Remote Network Monitoring Technology Based on Computer Technology

With the rapid development of Internet technology, using images to express the characteristics of things more direct, compared with text, audio, image expression content is more ambiguous, which makes the rapid increase of digital images on the Internet. Nowadays one of the hot directions of computer vision research is how to accurately and quickly retrieve the target image from a large amount of image data. This paper summarizes the development of image retrieval technology at home and abroad, and proposes an image search method based on color histogram and Chi-square distance. This paper discusses how to construct an image search system, which can search the image quickly, describe the color distribution of the photo with color histogram, divide the image into five regions, extract image features from the color histogram of each region, and then get the data set of multi-dimensional image features. Then the chi-square distance is used to calculate the similarity of color histogram, and the closest image is selected as the first similar image, which realizes the necessary logic of receiving query image and returning related results.

Reproducing the UVJ Color Distribution of Star-forming Galaxies at 0.5 < z < 2.5 with a Geometric Model of Dust Attenuation

We analyze the distribution of rest-frame U − V and V − J colors for star-forming galaxies at 0.5 < z < 2.5. Using stellar population synthesis, stochastic star formation histories, and a simple prescription for the dust attenuation that accounts for the shape and inclination of galaxies, we construct a model for the distribution of galaxy colors. With only two free parameters, this model is able to reproduce the observed galaxy colors as a function of redshift and stellar mass remarkably well. Our analysis suggests that the wide range of dust attenuation values measured for star-forming galaxies at a given redshift and stellar mass is almost entirely due to the effect of inclination; if all galaxies at a given stellar mass were observed edge-on, they would show very similar dust attenuation. This result has important implications for the interpretation of dust attenuation measurements, the treatment of UV and IR luminosity, and the comparison between numerical simulations and observations.

StainNet: A Fast and Robust Stain Normalization Network

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.

Highlighted Document Image Classification

There are many existing document image classification researches, but most of them are not designed for use in constrained computer resources, like printers, or focused on documents with highlighter pen marks. To enable printers to better discriminate highlighted documents, we designed a set of features in CIE Lch(a* b*) space to use along with the support vector machine. The features include two gamut-based features and six low-level color features. By first identifying the highlight pixels, and then computing the distance from the highlight pixels to the boundary of the printer gamut, the gamut-based features can be obtained. The low-level color features are built upon the color distribution information of the image blocks. The best feature subset of the existing and new features is constructed by sequential forward floating selection (SFFS) feature selection. Leave-one-out cross-validation is performed on a dataset with 400 document images to evaluate the effectiveness of the classification model. The cross-validation results indicate significant improvements over the baseline highlighted document classification model.

The Prevalence and Extent of Physiological and Pathological Gingival Pigmentation in Patients Visiting Riyadh Elm University Clinics

Background: Gingival pigmentation presents as a diffuse deep discoloration or as irregularly shaped brown and light brown or black patches, striate, or strands. It is generally agreed that pigmented areas are solely pre-sent when melanin granules, synthesized by melanocytes, are transferred to keratinocytes. Aim: To study the prevalence, extent, and etiology of gingival pigmentation among Riyadh Elm University clin-ics’ attendees. Design and Settings: Examinations were done in Riyadh Elm University by four calibrated examiners. Methods: Patients underwent a Gingival Index examination, followed by an assessment of the presence or ab-sence of gingival pigmentations. If the latter were present, the patient underwent a Hedin’s Melanin Index, a Gingival Melanosis Record, and a Von Luschan Scale examination to evaluate the extent, distribution, and col-or of the pigmentation. Statistical analysis: Cohen’s Kappa Test and Chi-Square Test Results: A total of 139 (80.3%) patients had gingival pigmentation, of which 79 (56.8%) were males. Gingival pigmentations were found in 42 (93.3%) cigarette smokers, 40 (83.3%) hubbly bubbly smokers, and 20 (86.9%) electronic cigarette smokers. Both arches were affected in 102 patients, the canines’ area had the highest incidence of pigmentation (88.9%) while the molars had the least incidence (18.1%). Scores of 2 and 3 on the Hedin’s Index were the highest at 51 and 52 patients respectively. The mean color of the pigmentations on the Von Luschan scale was 21.49 ± 4.59, but it was higher for smokers and patients with severe inflamma-tion. Conclusions : Gingival pigmentation was more prevalent among all types of smokers—cigarettes, hubbly bubbly, and e-cigarettes compared to non-smokers. More pigmentations were also associated with the higher intake. The major patterns of pigmentation distribution were the short-connected-continuous ribbons and the more-than-two-solitary-papillae. Keywords: Gingival Pigmentation, Hedin’s Index, Color, Distribution, Smokers

Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques

(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification.

