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Thomas E. White - One of the best experts on this subject based on the ideXlab platform.
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pavo 2 new tools for the spectral and spatial analysis of Colour in r
Methods in Ecology and Evolution, 2019Co-Authors: Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. WhiteAbstract:Author(s): Maia, Rafael; Gruson, Hugo; Endler, John; White, Thomas | Abstract: Abstract Biological Colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in Colour-based phenotypes has driven, and been driven by, improved techniques for Quantifying Colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2 , the latest iteration of the R package pavo . This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of Colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of Colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (Colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2 offers a flexible and reproducible environment for the analysis of Colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.
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pavo 2 0 new tools for the spectral and spatial analysis of Colour in r
bioRxiv, 2018Co-Authors: Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. WhiteAbstract:Biological Colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in Colour-based phenotypes has driven, and been driven by, improved techniques for Quantifying Colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2.0, the latest iteration of the R package pavo. This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of Colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2.0 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of Colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (Colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2.0 offers a flexible and reproducible environment for the analysis of Colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.
M. Ronnier Luo - One of the best experts on this subject based on the ideXlab platform.
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Quantifying Colour appearance. part V. simultaneous contrast
Color Research & Application, 1995Co-Authors: M. Ronnier Luo, X. W. Gao, Stephen A. R. ScrivenerAbstract:Experiments were carried out to investigate the effect of simultaneous contrast on Colour appearance by varying the lightness, Colourfulness, and hue of an induction field surrounding a test Colour. A total of 814 test/surround combinations were displayed on high-resolution Colour displays. Each was assessed by a panel of five to six observers using a magnitude estimation technique. the results indicate that Colours presented on a computer display are affected by simultaneous contrast in a similar way to surface Colours. All three Colour appearance parameters studied (i.e., lightness, Colourfulness, and hue) are affected and these effects are summarized. In general, the results support and add to the findings of the other studies. the Hunt Colour appearance model was tested and gave a somewhat poor prediction to this data set. Further modifications are required to improve its performance. © 1995 John Wiley & Sons, Inc.
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Quantifying Colour appearance. part IV. Transmissive media
Color Research & Application, 1993Co-Authors: M. Ronnier Luo, X. W. Gao, Peter A. Rhodes, John Haozhong Xin, A.a. Clarke, Stephen A. R. ScrivenerAbstract:The experimental data from this study extends the LUTCHI Colour-appearance data to cover transmissive media. Two further experiments were carried out: one used a large cut-sheet transparency viewed using a back-lit illuminator, and another used a 35-mm slide projected onto a white screen. These new data were used to reveal the changes in Colour appearance caused by different viewing parameters studied, and to evaluate the predictive accuracy of five uniform Colour spaces and Colour-appearance models. The results show that Hunt's 91 model (developed to fit earlier experimental results) did not perform as well as it did in the previous studies using nontransmissive media. This implies that there are large differences in perceived Colour appearance between transmissive and nontransmissive media viewing conditions. Some modifications were subsequently made to Hunt's 91 model and the predictive accuracy was greatly improved.
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Quantifying Colour appearance. part III. Supplementary LUTCHI Colour appearance data
Color Research & Application, 1993Co-Authors: M. Ronnier Luo, X. W. Gao, Peter A. Rhodes, John Haozhong Xin, A.a. Clarke, Stephen A. R. ScrivenerAbstract:The experimental data from this study supplements the LUTCHI Colour Appearance Data as described in Part I of this paper. Two further experiments were carried out: one was to check conflicting results found previously, and another was to extend the range of luminance conditions used earlier. In addition, a brightness attribute was added to the original lightness, Colourfulness, and hue scales for Colour assessment. Experiment I results verified the uncertainties found previously in the comparison between luminous and nonluminous Colours, and between the grey background results with and without a white border. Experiment II results revealed the changes in four perceived attributes under six quite different adapting luminances. The results were then used to test five uniform Colour spaces and Colour-appearance models used in Part II of this paper. Hunt's 91 model gave more accurate predictions of the experimental visual results, in comparison with the other spaces and models. Its predictive error for all attributes studied is within the accuracy of the typical observer.
