scholarly journals 3D Facial Similarity Measure Based on Geodesic Network and Curvatures

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Junli Zhao ◽  
Cuiting Liu ◽  
Zhongke Wu ◽  
Fuqing Duan ◽  
Minqi Zhang ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Subjective Evaluation ◽  
Similarity Measures ◽  
Weighted Average ◽  
Correlation Coefficients ◽  
Shape Index ◽  
3D Face ◽  
The Mean ◽  
Facial Similarity ◽  
Face Similarity

Automated 3D facial similarity measure is a challenging and valuable research topic in anthropology and computer graphics. It is widely used in various fields, such as criminal investigation, kinship confirmation, and face recognition. This paper proposes a 3D facial similarity measure method based on a combination of geodesic and curvature features. Firstly, a geodesic network is generated for each face with geodesics and iso-geodesics determined and these network points are adopted as the correspondence across face models. Then, four metrics associated with curvatures, that is, the mean curvature, Gaussian curvature, shape index, and curvedness, are computed for each network point by using a weighted average of its neighborhood points. Finally, correlation coefficients according to these metrics are computed, respectively, as the similarity measures between two 3D face models. Experiments of different persons’ 3D facial models and different 3D facial models of the same person are implemented and compared with a subjective face similarity study. The results show that the geodesic network plays an important role in 3D facial similarity measure. The similarity measure defined by shape index is consistent with human’s subjective evaluation basically, and it can measure the 3D face similarity more objectively than the other indices.

scholarly journals Craniofacial Reconstruction Evaluation by Geodesic Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Junli Zhao ◽  
Cuiting Liu ◽  
Zhongke Wu ◽  
Fuqing Duan ◽  
Kang Wang ◽  
...  
Keyword(s):  
Cosmetic Surgery ◽  
Subjective Evaluation ◽  
Weighted Average ◽  
Shape Index ◽  
Local Similarity ◽  
Objective Method ◽  
Widespread Application ◽  
Craniofacial Reconstruction ◽  
Face Model ◽  
Absolute Value

Craniofacial reconstruction is to estimate an individual’s face model from its skull. It has a widespread application in forensic medicine, archeology, medical cosmetic surgery, and so forth. However, little attention is paid to the evaluation of craniofacial reconstruction. This paper proposes an objective method to evaluate globally and locally the reconstructed craniofacial faces based on the geodesic network. Firstly, the geodesic networks of the reconstructed craniofacial face and the original face are built, respectively, by geodesics and isogeodesics, whose intersections are network vertices. Then, the absolute value of the correlation coefficient of the features of all corresponding geodesic network vertices between two models is taken as the holistic similarity, where the weighted average of the shape index values in a neighborhood is defined as the feature of each network vertex. Moreover, the geodesic network vertices of each model are divided into six subareas, that is, forehead, eyes, nose, mouth, cheeks, and chin, and the local similarity is measured for each subarea. Experiments using 100 pairs of reconstructed craniofacial faces and their corresponding original faces show that the evaluation by our method is roughly consistent with the subjective evaluation derived from thirty-five persons in five groups.

3D Face Similarity Measure by Fréchet Distances of Geodesics

2018 ◽  
Vol 33 (1) ◽  
pp. 207-222 ◽  
Author(s):  
Jun-Li Zhao ◽  
Zhong-Ke Wu ◽  
Zhen-Kuan Pan ◽  
Fu-Qing Duan ◽  
Jin-Hua Li ◽  
...  
Keyword(s):  
Similarity Measure ◽  
3D Face ◽  
Face Similarity

scholarly journals Algorithms for Multipolar Interval-Valued Neutrosophic Soft Set with Information Measures to Solve Multicriteria Decision-Making Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Rana Muhammad Zulqarnain ◽  
Imran Siddique ◽  
Aiyared Iampan ◽  
Ebenezer Bonyah
Keyword(s):  
Decision Making ◽  
Similarity Measures ◽  
Weighted Average ◽  
Correlation Coefficients ◽  
Multicriteria Decision Making ◽  
Soft Set ◽  
Multicriteria Decision ◽  
Information Measures ◽  
Decision Making Problem ◽  
Interval Valued

