Titlebook: Computer Analysis of Images and Patterns; 13th International C Xiaoyi Jiang,Nicolai Petkov Conference proceedings 2009 Springer-Verlag Berl

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书目名称Computer Analysis of Images and Patterns影响因子(影响力)




书目名称Computer Analysis of Images and Patterns影响因子(影响力)学科排名




书目名称Computer Analysis of Images and Patterns网络公开度




书目名称Computer Analysis of Images and Patterns网络公开度学科排名




书目名称Computer Analysis of Images and Patterns被引频次




书目名称Computer Analysis of Images and Patterns被引频次学科排名




书目名称Computer Analysis of Images and Patterns年度引用




书目名称Computer Analysis of Images and Patterns年度引用学科排名




书目名称Computer Analysis of Images and Patterns读者反馈




书目名称Computer Analysis of Images and Patterns读者反馈学科排名

Fatten

https://doi.org/10.1007/978-3-663-06736-8en compared to the detector that used Viola & Jones’s weak classifiers. When compared to detectors that used Rasolzadeh et al.’s and Mita et al.’s weak classifiers, the GWC-based detector produced 11% and 9% fewer false positives. Simultaneously, it required 37% and 42% less time for the scanning pr

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https://doi.org/10.1007/978-3-663-06736-8 feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very prom

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0302-9743 n a single-track program.In addition, wewereveryhappyto haveAljoscha Smolicand David G. Storkasourinvitedspeakerstopresenttheirworkintwofascinatingareas.With th978-3-642-03766-5978-3-642-03767-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Model Based Analysis of Face Images for Facial Feature Extraction feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very prom

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Conference proceedings 2009 deserve a great thanks for their timely and competent reviews. The accepted papers were presented either as oral presentations or posters in a single-track program.In addition, wewereveryhappyto haveAljoscha Smolicand David G. Storkasourinvitedspeakerstopresenttheirworkintwofascinatingareas.With th

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https://doi.org/10.1007/978-3-322-96174-7sts employed optical tools. Computer methods will not replace tradition art historical methods of connoisseurship but enhance and extend them. As such, for these computer methods to be useful to the art community, they must continue to be refined through application to a variety of significant art historical problems.

Pulmonary-Veins

https://doi.org/10.1007/978-3-322-96174-7at the previous step. This boosting strategy helps to focus on hard examples and selects a set of complementary features. Results are compared with three state-of-the-art methods on the Pointing 04 database.