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Quantifying Colour appearance. Part I. Lutchi Colour appearance data
Color Research & Application, 1991Co-Authors: M. Ronnier Luo, Stephen A. R. Scrivener, Peter A. Rhodes, A.a. Clarke, André Schappo, Chris J. TaitAbstract:The work described here forms part of a research project entitled Predictive Perceptual Colour Models. The aim of this project is to develop a Colour appearance model capable of predicting changes of Colour appearance under various different viewing conditions. This will provide industry with a quantitative measure for assessing the quality of Colour reproduction and enable more rapid and accurate proofing simulations in the graphic art industry. A large-scale experiment has been carried out in which Colour appearance was assessed under a wide range of viewing conditions. The parameters studied were (1) D65, D50, white fluorescent, and tungsten light sources, (2) luminance levels of about 40 and 240 cd/m2, (3) five background conditions: white, grey, black, grey with white border, and grey with black border, and (4) two media: luminous Colours (displayed on a high-resolution Colour monitor) and nonluminous Colours (presented in a viewing cabinet). Each Colour was assessed by a panel of six or seven observers using a magnitude estimation method. In total, 43,332 estimations were made, and these form the LUTCHI Colour Appearance Data. Data analysis has been carried out to examine the reliability of the experimental results and to understand the effects of the various viewing parameters studied. (Part II of this article describes how the LUTCHI Colour Appearance Data has been used to test the performance of various Colour spaces and models.
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Quantifying Colour appearance. Part II. Testing Colour models performance using lutchi Colour appearance data
Color Research & Application, 1991Co-Authors: M. Ronnier LuoAbstract:The acquisition of the LUTCHI Colour Appearance Data has been described in Part I of this article. Having obtained the data, they were used to test the accuracy of prediction for various Colour spaces and models. The results clearly indicate that Hunt's 91 model gives the best fit to the visual results of all the models studied. Hunt's 91 has been further refined to improve the fit to the Colourfulness results, and this refined model has been designated Hunt-ACAM (ACAM being the Alvey Colour Appearance Model). The error of prediction from Hunt-ACAM is close to the typical error that is seen to occur between individuals' results and the mean visual results. This performance is considered to be very satisfactory, and the model is therefore believed to provide a reasonably accurate way of evaluating Colour fidelity for various Colour reproduction systems. Various chromatic-adaptation transformations were also compared with three sets of corresponding chromaticities derived from the results of experiments conducted under four conditions of chromatic adaptation. The results are in reasonable agreement with those obtained by Helson et al. [Illum. Eng. 47, 221–233 (1952)] and Lam and Rigg [Ph.D. thesis, University of Bradford (1985)]. All results indicate that the Bradford and Hunt-ACAM transformations perform the best and the second best, respectively, of all the selected formulae. The current CIE recommendation does not perform as well as expected.
Rafael Maia - One of the best experts on this subject based on the ideXlab platform.
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pavo 2 new tools for the spectral and spatial analysis of Colour in r
Methods in Ecology and Evolution, 2019Co-Authors: Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. WhiteAbstract:Author(s): Maia, Rafael; Gruson, Hugo; Endler, John; White, Thomas | Abstract: Abstract Biological Colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in Colour-based phenotypes has driven, and been driven by, improved techniques for Quantifying Colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2 , the latest iteration of the R package pavo . This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of Colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of Colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (Colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2 offers a flexible and reproducible environment for the analysis of Colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.
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pavo 2 0 new tools for the spectral and spatial analysis of Colour in r
bioRxiv, 2018Co-Authors: Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. WhiteAbstract:Biological Colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in Colour-based phenotypes has driven, and been driven by, improved techniques for Quantifying Colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2.0, the latest iteration of the R package pavo. This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of Colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2.0 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of Colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (Colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2.0 offers a flexible and reproducible environment for the analysis of Colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.
Stephen A. R. Scrivener - One of the best experts on this subject based on the ideXlab platform.
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Quantifying Colour appearance. part V. simultaneous contrast
Color Research & Application, 1995Co-Authors: M. Ronnier Luo, X. W. Gao, Stephen A. R. ScrivenerAbstract:Experiments were carried out to investigate the effect of simultaneous contrast on Colour appearance by varying the lightness, Colourfulness, and hue of an induction field surrounding a test Colour. A total of 814 test/surround combinations were displayed on high-resolution Colour displays. Each was assessed by a panel of five to six observers using a magnitude estimation technique. the results indicate that Colours presented on a computer display are affected by simultaneous contrast in a similar way to surface Colours. All three Colour appearance parameters studied (i.e., lightness, Colourfulness, and hue) are affected and these effects are summarized. In general, the results support and add to the findings of the other studies. the Hunt Colour appearance model was tested and gave a somewhat poor prediction to this data set. Further modifications are required to improve its performance. © 1995 John Wiley & Sons, Inc.
