scholarly journals Influence of the Smile Line on Smile Attractiveness in Short and Long Face Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Amjad Al Taki ◽  
Thar Hayder Mohammed ◽  
Ahmad Mohammad Hamdan
Keyword(s):  
Image Processing ◽  
Group Differences ◽  
Pairwise Comparisons ◽  
Attractiveness Rating ◽  
Facial Height ◽  
The Impact ◽  
Dental Professionals

Objectives. The study assessed the impact of facial height on attractiveness of smile, in association with the maxillary gingival display. This research was performed by dental professionals and laypersons.Materials and Methods. Frontal extraoral photographs were captured for both short and long faces. The photographs were modified using software for image-processing and three rater groups (orthodontists, dentists, and laypersons) evaluated the smile attractiveness, with 30 subjects in each group. Differences in ratings of the different smiles among the different experimental groups were examined using the Kruskal-Wallis test. The Mann–WhitneyUtest was performed for pairwise comparisons between the experimental groups.Results. Dentists and laypeople were most likely to agree. For the short face, laypeople and dentists both rated the +2 mm gingival display smile as the most attractive smile whilst orthodontists ranked the 0 mm gingival display smile as the most attractive smile. For the long face, laypeople and dentists ranked the 0 mm gingival display smile as the most attractive smile, whilst orthodontists ranked the +2 mm gingival display as the most attractive.Conclusion. Smile line of both short and long face subjects was found to influence the smile attractiveness rating by the three rater groups.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

Social Insecurity: Children and Benefit Dynamics

2006 ◽  
Vol 35 (3) ◽  
pp. 391-410 ◽  
Author(s):  
LUCINDA PLATT
Keyword(s):  
Low Income ◽  
Group Differences ◽  
Unit Of Analysis ◽  
Quarterly Data ◽  
Income Family ◽  
Ethnic Group Differences ◽  
Comprehensive Picture ◽  
Social Insecurity ◽  
Housing Benefit ◽  
The Impact

This article uses administrative data to explore benefit dynamics for children in Britain's second largest city, Birmingham, over the period January 1998 to June 1999. As the benefits in question (housing benefit and council tax benefit) are means tested, the dynamics are also informative about moves in and out of low income. The article is original in its use of quarterly data to provide a comprehensive picture of benefit dynamics, in treating the child rather than the benefit claimant as the unit of analysis, and also in including ethnic group differences in its analysis of benefit exit and re-entry. It provides a picture of substantial ‘welfare dynamics’: that is, movements in and out of benefit support. Living in a low-income family in receipt of benefit can be seen to be a part, and sometimes a recurring part, of the experience of a large proportion of children. It argues that policy needs to investigate and take account of the impact of insecure income as well as poverty when considering the welfare of children.

Abstract 19771: Persistent Thrombocytopenia after Myocardial Infarction is Associated with Increased Short- and Long-Term Mortality

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Anirudh Kumar ◽  
Salim Virani ◽  
Scott Bassett ◽  
Mahboob Alam ◽  
Ravi Hira ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Bleeding Complications ◽  
Group Differences ◽  
Long Term Outcomes ◽  
Post Discharge ◽  
Short And Long Term ◽  
The Impact ◽  
Long Term Mortality

Background: Thrombocytopenia (TCP) occurs commonly in patients hospitalized with acute myocardial infarction (AMI). It is unclear whether persistent TCP after discharge among AMI survivors is associated with worse outcomes. Methods: We examined the impact of persistent post-discharge TCP on outcomes in a registry of consecutive AMI patients hospitalized between January 2004 and December 2007. In-hospital (IH) TCP was defined by a nadir platelet count < 150 x 109/L. Resolved TCP was defined as IH TCP which resolved within 3 months after discharge while persistent TCP was defined as IH TCP which did not resolve within 3 months. Results: Of 842 patients hospitalized for a first AMI, we examined data on 617 hospital survivors who had follow-up within 3 months of discharge and documented long-term outcomes. Of those, 474 (76.8%) patients did not experience IH TCP while 42 (6.8%) and 101 (16.4%) had persistent and resolved TCP, respectively (Table). Patients with persistent TCP were older, had worse comorbidities, and were more likely to have TCP at baseline and discharge. There were no inter-group differences in infarct size, major bleeding complications, revascularization, or ejection fraction at discharge. Mortality following discharge was higher at all time-points among AMI patients with persistent TCP compared to patients with resolved or without IH TCP (Figure). Patients with resolved TCP had comparable mortality to those without IH TCP. Conclusion: Persistent TCP within 3 months after hospital discharge for AMI is associated with significantly increased short- and long-term mortality compared to patients with recovered TCP or without IH TCP.

