored object or a colored light through a frosted glass, we perceive a translucent color. In this mode, it is different from other modes that a plural color perception is caused by visual system. Therefore it is suggested that translucent perception relates to functions of higher level in color vision. The purpose of this work is to analyze the mechanism of translucent perception by simulating translucent colors with binocular parallax.
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Rosenthal, Odeda. "Why ignore color vision confusion?" In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mx6.

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Meager statistics tell us that at least 20 million males and 2 million females in the U.S.A, have color vision confusion. Many more acquire it through industrial, accidental or medical reasons. Yet although computer-generated and other color coding systems are used more and more, little attention has been given to the problem. For more than 25 years I have collected anecdotal and personal information of color vision confusion and spoken of my findings. Recent experiments have verified my assertions (Cole 1988. Davidson and Myslinski 1990, Steward and Cole 1989). Gerald Murch, who first forced an electrical impulse to create colors on a computer monitor, invited me to speak at the National Computer Graphics Association conference at Anaheim in 1988 on the human factors of color vision confusion because he has had to deal with it first hand. There is reason for the bias against speaking on the problems of those with color vision confusion, but it is time to face the reality. Actual cases are presented and a 12-min. video of six persons speaking about their reactions can be shown.
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Werner, John S., and Michael A. Webster. "Color vision is form and object vision." In 9th Congress of the International Color Association, edited by Robert Chung and Allan Rodrigues. SPIE, 2002. http://dx.doi.org/10.1117/12.464653.

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Lennie, Peter. "Roles of Central Chromatic Mechanisms." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fc2.

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One of the most challenging problems of color vision is to understand what analyses take place at higher levels in the visual pathway. Psychophysical and physiological observations have established very clearly the general properties of the receptoral and immediate post-receptoral stages of color vision, but have not firmly defined the characteristics of mechanisms at higher levels.
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Reports on the topic "Color vision"

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Krauskopf, John. Higher Order Mechanisms of Color Vision. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada214616.

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Krauskopf, John. Higher Order Mechanisms of Color Vision. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada198093.

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Laxar, Kevin V. U.S. Navy Color Vision Standards Revisited. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada347110.

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Krauskopf, John. Higher Order Mechanisms of Color Vision. Fort Belvoir, VA: Defense Technical Information Center, November 1991. http://dx.doi.org/10.21236/ada244720.

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Goulding, John. Adaptive Color Correlation of Knots in Wood Images and Weighted-value Product Selection Methods in a Machine Vision System. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7065.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, May 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in AI-SCW have been implemented using the python programming language, specifically using libraries such as scikit-image for image processing, scikit-learn for machine learning and dimensionality reduction, keras for computer vision with deep learning, and matplotlib for generating visualizations. Therefore, AI-SCW modularized implementation allows users to accomplish a variety of underwater computer vision tasks, which include: detecting laser points from the underwater images for use in scale determination; performing contrast enhancement and color normalization to improve the visual quality of the images; semi-automated generation of annotations to be used downstream during supervised classification; training a convolutional neural network (Inception v3) using the generated annotations to semantically classify each image into one of pre-defined seafloor habitat categories; evaluating sampling strategies for generation of balanced training images to be used for fitting an unsupervised k-means classifier; and visualization of classification results in both feature space view and in map view geospatial co-ordinates. Thus, the workflow is useful for a quick but objective generation of image-based seafloor habitat maps to support monitoring of remote benthic ecosystems.
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Rösener, Ringo. Little Rock Revisited – On the Challenges of Training One’s Imagination to Go Visiting. Association Inter-University Centre Dubrovnik, March 2022. http://dx.doi.org/10.53099/ntkd4305.

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In this working paper, I ask whether or not whites could and should write about concerns of People of Color. To this end, I deal with Hannah Arendt’s controversial article “Reflections on Little Rock” from winter 1958/59. In her article, Arendt comments on the de-segregation of black school children in the USA and the associated unrests in Little Rock (Arkansas) and Charlotte (North Carolina) on September 4, 1957. My analysis of her article is initiated by a confrontation of two other texts. In the first, Why I’m No Longer Talking to White People About Race Reni Eddo-Lodge argues that white people are not able to understand the point of view of people of color. In the second, On Kant’s Political Philosophy Hannah Arendt advocates for the contrary that people can understand each other’s point of view when training their imagination to take visits. Since Arendt’s “Reflections on Little Rock” is considered to be a failure, especially in regards of grasping the problems of people of color in the USA, my general question is whether Eddo-Lodge is right, and whether there is no understanding possible or if Arendt missed a crucial step in her own attempt to go visiting? To clarify this, my analysis focuses on Arendt’s use of the term “discrimination”.
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Moynihan, Emily, and James O’Donoghue. The Forefront : A Review of ERDC Publications, Summer 2022. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44862.

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As the main research and development organization for the US Army Corps of Engineers (USACE), the Engineer Research and Development Center (ERDC) helps solve our nation’s most challenging problems. With seven laboratories under the ERDC umbrella, ERDC expertise spans a wide range of disciplines. This provides researchers an amazing network of collaborators both within labs and across them. Many of the publications produced by ERDC through the Information Technology Laboratory’s Information Science and Knowledge Management Branch (ISKM), the publishing authority for ERDC, are a testament to the power of these partnerships. Therefore, in this issue of The Forefront, we wanted to highlight some of those collaborations, across ERDC and beyond. Colored flags at the top of each page indicate the laboratories involved in each report (see the end of this issue for a full list of the laboratories and their lab colors), in addition to USACE red for district collaborators and gray for others. Through these collaborations, ERDC is continuing to demonstrate its value nationally and internationally. Questions about the reports highlighted in The Forefront or others published by ERDC? Contact the ISKM virtual reference desk at erdclibrary@ask-a-librarian.info or visit ERDC Knowledge Core, ISKM’s online repository, at https://erdc-library.erdc.dren.mil/. For general questions about editing and publishing at ERDC, you are also welcome to reach out to me at Emily.B.Moynihan@usace.army.mil. We look forward to continuing to be a resource for ERDC and seeing all the remarkable research that is yet to come.
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