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Quantifying Colour appearance. part IV. Transmissive media
Color Research & Application, 1993Co-Authors: M. Ronnier Luo, X. W. Gao, Peter A. Rhodes, John Haozhong Xin, A.a. Clarke, Stephen A. R. ScrivenerAbstract:The experimental data from this study extends the LUTCHI Colour-appearance data to cover transmissive media. Two further experiments were carried out: one used a large cut-sheet transparency viewed using a back-lit illuminator, and another used a 35-mm slide projected onto a white screen. These new data were used to reveal the changes in Colour appearance caused by different viewing parameters studied, and to evaluate the predictive accuracy of five uniform Colour spaces and Colour-appearance models. The results show that Hunt's 91 model (developed to fit earlier experimental results) did not perform as well as it did in the previous studies using nontransmissive media. This implies that there are large differences in perceived Colour appearance between transmissive and nontransmissive media viewing conditions. Some modifications were subsequently made to Hunt's 91 model and the predictive accuracy was greatly improved.
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Quantifying Colour appearance. part III. Supplementary LUTCHI Colour appearance data
Color Research & Application, 1993Co-Authors: M. Ronnier Luo, X. W. Gao, Peter A. Rhodes, John Haozhong Xin, A.a. Clarke, Stephen A. R. ScrivenerAbstract:The experimental data from this study supplements the LUTCHI Colour Appearance Data as described in Part I of this paper. Two further experiments were carried out: one was to check conflicting results found previously, and another was to extend the range of luminance conditions used earlier. In addition, a brightness attribute was added to the original lightness, Colourfulness, and hue scales for Colour assessment. Experiment I results verified the uncertainties found previously in the comparison between luminous and nonluminous Colours, and between the grey background results with and without a white border. Experiment II results revealed the changes in four perceived attributes under six quite different adapting luminances. The results were then used to test five uniform Colour spaces and Colour-appearance models used in Part II of this paper. Hunt's 91 model gave more accurate predictions of the experimental visual results, in comparison with the other spaces and models. Its predictive error for all attributes studied is within the accuracy of the typical observer.
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Quantifying Colour appearance. Part I. Lutchi Colour appearance data
Color Research & Application, 1991Co-Authors: M. Ronnier Luo, Stephen A. R. Scrivener, Peter A. Rhodes, A.a. Clarke, André Schappo, Chris J. TaitAbstract:The work described here forms part of a research project entitled Predictive Perceptual Colour Models. The aim of this project is to develop a Colour appearance model capable of predicting changes of Colour appearance under various different viewing conditions. This will provide industry with a quantitative measure for assessing the quality of Colour reproduction and enable more rapid and accurate proofing simulations in the graphic art industry. A large-scale experiment has been carried out in which Colour appearance was assessed under a wide range of viewing conditions. The parameters studied were (1) D65, D50, white fluorescent, and tungsten light sources, (2) luminance levels of about 40 and 240 cd/m2, (3) five background conditions: white, grey, black, grey with white border, and grey with black border, and (4) two media: luminous Colours (displayed on a high-resolution Colour monitor) and nonluminous Colours (presented in a viewing cabinet). Each Colour was assessed by a panel of six or seven observers using a magnitude estimation method. In total, 43,332 estimations were made, and these form the LUTCHI Colour Appearance Data. Data analysis has been carried out to examine the reliability of the experimental results and to understand the effects of the various viewing parameters studied. (Part II of this article describes how the LUTCHI Colour Appearance Data has been used to test the performance of various Colour spaces and models.
Hugo Gruson - One of the best experts on this subject based on the ideXlab platform.
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pavo 2 new tools for the spectral and spatial analysis of Colour in r
Methods in Ecology and Evolution, 2019Co-Authors: Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. WhiteAbstract:Author(s): Maia, Rafael; Gruson, Hugo; Endler, John; White, Thomas | Abstract: Abstract Biological Colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in Colour-based phenotypes has driven, and been driven by, improved techniques for Quantifying Colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2 , the latest iteration of the R package pavo . This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of Colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of Colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (Colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2 offers a flexible and reproducible environment for the analysis of Colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.
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pavo 2 0 new tools for the spectral and spatial analysis of Colour in r
bioRxiv, 2018Co-Authors: Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. WhiteAbstract:Biological Colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in Colour-based phenotypes has driven, and been driven by, improved techniques for Quantifying Colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2.0, the latest iteration of the R package pavo. This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of Colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2.0 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of Colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (Colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2.0 offers a flexible and reproducible environment for the analysis of Colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.
