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

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發(fā)表于 2025-3-21 16:40:55 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Analysis of Images and Patterns
副標題13th International C
編輯Xiaoyi Jiang,Nicolai Petkov
視頻videohttp://file.papertrans.cn/234/233433/233433.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Analysis of Images and Patterns; 13th International C Xiaoyi Jiang,Nicolai Petkov Conference proceedings 2009 Springer-Verlag Berl
描述It was an honor and a pleasure to organizethe 13th International Conference on Computer Analysis of Images and Patterns (CAIP 2009) in Mu ¨nster, Germany. CAIP has been held biennially since 1985: Berlin (1985), Wismar (1987), Leipzig (1989), Dresden (1991), Budapest (1993), Prague (1995), Kiel (1997), Ljubljana (1999), Warsaw (2001), Groningen (2003), Paris (2005), and Vienna (2007). Initially, this conference series served as a forum for getting together s- entistsfromEastandWestEurope.Nowadays,CAIPenjoysahighinternational visibility and attracts participants from all over the world. For CAIP 2009 we received a record number of 405 submissions. All papers were reviewed by two, and in most cases, three reviewers. Finally, 148 papers were selected for presentation at the conference, resulting in an acceptance rate of 36%. All Program Committee members and additional reviewers listed here 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
出版日期Conference proceedings 2009
關鍵詞Markov Model; Support Vector Machine; action recognition; algorithms; biometrics; classification; data min
版次1
doihttps://doi.org/10.1007/978-3-642-03767-2
isbn_softcover978-3-642-03766-5
isbn_ebook978-3-642-03767-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

書目名稱Computer Analysis of Images and Patterns影響因子(影響力)




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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
板凳
發(fā)表于 2025-3-22 00:55:00 | 只看該作者
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
地板
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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.
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發(fā)表于 2025-3-23 05:34:40 | 只看該作者
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.
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