Trends in Funding, Internationalization, and Types of Study for Original Articles Published in Five Implant-Related Journals Between 2005 and 2009

Purpose: The aims of this study were to evaluate the trends in funding, geographic origin, and study types of original articles in the dental implant literature and to investigate the relationships among these factors. Materials and Methods: Articles published in Clinical Oral Implants Research, The International Journal of Oral & Maxillofacial Implants, Clinical Implant Dentistry and Related Research, Implant Dentistry, and Journal of Oral Implantology from 2005 to 2009 were reviewed. Nonoriginal articles were excluded. For each article included, extramural funding source, geographic origin, and study type were recorded. Descriptive and analytic analyses (alpha = .05), including a logistic regression analysis, and chi-square test were used where appropriate. Results: of a total of 2,085 articles published, 1,503 met the inclusion criteria. The most common source of funding was from industry (32.4%). The proportion of studies that reported funding increased significantly over time. Europe represented the highest percentage (55.8%) of published articles. Most of the articles reported on clinical studies (49.9%), followed by animal studies (25.9%). Articles from Asia and South America and animal and in vitro studies were significantly more likely to be funded. Conclusion: Almost half of the original dental implant articles were funded. The trend toward internationalization of authorship was evident. A strong association was observed between funding and geographic origin and between funding and study type. Most studies in North America and Europe were clinical studies and supported by industry, whereas a greater proportion of studies in Asia and South America were in vitro or animal studies funded through government resources. INT J ORAL MAXILLOFAC IMPLANTS 2012;27:69-76

Authorship, Collaboration, and Funding Trends in Implantology Literature: Analysis of Five Journals From 2005 to 2009

Purpose: To identify the trend of authorship in dental implant by exploring the prevalence of coauthored articles and to investigate the collaboration efforts, trends in funding involved in original articles, and their relationships. Materials: Articles published in the Clinical Oral Implants Research, International Journal of Oral & Maxillofacial Implants, Clinical Implant Dentistry and Related Research, Implant Dentistry, and Journal of Oral Implantology from 2005 to 2009 were reviewed. Nonoriginal articles were excluded. For each included articles, number of authors, collaboration efforts, and extramural funding were recorded. Descriptive and analytical statistics (alpha = 0.05), including logistic regression analysis and chi(2) test, were used. Results: From a total of 2085 articles, 1503 met the inclusion criteria. Publications with 5 or more authors increased over time (P = 0.813). The amount of collaboration among different disciplines, institutions, and countries all increased. The greatest increase of collaboration was seen among institutions (P = 0.09). Non-funding studies decreased over time (P = 0.031). There was a strong association between collaboration and funding for the manuscripts during the years studied (OR, 1.5). Conclusion: The number of authors per articles and collaborative studies increased over time in implant-related journals. Collaborative studies were more likely to be funded. (Implant Dent 2011;20:68-75)

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities ill images. This estimation is crucial for the problem of crowd monitoring. and control. The assessment is carried out oil a set of nearly 300 real images captured from Liverpool Street Train Station. London, UK using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight lille segments. Fourier analysis. and fractal dimensions. The estimations of dowel densities are given in terms of the classification of the input images ill five classes of densities (very low, low. moderate. high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model). Bayesian. and an approach based on fitting functions. The results obtained by these three classifiers. using the four texture measures. allowed the conclusion that, for the problem of crowd density estimation. texture analysis is very effective.

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Estimating crowd density with Minkowski fractal dimension

The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. Fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.