Similarity measures (SM) and correlation coefficients (CC) are used to solve many problems. These problems include vague and imprecise information, excluding the inability to deal with general vagueness and numerous information problems. The main purpose of this research is to propose an m-polar interval-valued neutrosophic soft set (mPIVNSS) by merging the m-polar fuzzy set and interval-valued neutrosophic soft set and then study various operations based on the proposed notion, such as AND operator, OR operator, truth-favorite, and false-favorite operators with their properties. This research also puts forward the concept of the necessity and possibility operations of mPIVNSS and also the m-polar interval-valued neutrosophic soft weighted average operator (mPIVNSWA) with its desirable properties. Cosine and set-theoretic similarity measures have been proposed for mPIVNSS using Bhattacharya distance and discussed their fundamental properties. Furthermore, we extend the concept of CC and weighted correlation coefficient (WCC) for mPIVNSS and presented their necessary characteristics. Moreover, utilizing the mPIVNSWA operator, CC, and SM developed three novel algorithms for mPIVNSS to solve the multicriteria decision-making problem. Finally, the advantages, effectiveness, flexibility, and comparative analysis of the developed algorithms are given with the prevailing techniques.

scholarly journals Effect of Collaborative Recommender System Parameters: Common Set Cardinality and the Similarity Measure

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mohammad Yahya H. Al-Shamri
Keyword(s):  
Similarity Measure ◽  
Recommender System ◽  
System Performance ◽  
Similarity Measures ◽  
Mean Difference ◽  
User Profiles ◽  
System Parameters ◽  
The Mean ◽  
Common Items ◽  
Evaluation Metric

Recommender systems are widespread due to their ability to help Web users surf the Internet in a personalized way. For example, collaborative recommender system is a powerful Web personalization tool for suggesting many useful items to a given user based on opinions collected from his neighbors. Among many, similarity measure is an important factor affecting the performance of the collaborative recommender system. However, the similarity measure itself largely depends on the overlapping between the user profiles. Most of the previous systems are tested on a predefined number of common items and neighbors. However, the system performance may vary if we changed these parameters. The main aim of this paper is to examine the performance of the collaborative recommender system under many similarity measures, common set cardinalities, rating mean groups, and neighborhood set sizes. For this purpose, we propose a modified version for the mean difference weight similarity measure and a new evaluation metric called users’ coverage for measuring the recommender system ability for helping users. The experimental results show that the modified mean difference weight similarity measure outperforms other similarity measures and the collaborative recommender system performance varies by varying its parameters; hence we must specify the system parameters in advance.

scholarly journals Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Hamed Faroqi ◽  
Mahmoud Mesbah ◽  
Jiwon Kim
Keyword(s):  
Similarity Measure ◽  
Smart Card ◽  
Public Transit ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Correlation Coefficients ◽  
The Public ◽  
Spatial Similarity ◽  
Smart Card Data ◽  
One Step

The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

Efficiency of special cooling liquid on thermal state of athletes at high temperatures

2019 ◽  
pp. 60-63
Author(s):  
S. M. Rasinkin ◽  
M. V. Dvornikov ◽  
I. A. Artamonova ◽  
V. V. Petrova ◽  
A. A. Kish ◽  
...  
Keyword(s):  
High Temperatures ◽  
Thermal State ◽  
Subjective Evaluation ◽  
Maximal Oxygen Consumption ◽  
Weighted Average ◽  
Male Athletes ◽  
Cooling Liquid ◽  
Sport Teams ◽  
Heat State ◽  
Average Skin

The article presents results of evaluating efficiency of special cooling liquid influence on heat state of athletes at high temperatures. The study covered 7 male athletes of cyclic sports, with sport rank at least 1 adult, average age 19.29±1.80 years. All the athletes underwent double examination including: anamnesis and complaints records, doctor’s examination, subjective evaluation of heat sensations, weight measurements, thermometry (tympanic, sublingual, rectal and skin (in 5 points)), ergospirometric stress testing. Changes in the athletes’ heat state were evaluated via dynamics of weighted average skin temperature and rectal temperature. Moreover, subjective evaluation of heat sensations was considered. Efficiency of the cooling liquid was assessed via dynamics of exercises performance time, maximal oxygen consumption and anaerobic metabolism threshold. The cooling liquid use appeared to be expedientin sport teams of summer sports for specific exertion after individual tests for adverse allergic reactions.

