Knowledge and Awareness of Artificial Food Colours and its Impact on Nutrition and Health Among Adolescent Population

Food serves as the primary factor for human survival. Various types of foods are consumed by different people of various localities. For children and adolescents, the food is consumed predominantly by its colour. Unfortunately, the natural colours of food are not maintained as such and are altered by using several non-permissible artificial food colour additives. A large body of laboratory research has demonstrated that changing the intensity/saturation of the colour of food and beverage items can exert a sometimes dramatic impact on the expectations, and hence on the subsequent experiences, of consumers (or participants in the lab). However, should the colour not match the taste, then the result may well be a negatively valenced disconfirmation of expectation. The study was designed to find the impact of colour of food in nutrition consumption among 100 participants. In our study, 58% male and 42% female participated, out of them 60% are undergraduates, 29% are graduates, 8% are postgraduates and 3% are uneducated. 75% of the population says colour of food affects nutritional consumption. 63% answered nutritional value of food is dependent upon its colour. The study found that the colour of food can cause an impact on nutritional consumption of health among the adolescent population. As prevention is better than cure, it is essential to implement certain rules or law to evaluate the frequency of using toxic non permitted colour as well as permitted colours and also to prevent ill effects on using both synthetic colour (non-permitted colours) and permitted colour above permissible law to improve health among the population.

Color Intensity of Architectural Concrete Depending on the Type of Cement

In this paper, the influence of the cement type on the final coloring of the concrete with colored powdered inorganic pigments used currently with increasing tendency in construction and modern architecture is presented. In the experimental testing, the difference in intensity/saturation of the color of the concrete containing white cement (CEM I 52.5 R - SR 5 white), which is preferred when using color pigments, and gray (conventional) Portland cement (CEM I 52.5 R) from the same manufacturer has been measured and compared. Three colored inorganic powder pigments (red, green and yellow pigment) containing iron oxides (Fe2O3) have been used to color the concrete mixture. Objectivity of measurement, comparison, and subsequent analysis of individual resulting shades has been ensured by measuring exact RGB values at several places of the concrete cured surface by means of the Colorimeter Colorcatch nanodevice which automatically eliminates all irrelevant color shades, shadows, and impurities. This eliminated the subjective color impression which is usually always biased by external circumstances. Comparison of the color intensity of six experimental samples, including three pigment colors and two types of cement (white/grey), is conclusively related to the color efficiency ratio, the resulting costs, and the ecological footprint arising from the production of the cements above.

Highly Accurate Melanoma Detection from Skin Images using Multiple Feature Extraction and Support Vector Machine (SVM)

Digital image processing (DIP) plays a major role in the biomedical image segmentation and classification process. Melanoma detection from skin images is most widely using the application in biomedical imaging. In this paper, a novel algorithm for Melanoma Detection from digital images using multiple feature extraction with a Support Vector Machine (SVM) for highly accurate classification. Multiple features such as color, texture and statistical features are considered to train the SVM. To increase the classification sensitivity, feature extraction is done in (Red, Green, Blue) RGB and (Hue, Intensity, Saturation) HIS color domains. Two separate modules for training and testing is performed with collected sample data by using medical experts. To separate the region from the skin, a cantor-based segmentation technique is used in the gray level component of the input image. The proposed method is tested with various images that are collected from different patients from different places. Form the result validation it is clear that the proposed algorithm can give maximum accuracy of 95% which is best when compared to the conventional classification algorithms

Fractional Contrast Stretching for Image Enhancement of Aerial and Satellite Images

Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. This is usually due to shadowing self-obstruction or light reflection. Existing image enhancement methods fail to improve hidden details and local contrast at the same visualization level. They are not developed to enhance through local dark or light regions simultaneously. Also, the current aerial and satellite image enhancement methods have several limitations. For instance, these include intensity saturation, non-uniform brightness, halo effect, blur edges, and so on. This article introduces a fractional contrast stretching concept for aerial and satellite image enhancement based on a novel automated non-uniform luminance normalization that is not provided by the user as input parameters. The introduced approach contains several new techniques: (i) no reference non-linearly fractional contrast stretching with automatic non-uniform luminance normalization and (ii) non-linearly local contrast stretching for spatial details and edge sharpening. The proposed algorithm was tested on the orthorectified aerial photograph database with a pixel resolution of 1 meter or finer from across the United States during 2000–2016. The simulation results illustrate the efficiency of the proposed algorithm and its advantages for cutting-edge aerial and satellite image enhancement, resulting in visualization quality.

Fractional Contrast Stretching for Image Enhancement of Aerial and Satellite Images

Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. This is usually due to shadowing self-obstruction or light reflection. Existing image enhancement methods fail to improve hidden details and local contrast at the same visualization level. They are not developed to enhance through local dark or light regions simultaneously. Also, the current aerial and satellite image enhancement methods have several limitations. For instance, these include intensity saturation, non-uniform brightness, halo effect, blur edges, and so on. This article introduces a fractional contrast stretching concept for aerial and satellite image enhancement based on a novel automated non-uniform luminance normalization that is not provided by the user as input parameters. The introduced approach contains several new techniques: (i) no reference non-linearly fractional contrast stretching with automatic non-uniform luminance normalization and (ii) non-linearly local contrast stretching for spatial details and edge sharpening. The proposed algorithm was tested on the orthorectified aerial photograph database with a pixel resolution of 1 meter or finer from across the United States during 2000‐2016. The simulation results illustrate the efficiency of the proposed algorithm and its advantages for cutting-edge aerial and satellite image enhancement, resulting in visualization quality.

The spectral identity of foveal cones is preserved in hue perception

Organisms are faced with the challenge of making inferences about the physical world from incomplete incoming sensory information. One strategy to combat ambiguity in this process is to combine new information with prior experiences. We investigated the strategy of combining these information sources in color vision. Single cones in human subjects were stimulated and the associated percepts were recorded. Subjects rated each flash for brightness, hue and saturation. Brightness ratings were proportional to stimulus intensity. Saturation was independent of intensity, but varied between cones. Hue, in contrast, was assigned in a stereotyped manner that was predicted by cone type. These experiments revealed that, near the fovea, long (L) and middle (M) wavelength sensitive cones produce sensations that can be reliably distinguished on the basis of hue, but not saturation or brightness. Taken together, these observations implicate the high-resolution, color-opponent parvocellular pathway in this low-level visual task.