Foreground detection enhancement using Pearson correlation filtering

Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Appropriate Similarity Measures for Author Cocitation Analysis

We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis

Effects of television advertising on children: with special reference to pakistani urban children

The purpose of study is to deliberate upon the impacts of television advertising on children & to identify those critical impacts which lead to behavioral and eating disorder in children. Impacts of TV advertising were identified as unnecessary purchasing, low nutritional food and materialism. A questionnaire using five point likert scale was administered to 425 parents of children aged between 9-14 years, and studying in schools. Samples were drawn through convenience sampling approach from four cities of Pakistan namely Islamabad, Rawalpindi, Bahawalpur & Multan. Data were analyzed by using SPSS software. Pearson correlation was used to measure the relationships of the variables on one-to-one basis indicating the most correlated variable was Unnecessary Purchasing which had Pearson correlation value of 0.312 and significance value of 0.000. It was followed by a Materialism which had Pearson correlation value of 0.260 and significance value of 0.000. Then comes Low Nutritional Food being Pearson correlation value of 0.258 and significance value of 0.000. Testing of hypothesis found that television advertising increases the consumption of food that is unhealthy, having low nutritional values and high in Sugar, Fat and Salt (SFS) in children with F=30.146 & P=0.000. Subsequently, it was found that Television advertising leads to increase in unnecessary purchasing in children with F= 45.747 & P=0.000 and materialism in children with F=30.545 & P=0.000. So, it is summed up that TV advertising is affecting children by increasing their food consumption pattern, preference for low-nutrient, high in sugar, fat & salt (SFS) foods and beverages, change in attitude that is aggressive and violent in nature and inclination towards unnecessary purchasing

Some Comments on the Question Whether Co-Occurrence Data Should Be Normalized

In a recent article in JASIST, L. Leydesdorff and L. Vaughan (2006) asserted that raw cocitation data should be analyzed directly, without first applying a normalization such as the Pearson correlation. In this communication, it is argued that there is nothing wrong with the widely adopted practice of normalizing cocitation data. One of the arguments put forward by Leydesdorff and Vaughan turns out to depend crucially on incorrect multidimensional scaling maps that are due to an error in the PROXSCAL program in SPSS.multidimensional scaling;PROXSCAL;Pearson correlation;author cocitation analysis;co-occurrence data;normalization

Some Comments on the Question Whether Co-occurrence Data Should Be Normalized

In a recent paper in the Journal of the American Society for Information Science and Technology, Leydesdorff and Vaughan assert that raw cocitation data should be analyzed directly, without first applying a normalization like the Pearson correlation. In this report, it is argued that there is nothing wrong with the widely adopted practice of normalizing cocitation data. One of the arguments put forward by Leydesdorff and Vaughan turns out to depend crucially on incorrect multidimensional scaling maps that are due to an error in the PROXSCAL program in SPSS.Multidimensional scaling;Author cocitation analysis;Co-occurrence data;Normalization;PROXSCAL;Pearson correlation