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

scholarly journals The quality of life in Chinese juvenile idiopathic arthritis patients: psychometric properties of the pediatric quality of life inventor generic core scales and rheumatology module

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hua-hong Wu ◽  
Feng-qi Wu ◽  
Yang Li ◽  
Jian-ming Lai ◽  
Gai-xiu Su ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Juvenile Idiopathic Arthritis ◽  
Reliability And Validity ◽  
Correlation Coefficients ◽  
Active Period ◽  
Generic Core Scale ◽  
Remission Period ◽  
Pedsql 4.0 ◽  
The Mean

Abstract Background Juvenile idiopathic arthritis (JIA) may seriously affects patients’ quality of life (QoL), but it was rarely focused and studied in China, so we explore JIA children’s QoL using Chinese version of the PedsQL4.0 Generic Core and PedsQL3.0 Rheumatology Module scale, and analyzed the psychometric properties of these two Scales among Chinese JIA children. Methods We recruited 180 JIA patients from Children's Hospital Affiliated to Capital Institute of Pediatrics and Hebei Yanda Hospital from July 2018 to August 2019. The questionnaires include information related on JIA, PedsQL4.0 generic core and PedsQL3.0 Rheumatology Module scales. According to the disease type, onset age of and course of JIA, we divided them into different groups, then compared the QoL status among different groups. Moreover, we analyzed the reliability and validity of these two scales in these 180 JIA children. Results The mean score of PedsQL4.0 generic core scale on these 180 patients was 82.85 ± 14.82, for these in active period was 72.05 ± 15.29, in remission period was 89.77 ± 9.23; the QoL score of systemic, polyarticular and oligoarticular JIA patients were 77.05 ± 19.11, 84.33 ± 12.46 and 87.12 ± 10.23. The mean score of PedsQL3.0 Rheumatology Module scale on 180 patients was 91.22 ± 9.45, for these in active period was 84.70 ± 11.37, in remission period was 95.43 ± 4.48; the QoL score of systemic, polyarticular and oligoarticular JIA patients were 89.41 ± 11.54, 89.38 ± 10.08 and 93.71 ± 6.92. In the PedsQL 4.0 Generic Core scale, the α coefficients of total scale and almost every dimension are all greater than 0.8 except for the school activity dimension of 0.589; the correlation coefficients of 22 items’ scores (total 23 items) with the scores of dimensions they belong to are greater than 0.5 (maximum value is 0.864), and the other one is 0.406. In PedsQL3.0 Rheumatology Module scale, except for the treatment and worry dimensions of 0.652 and 0.635, the α coefficients of other dimensions and the total scale are all greater than 0.7; the correlation coefficients of all items’ score were greater than 0.5 (the maximum is 0.933, the minimum is 0.515). Conclusions The QoL of Chinese JIA children is worse than their healthy peers, these in active period and diagnosed as systemic type were undergoing worst quality of life. The reliability and validity of PedsQL 4.0 Generic Core and PedsQL3.0 Rheumatology Module scale in Chinese JIA children are satisfactory, and can be used in clinical and scientific researches.

scholarly journals Daily Time Use by Activity of Community-Dwelling Older Koreans: Focus on Health Management

2021 ◽  
Vol 18 (4) ◽  
pp. 1688
Author(s):  
Hana Ko
Keyword(s):  
Older Adults ◽  
Time Use ◽  
Health Management ◽  
Subjective Evaluation ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Community Dwelling ◽  
Senior Center ◽  
The Mean ◽  
Daily Time

This study aimed to examine the daily time use by activity and identified factors related to health management time (HMT) use among 195 older adults (mean age = 77.5, SD = 6.28 years; 70.8% women) attending a Korean senior center. Descriptive statistics were analyzed and gamma regression analyses were performed. Participants used the most time on rest, followed by leisure, health management, daily living activities, and work. The mean duration of HMT was 205.38 min/day. The mean score for the subjective evaluation of health management (SEHM) was 13.62 and the importance score for SEHM was 4.72. Factors influencing HMT included exercise, number of chronic conditions, fasting blood sugar level, low density lipoprotein level, and cognitive function. HMT and frailty significantly predicted SEHM. HMT interventions focus on promoting exercise and acquiring health information to improve health outcomes among older adults in senior centers.

scholarly journals Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Jiajun Zhang ◽  
Georgina Cosma ◽  
Jason Watkins
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Recognition Rate ◽  
Weighted Average ◽  
Wind Turbine Blade ◽  
Evaluation Measures ◽  
Augmentation Techniques ◽  
The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

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