Acne Scan - Application In Assessment Of Acne And Supports Treatment Road

As society develops, many aspects of life are concerned by people, including facial skincare, avoiding acne-related diseases. In this work, we will propose a complete solution for treating acne at home, including 4 processors. First, the anomaly detector uses image processing techniques by Multi-Threshold and Color Segmentation, depending on each color channel corresponding to each type of acne. The sensitivity of the detector is 89.4%. Second, the set of anomalies classifiers into 6 main categories, including 4 major acne types and 2 non-acne types. By applying the convolutional neural model, the accuracy, sensitivity, and F1 are 84.17%, 81.5%, and 82%, respectively. Third, the acne status assessment kit is based on the mGAGS method to classify the condition of a face as mild, moderate, severe, or very severe with an accuracy of 81.25%. Finally, the product recommender, which generalizes from the results of the previous processors with an accuracy of 70-90%. This is the premise that helps doctors as well as general users to evaluate the level of acne on a face effectively and save time.

Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.

Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

The effect of hidden-color channel on the structure of strangeness S = −1 dibaryon

The study of dibaryons has gained extensive attention both theoretically and experimentally since the confirmation of six-quark exotic [Formula: see text] dibaryon (with spin 3 and isospin 0) by recent COSY experiment. We ever proposed the chiral SU(3) quark model and predicted the binding energy of the [Formula: see text] system, in which the hidden-color channel was shown to play an important role in its structure. In this work, we further study the structure of [Formula: see text] dibaryon (with spin 0 and isospin [Formula: see text]) and strangeness [Formula: see text] under the chiral SU(3) quark model. The results show that hidden-color channel has an obvious influence on the binding energy of [Formula: see text] system.

A Proximity Ligation Method to Detect Proteins Bound to Single-Stranded DNA after DNA End Resection at DNA Double-Strand Breaks

After a DNA double-strand break, cells utilize either non-homologous end joining or homologous recombination to repair the broken DNA ends. Homologous recombination requires extensive nucleolytic processing of one of the DNA strands, resulting in long stretches of 3′ single-strand DNA overhangs. Typically, single-stranded DNA is measured using immunofluorescence microscopy to image the foci of replication protein A, a single-stranded DNA-binding protein. Microscopy analysis of bromodeoxyuridine foci under nondenaturing conditions has also been used to measure single-stranded DNA. Here, we describe a proximity ligation assay which uses genome-wide bromodeoxyuridine incorporation to label single-stranded DNA in order to measure the association of a protein of interest with single-stranded DNA. This method is advantageous over traditional foci analysis because it is more direct and specific than traditional foci co-localization microscopy methods, uses only one color channel, and can reveal protein-single-stranded DNA interactions that are rare and potentially undetectable using traditional microscopy methods. We show here the association of replication protein A and bromodeoxyuridine as proof-of-concept.

“Blue Sky Effect”: Contextual Influences on Pupil Size During Naturalistic Visual Search

Pupil size is influenced by cognitive and non-cognitive factors. One of the strongest modulators of pupil size is scene luminance, which complicates studies of cognitive pupillometry in environments with complex patterns of visual stimulation. To help understand how dynamic visual scene statistics influence pupil size during an active visual search task in a visually rich 3D virtual environment (VE), we analyzed the correlation between pupil size and intensity changes of image pixels in the red, green, and blue (RGB) channels within a large window (~14 degrees) surrounding the gaze position over time. Overall, blue and green channels had a stronger influence on pupil size than the red channel. The correlation maps were not consistent with the hypothesis of a foveal bias for luminance, instead revealing a significant contextual effect, whereby pixels above the gaze point in the green/blue channels had a disproportionate impact on pupil size. We hypothesized this differential sensitivity of pupil responsiveness to blue light from above as a “blue sky effect,” and confirmed this finding with a follow-on experiment with a controlled laboratory task. Pupillary constrictions were significantly stronger when blue was presented above fixation (paired with luminance-matched gray on bottom) compared to below fixation. This effect was specific for the blue color channel and this stimulus orientation. These results highlight the differential sensitivity of pupillary responses to scene statistics in studies or applications that involve complex visual environments and suggest blue light as a predominant factor influencing pupil size.

Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.

Singular Spectrum Analysis for Background Initialization with Spatio-Temporal RGB Color Channel Data

In video processing, background initialization aims to obtain a scene without foreground objects. Recently, the background initialization problem has attracted the attention of researchers because of its real-world applications, such as video segmentation, computational photography, video surveillance, etc. However, the background initialization problem is still challenging because of the complex variations in illumination, intermittent motion, camera jitter, shadow, etc. This paper proposes a novel and effective background initialization method using singular spectrum analysis. Firstly, we extract the video’s color frames and split them into RGB color channels. Next, RGB color channels of the video are saved as color channel spatio-temporal data. After decomposing the color channel spatio-temporal data by singular spectrum analysis, we obtain the stable and dynamic components using different eigentriple groups. Our study indicates that the stable component contains a background image and the dynamic component includes the foreground image. Finally, the color background image is reconstructed by merging RGB color channel images obtained by reshaping the stable component data. Experimental results on the public scene background initialization databases show that our proposed method achieves a good color background image compared with state-of-the-art methods.

Possibility of using satellite-based monitoring for large-scale mapping and research of dynamics of mud volcanic landscapes

Currently Earth remote probing to study vegetation dynamics and monitor volcanic activity is of great scientific interest. The purpose of this study is to create a large-scale outline map of Yuzhno-Sakhalinsk mud volcano which will include the topography objects, mud fields of eruptions of various years and gryphons, and to perform semi-automatic classification of Yuzhno-Sakhalinsk mud volcano. Work was performed with QGIS software using the following modules: «QuickMapServices», «Freehandrastergeoreference», «LatLanTools», and «Semi-AutomaticClassificationPlugin». We developed an outline map of Yuzhno-Sakhalinsk mud volcano on a scale of 1:10000, which shows how the mud flows have changed directions over the last 70 years, as well as mud fields have been formed over the last 20 years. Using semi-automatic classification of satellite images from Sentinel-2A satellite in various color channel sets, we obtained two premaps of Yuzhno-Sakhalinsk mud volcano vegetation on a scale of 1:50 000. Satellite monitoring of YuSMV activity allows us to track the eruptive activity of the volcano, and assess its impact on vegetation.

Discrimination of tomato seeds belonging to different cultivars using machine learning

This study was aimed at developing the discriminant models for distinguishing the tomato seeds based on texture parameters of the outer surface of seeds calculated from the images (scans) converted to individual color channels R, G, B, L, a, b, X, Y, Z. The seeds of tomatoes ‘Green Zebra’, ‘Ożarowski’, ‘Pineapple’, Sacher F1 and Sandoline F1 were discriminated in pairs. The highest results were observed for models built based on sets of textures selected individually from color channels R, L and X and sets of textures selected from all color channels. In all cases, the tomato seeds ‘Green Zebra’ and ‘Ożarowski’ were discriminated with the highest average accuracy equal to 97% for the Multilayer Perceptron classifier and 96.25% for Random Forest for color channel R, 95.25% (Multilayer Perceptron) and 95% (Random Forest) for color channel L, 93% (Multilayer Perceptron) and 95% (Random Forest) for color channel X, 99.75% (Multilayer Perceptron) and 99.5% (Random Forest) for a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, X). The highest average accuracies for other pairs of cultivars reached 98.25% for ‘Ożarowski’ vs. Sacher F1, 95.75% for ‘Pineapple’ vs. Sandoline F1, 97.5% for ‘Green Zebra’ vs. Sandoline F1, 97.25% for Sacher F1 vs. Sandoline F1 for models built based on textures selected from all color channels. The obtained results may be used in practice for the identification of cultivar of tomato seeds. The developed models allow to distinguish the tomato seed cultivars in an objective and fast way using digital image processing. The results confirmed the usefulness of texture parameters of the outer surface of tomato seeds for classification purposes. The discriminative models allow to obtain a very high probability and may be applied to authenticate and detect seed adulteration.