The magnitude of neurocognitive impairment is overestimated in depression: the role of motivation, debilitating momentary influences, and the overreliance on mean differences

2022 ◽  
pp. 1-11
Author(s):  
Steffen Moritz ◽  
Jingyuan Xie ◽  
Danielle Penney ◽  
Lisa Bihl ◽  
Niklas Hlubek ◽  
...  
Keyword(s):  
Effect Size ◽  
Neurocognitive Impairment ◽  
Self Report ◽  
Medium Effect ◽  
Group Differences ◽  
Neurocognitive Deficits ◽  
Performance Deficits ◽  
Impact Performance ◽  
The Impact ◽  
Performance Scale

Abstract Background Meta-analyses agree that depression is characterized by neurocognitive dysfunctions relative to nonclinical controls. These deficits allegedly stem from impairments in functionally corresponding brain areas. Increasingly, studies suggest that some performance deficits are in part caused by negative task-taking attitudes such as poor motivation or the presence of distracting symptoms. A pilot study confirmed that these factors mediate neurocognitive deficits in depression. The validity of these results is however questionable given they were based solely on self-report measures. The present study addresses this caveat by having examiners assess influences during a neurocognitive examination, which were concurrently tested for their predictive value on performance. Methods Thirty-three patients with depression and 36 healthy controls were assessed on a battery of neurocognitive tests. The examiner completed the Impact on Performance Scale, a questionnaire evaluating mediating influences that may impact performance. Results On average, patients performed worse than controls at a large effect size. When the total score of the Impact on Performance Scale was accounted for by mediation analysis and analyses of covariance, group differences were reduced to a medium effect size. A total of 30% of patients showed impairments of at least one standard deviation below the mean. Conclusions This study confirms that neurocognitive impairment in depression is likely overestimated; future studies should consider fair test-taking conditions. We advise researchers to report percentages of patients showing performance deficits rather than relying solely on overall group differences. This prevents fostering the impression that the majority of patients exert deficits, when in fact deficits are only true for a subgroup.

Photogrammetric Investigation of the Flying Shape of Spinnakers in a Twisted Flow Wind Tunnel

2009 ◽  
Author(s):  
Kai Graf ◽  
Olaf Müller
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
High Resolution ◽  
Wind Tunnel ◽  
Wind Velocity ◽  
Digital Cameras ◽  
Pattern Recognition Methods ◽  
Twisted Flow ◽  
High Resolution Images ◽  
The Impact

This paper describes a method for the acquisition of the flying shape of spinnakers in a twisted flow wind tunnel. The method is based on photogrammetry. A set of digital cameras is used to obtain high resolution images of the spinnaker from different viewing angles. The images are post-processed using image-processing tools, pattern recognition methods and finally the photogrammetry algorithm. Results are shown comparing design versus flying shape of the spinnaker and the impact of wind velocity and wind twist on the flying shape. Finally some common rules for optimum spinnaker trimming are investigated and examined.

Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net

2021 ◽  
Author(s):  
Shidong Li ◽  
Jianwei Liu ◽  
Zhanjie Song
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
The Impact

Abstract Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-ofinterest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽  
2017 ◽  
pp. 382-402
Author(s):  
Petre Anghelescu
Keyword(s):  
Image Processing ◽  
Cellular Automata ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Understanding ◽  
Software Application ◽  
Detection Algorithms ◽  
Human Image ◽  
The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

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