Image Brightness reduction by canceling bright areas using brightness level and reconstruction by geodesic dilation

2021 ◽  
Author(s):  
Edgar Ruben Godoy Liseras ◽  
Julio Cesar Melle-Roman ◽  
Jose Luis Vazquez Noguera ◽  
Horacio Legal-Ayala
Keyword(s):  
Image Brightness ◽  
Brightness Level

Field-Ion Microscopy of BCC Metals: A Study of Image Differences and Topographical Variations

1973 ◽  
Vol 31 ◽  
pp. 114-115
Author(s):  
O. T. Inal ◽  
L. E. Murr
Keyword(s):  
Liquid Nitrogen ◽  
Liquid Nitrogen Temperature ◽  
Surface Atom ◽  
Image Brightness ◽  
Field Ion Microscopy ◽  
Topographic Features ◽  
Bcc Metals ◽  
Electron Orbital ◽  
Ion Microscopy ◽  
Field Ion Microscope

When sharp metal filaments of W, Fe, Nb or Ta are observed in the field-ion microscope (FIM), their appearance is differentiated primarily by variations in regional brightness. This regional brightness, particularly prominent at liquid nitrogen temperature has been attributed in the main to chemical specificity which manifests itself in a paricular array of surface-atom electron-orbital configurations.Recently, anomalous image brightness and streaks in both fcc and bee materials observed in the FIM have been shown to be the result of surface asperities and related topographic features which arise by the unsystematic etching of the emission-tip end forms.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

Quantitative Analysis of Enhanced Selfies in a Young Adult Population

2021 ◽  
Author(s):  
Keon M. Parsa ◽  
Ish A. Talati ◽  
Haijun Wang ◽  
Eugenia Chu ◽  
Lily Talakoub ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Young Adult ◽  
Expression Analysis ◽  
Adult Population ◽  
Mobile App ◽  
Image Brightness ◽  
Young Adult Population ◽  
Digital Copy ◽  
Unrealistic Expectations ◽  
Neutral Expression

AbstractThe use of filters and editing tools for perfecting selfies is increasing. While some aesthetic experts have touted the ability of this technology to help patients convey their aesthetic goals, others have expressed concerns about the unrealistic expectations that may come from the ability for individuals to digitally alter their own photos in these so-called “super-selfies.” The aim of the study is to determine the changes that individuals seek when enhancing selfies. Twenty subjects participated in this study between July 25 and September 24, 2019. Subjects had two sets of headshots taken (neutral and smile) and were provided an introduction on the use of the Facetune2 app. Subjects received a digital copy of their photographs and were asked to download the free mobile app. After 1 week of trialing the different tools for enhancing their appearance, subjects submitted their self-determined most attractive edited photographs. Changes in marginal reflex distance (MRD) 1 and 2, nose height and width, eyebrow height, facial width, skin smoothness, skin hue, and saturation as well as overall image brightness were recorded. Paired two-tailed t-test was used to evaluate pre- and post-facial measurements. There were no statistically significant changes identified in the analysis of the altered photos in neutral expression. Analysis of all smiling photographs revealed that subjects increased their smile angle (right: +2.92 mm, p = 0.04; left: +3.58 mm, p < 0.001). When smiling photographs were assessed by gender, females were found to significantly increase their MRD2 (right: +0.64 mm, p = 0.04; left: +0.74 mm, p = 0.05) and their smile angle (right: +1.90 mm, p = 0.03; left: +2.31 mm, p = 0.005) while also decreasing their nose height (−2.8 mm, p = 0.04). Males did not significantly alter any of the facial measurements assessed. This study identifies the types of changes that individuals seek when enhancing selfies and specifies the different aspects of image adjustment that may be sought based on a patient's gender.

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

scholarly journals A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Color Model ◽  
Low Light ◽  
Image Brightness ◽  
Low Contrast ◽  
Light Sensor ◽  
Hsi Color Model ◽  
Art Research ◽  
Enhancement Algorithm

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

scholarly journals Optimum perceived-brightness level considering the practical use environment for the mobile organic light-emitting diode display

2016 ◽  
Vol 17 (4) ◽  
pp. 151-158 ◽  
Author(s):  
Eunjung Lee ◽  
Seungbae Lee ◽  
Su Young Kim ◽  
Jong-Ho Chong ◽  
Byeong Hwa Choi ◽  
...  
Keyword(s):  
Light Emitting Diode ◽  
Light Emitting ◽  
Organic Light Emitting Diode ◽  
Brightness Level ◽  
Organic Light

scholarly journals The structure of the lower solar chromosphere in undisturbed and active regions

1968 ◽  
Vol 35 ◽  
pp. 255-258 ◽  
Author(s):  
E. Dubov
Keyword(s):  
Active Regions ◽  
Brightness Distribution ◽  
Solar Chromosphere ◽  
Image Brightness ◽  
Observational Material ◽  
Birefringent Filter ◽  
Doppler Shifts

As observational material in this work we used spectroheliograms taken with the Crimean solar tower telescope in K232 and Hα filtergrams taken with the chromospheric telescope in Simeis. The Hα birefringent filter was so adjusted, that by tuning the last polaroid we could take filtergrams in the centre of Hα or combined filtergrams in the two wings at Hα ± 0·5 Å. So the effect of Doppler shifts on image-brightness distribution was diminished. We compared the brightness distribution with that of spectroheliograms.

scholarly journals Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 12
Author(s):  
Wojciech Więcławek ◽  
Marta Danch-Wierzchowska ◽  
Marcin Rudzki ◽  
Bogumiła Sędziak-Marcinek ◽  
Slawomir Jan Teper
Keyword(s):  
Image Quality ◽  
Fluorescein Angiography ◽  
Imaging Modality ◽  
Correction Method ◽  
Correction Function ◽  
Image Brightness ◽  
Geometrical Features ◽  
Noticeable Improvement ◽  
Manual Processing ◽  
Image Quality Measures

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method’s performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.

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