scholarly journals Transformations from cone responses to opponent color spaces

2021 ◽  
Author(s):  
Meiyue Liu ◽  
Cheng Gao ◽  
Xiaohui Zhang ◽  
Manuel Melgosa ◽  
Kaida Xiao ◽  
...  
Keyword(s):  
Color Spaces ◽  
Opponent Color

scholarly journals Geometrical structure of perceptual color space: mental representations and adaptation invariance

2018 ◽  
Author(s):  
Robert J Ennis ◽  
Qasim Zaidi
Keyword(s):  
Mental Representation ◽  
Neural Coding ◽  
Geometrical Structure ◽  
Color Space ◽  
Physical Space ◽  
Affine Geometry ◽  
Representation Space ◽  
Perceptual Space ◽  
Color Spaces ◽  
Opponent Color

AbstractA central issue in neuroscience is to understand how the brain builds structured representations of percepts that facilitate useful inferences about the world. Similarity between percepts is used to accomplish many everyday tasks, e.g. object identification, so is widely used to construct geometrical spaces that represent stimulus qualities, but the intrinsic validity of the geometry is not tested critically. We introduce an experimental approach to equating relative similarities by setting perceived midpoints between pairs of stimuli. Midpoint settings are used with Varignon’s Theorem to test the intrinsic geometry of a representation space, and its mapping to a physical space of stimuli. For perceptual color space, we demonstrate that geometrical structure depends on the mental representation used in judging similarity: an affine geometry is valid only when observers use an opponent-color mental representation. An affine geometry implies that similarity can be judged within straight lines and across parallel lines, and its neural coding could involve ratios of responses. We show that this perceptual space is invariant to changes in illumination color, providing a formal justification to generalize to all of color space, color constancy results measured for color categories. Our midpoint measurements deviate significantly from midpoints in the “uniform” color spaces CIELAB and CIELUV, used extensively in industry and research, so these spaces do not provide adequate metric representation of perceived colors. Our paradigm can thus test for intrinsic geometrical assumptions underlying the representation space for many perceptual modalities, and for the extrinsic perceptual geometry of the space of physical stimuli.SignificanceMathematical spaces based on similarity judgments are widely used to represent stimulus qualities in perception, cognition and neuroscience. We introduce a perceptual approach to equate relative similarities, and use them to test the geometry of a perceptual space and its mapping to a physical space of stimuli. For color perception, our results show that perceptual geometry depends on the mental representation used in judging similarity, and it has an affine structure when observers use an opponent-color representation. An affine geometry implies that neural coding of similarity could involve simple ratios of responses. Our measurements also reveal that the uniform color spaces CIELAB and CIELUV, used extensively in industrial applications, do not provide adequate representation of similarity between moderately spaced colors.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Fusion Based Edge Detection and Segmentation of Color Spaces

2017 ◽  
Author(s):  
Prof. S. H. Jawale ◽  
Prof. A. B. Bavaskar
Keyword(s):  
Edge Detection ◽  
Color Spaces

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

scholarly journals A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries

2021 ◽  
Vol 13 (5) ◽  
pp. 939
Author(s):  
Yongan Xue ◽  
Jinling Zhao ◽  
Mingmei Zhang
Keyword(s):  
Color Space ◽  
Cultivated Land ◽  
Color Spaces ◽  
Region Merging ◽  
Watershed Segmentation ◽  
Coverage Area ◽  
Time Efficiency ◽  
Image Contrast Enhancement ◽  
Segmentation Accuracy ◽  
Rgb Color Space

To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.

Relevance of Color spaces and Color channels in performing Image dehazing

2021 ◽  
Author(s):  
Sriparna Banerjee ◽  
Pritam Kumar Ghosh ◽  
Pranay Kumar Singha ◽  
Swati Chowdhuri ◽  
Sheli Sinha Chaudhuri
Keyword(s):  
Color Spaces ◽  
Image Dehazing ◽  
Color Channels

scholarly journals Color spaces of safflower (Carthamus tinctorius L.) for quality assessment

2016 ◽  
Vol 3 (3) ◽  
pp. 168-175 ◽  
Author(s):  
Manfei Xu ◽  
Chenzhao Du ◽  
Na Zhang ◽  
Xinyuan Shi ◽  
Zhisheng Wu ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Carthamus Tinctorius ◽  
Color Spaces
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