What is the “opposite” of “blue”? The language of colour wheels

© Society for Imaging Science and Technology 2019 A color wheel is a tool for ordering and understanding hue. Different color wheels differ in the spacing of the colors around the wheel. The opponent-color theory, Munsell's color system, the standard printer's primaries, the artist's primaries, and Newton's rainbow all present different variations of the color wheel. I show that some of this variation is owing to imprecise use of language, based on Berlin and Kay's theory of basic color names. I also show that the artist's color wheel is an outlier that does not match well to the technical color wheels because its principal colors are so strongly connected to the basic color names.

What is the “opposite” of “blue”? The language of colour wheels

© Society for Imaging Science and Technology 2019 A color wheel is a tool for ordering and understanding hue. Different color wheels differ in the spacing of the colors around the wheel. The opponent-color theory, Munsell's color system, the standard printer's primaries, the artist's primaries, and Newton's rainbow all present different variations of the color wheel. I show that some of this variation is owing to imprecise use of language, based on Berlin and Kay's theory of basic color names. I also show that the artist's color wheel is an outlier that does not match well to the technical color wheels because its principal colors are so strongly connected to the basic color names.

Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each image in the training set is calculated through feature extraction and description, image registration, and pose map optimization. Then, in the image retrieval stage, the training set and the query set are clustered to generate the vector of local aggregated descriptors (VLAD) description vector. In order to overcome the problem that the description vector loses the image color information and improve the retrieval accuracy under different lighting conditions, the opponent color information and depth information are added to the description vector for retrieval. Finally, using the point cloud corresponding to the retrieval result image and its pose, the pose of the retrieved image is calculated by perspective-n-point (PnP) method. The results of indoor scene positioning under different illumination conditions show that the proposed method not only improves the positioning accuracy compared with the original VLAD and ORB-SLAM2, but also has high computational efficiency.

A new transformation of cone responses to opponent color responses

It is widely agreed that the color vision process moves quickly from cone receptors to opponent color cells in the retina and lateral geniculate nucleus. Many workers have proposed the transformation or coding of long, medium, short (LMS) cone responses to r − g, y − b opponent color chromatic responses (unique hues) on the following basis: That L, M, S cones represent Red, Green, and Blue hues, with Yellow represented by (L + M), while r − g and y − b represent the opponent pairs of unique hues. The traditional coding from cones to opponent colors is that L − M gives r − g, while (L + M) − S gives y − b. This convention is open to several criticisms, and a new coding is required. A literature search produced 16 studies of cone responses LMS and 15 studies of spectral (i.e., ygb) opponent color chromatic responses, in terms of response wavelength peaks. Comparative analysis of the two sets of studies shows the means are almost identical (within 3 nm; i.e., L = y, M = g, S = b). Further, the response curves of LMS are very similar shapes to ygb. In sum, each set can directly transform to the other on this proposed coding: (S + L) − M gives r − g, while L − S gives y − b. This coding activates neural operations in the cardinal directions r − g and y − b.

Practical Color Contrast Sensitivity Functions for Luminance Levels up to 10000 cd/m2

We model color contrast sensitivity for Gabor patches as a function of spatial frequency, luminance and chromacity of the background, modulation direction in the color space and stimulus size. To fit the model parameters, we combine the data from five independent datasets, which let us make predictions for background luminance levels between 0.0002 cd/m2 and 10 000 cd/m2, and for spatial frequencies between 0.06 cpd and 32 cpd. The data are well-explained by two models: a model that encodes cone contrast and a model that encodes postreceptoral, opponent-color contrast. Our intention is to create practical models, which can well explain the detection performance for natural viewing in a wide range of conditions. As our models are fitted to the data spanning very large range of luminance, they can find applications in modeling visual performance for high dynamic range and augmented reality displays.

Viable Yeast Identification using Bag of Visual Words in Colored images

In this research it is reported a system to automate the process of identification of viable yeasts whose population control is a crucial task in the ethanol production process. The identification and counting of yeasts made by human vision under a light microscope, is repetitive and susceptible to errors. We used computer vision techniques such as BoVW, Color Coherence Vectors (CCV), Color Moments (CM), Bag-of-Color (BoC) and Opponent Color (OpC) were applied for extracting characteristics that were classified by the Naive Bayes, KNN, SVM and J48 algorithms in 2614 images of yeasts separated into three classes: viable, non-viable and background. The results were analyzed using software R, which in the ANOVA test resulted in a p value equal to 2e􀀀16 indicating a significant difference between the techniques. The OPC with SVM classifier showed the highest performance using the PCC Percent Correct Classification metric, about 95% compared to other techniques.

What is the “opposite” of “blue”? The language of colour wheels

© Society for Imaging Science and Technology 2019 A color wheel is a tool for ordering and understanding hue. Different color wheels differ in the spacing of the colors around the wheel. The opponent-color theory, Munsell's color system, the standard printer's primaries, the artist's primaries, and Newton's rainbow all present different variations of the color wheel. I show that some of this variation is owing to imprecise use of language, based on Berlin and Kay's theory of basic color names. I also show that the artist's color wheel is an outlier that does not match well to the technical color wheels because its principal colors are so strongly connected to the basic color names.