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10.1371/journal.pone.0162830 http://scihub22266oqcxt.onion/10.1371/journal.pone.0162830 C5017619!5017619!27611217     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2016 ; 11 (9): ä Nephropedia Template TP {{tp|p=27611217|t=2016. Human Detection Using Random Color Similarity Feature and Random Ferns Classifier |pdf=|usr=}}{{27611217}} gab.com Text Human Detection Using Random Color Similarity Feature and Random Ferns Classifier JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27611217 #FOAvip We explore a novel approach for human detection based on random color similarity feature (RCS) and random ferns classifier which is also known as semi-naive Bayesian classifier. In contrast to other existing features employed by human detection, color-based features are rarely used in vision-based human detection because of large intra-class variations. In this paper, we propose a novel color-based feature, RCS feature, which is yielded by simple color similarity computation between image cells randomly picked in still images, and can effectively characterize human appearances. In addition, a histogram of oriented gradient based local binary feature (HOG-LBF) is also introduced to enrich the human descriptor set. Furthermore, random ferns classifier is used in the proposed approach because of its faster speed in training and testing than traditional classifiers such as Support Vector Machine (SVM) classifier, without a loss in performance. Finally, the proposed method is conducted in public datasets and achieves competitive detection results. Twit Text FOAVip Human Detection Using Random Color Similarity Feature and Random Ferns Classifier ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27611217 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27611217 #FOAvip English Wikipedia <ref>{{Cite pmid|27611217}}</ref> Human Detection Using Random Color Similarity Feature and Random Ferns Classifier #MMPMID27611217 Zhang M; Xin M PLoS One 2016[]; 11 (9): ä PMID27611217show ga We explore a novel approach for human detection based on random color similarity feature (RCS) and random ferns classifier which is also known as semi-naive Bayesian classifier. In contrast to other existing features employed by human detection, color-based features are rarely used in vision-based human detection because of large intra-class variations. In this paper, we propose a novel color-based feature, RCS feature, which is yielded by simple color similarity computation between image cells randomly picked in still images, and can effectively characterize human appearances. In addition, a histogram of oriented gradient based local binary feature (HOG-LBF) is also introduced to enrich the human descriptor set. Furthermore, random ferns classifier is used in the proposed approach because of its faster speed in training and testing than traditional classifiers such as Support Vector Machine (SVM) classifier, without a loss in performance. Finally, the proposed method is conducted in public datasets and achieves competitive detection results. äHuman Detection Using Random Color Similarity Feature and Random Ferns(epmc).pdf DeepDyve Pubget Overpricing