Atmospheric Correction of True-Color RGB Imagery with Limb Area-Blending Based on 6S and Satellite Image Enhancement Techniques Using Geo-Kompsat-2A Advanced Meteorological Imager Data

This study aims for producing high-quality true-color red-green-blue (RGB) imagery that is useful for interpreting various environmental phenomena, particularly for GK2A. Here we deal with an issue that general atmospheric correction methods for RGB imagery might be breakdown at high solar/viewing zenith angle of GK2A due to erroneous atmospheric path lengths. Additionally, there is another issue about the green band of GK2A of which centroid wavelength (510 nm) is different from that of natural green band (555 nm), resulting in the unrealistic RGB imagery. To overcome those weakness of the RGB imagery for GK2A, we apply the second simulation of the satellite signal in the solar spectrum radiative transfer model look-up table with improved information considering altitude of the reflective surface to reduce the exaggerated atmospheric correction, and a blending technique that mixed the true-color imagery before and after atmospheric correction which produced a naturally expressed true-color image. Consequently, the root mean square error decreased by 0.1–0.5 in accordance with the solar and view zenith angles. The green band signal was modified by combining it with a veggie band to form hybrid green which adjust centroid wavelength of approximately 550 nm. The original composite of true-color RGB imagery is dark; therefore, to brighten the imagery, histogram equalization is conducted to flatten the color distribution. High-temporal-resolution true-color imagery from the GK2A AMI have significant potential to provide scientists and forecasters as a tools to visualize the changing Earth and also expected to intuitively understand the atmospheric phenomenon to the general public.

Guided Facial Skin Color Correction

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.

Development of a City-Scale Approach for Façade Color Measurement with Building Functional Classification Using Deep Learning and Street View Images

Precise measuring of urban façade color is necessary for urban color planning. The existing manual methods of measuring building façade color are limited by time and labor costs and hardly carried out on a city scale. These methods also make it challenging to identify the role of the building function in controlling and guiding urban color planning. This paper explores a city-scale approach to façade color measurement with building functional classification using state-of-the-art deep learning techniques and street view images. Firstly, we used semantic segmentation to extract building façades and conducted the color calibration of the photos for pre-processing the collected street view images. Then, we proposed a color chart-based façade color measurement method and a multi-label deep learning-based building classification method. Next, the field survey data were used as the ground truth to verify the accuracy of the façade color measurement and building function classification. Finally, we applied our approach to generate façade color distribution maps with the building classification for three metropolises in China, and the results proved the transferability and effectiveness of the scheme. The proposed approach can provide city managers with an overall perception of urban façade color and building function across city-scale areas in a cost-efficient way, contributing to data-driven decision making for urban analytics and planning.

Study of the changes of the distribution of spectral properties of S-type asteroidal dynamical families with age: Mean values and Skewness.

&lt;p&gt;Correlations between family-age and the mean value of slope of the spectral distribution, caused by the cumulative effect of cosmic irradiation, have been established for S-type dynamical families by many authors. We noticed that if there is a variety in the primordial surface composition, then the typical timescale that determines the speed of this evolution is bound to have a range of values. Consequently, as the mean value of the color distribution tends to steeper (redder) slopes, a progressive skewness in this distribution should develop. Using SDSS-MOC-4 colors and NEOWISE albedos, we cross-examined the S-type families members as defined by both Nesvorny et al. (2015) and Spotto et al (2015) and retained only members with albedos and colors in the characteristic range of the S-types. We corroborate the color evolution with age and we compare our results with previous estimations. Using only the &quot;true S-type&quot; family members, we also find a significative correlation between some particular skewness-estimation parameters and age. Our results offer additional evidence of the effects of cosmic-radiation on asteroidal surfaces and may provide possible new relations to determine the age of S-type dynamical families.&amp;#160;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Nesvorny D.(2015). NASA Planetary Data System, id. EAR-A-VARGBDET-5-NESVORNYFAM-V3.0&lt;/p&gt; &lt;p&gt;Spoto, F.; Milani, A.; Kne&amp;#382;evi&amp;#263;, Z. (2015). Asteroid family ages. Icarus, Volume 257, p. 275-289.&lt;/p&gt;