Titlebook: Computer Vision -- ACCV 2007; 8th Asian Conference Yasushi Yagi,Sing Bing Kang,Hongbin Zha Conference proceedings 2007 Springer-Verlag Berl

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書目名稱Computer Vision -- ACCV 2007影響因子(影響力)




書目名稱Computer Vision -- ACCV 2007影響因子(影響力)學(xué)科排名




書目名稱Computer Vision -- ACCV 2007網(wǎng)絡(luò)公開度




書目名稱Computer Vision -- ACCV 2007網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision -- ACCV 2007被引頻次




書目名稱Computer Vision -- ACCV 2007被引頻次學(xué)科排名




書目名稱Computer Vision -- ACCV 2007年度引用




書目名稱Computer Vision -- ACCV 2007年度引用學(xué)科排名




書目名稱Computer Vision -- ACCV 2007讀者反饋




書目名稱Computer Vision -- ACCV 2007讀者反饋學(xué)科排名

Ballad

原諒

0302-9743 Overview: 978-3-540-76385-7978-3-540-76386-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

Thymus

https://doi.org/10.1007/978-3-658-39702-9R), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising.

Detain

Localized Content-Based Image Retrieval Using Semi-Supervised Multiple Instance LearningR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising.

COLON

https://doi.org/10.1007/978-3-7908-1978-6tional cameras, enables novel imaging applications and simplifies many computer vision tasks. However, a majority of current Computational Photography methods involve taking multiple sequential photos by changing scene parameters and fusing the photos to create a richer representation. The goal of C

COLON

https://doi.org/10.1007/978-3-7908-1978-6d active shape. The fixed shape is a user-predefined simple shape with only a few landmarks which can be easily and accurately located by machine or human. The active one is composed of many landmarks with complex shape contour. When searching an active shape, pose parameter is calculated by the fix

BLAND

Arboreal

Stylized Facts and First Explanations,rties of Timoshenko beam, classical . can be derived. The comparison of these two models in terms of their robustness and precision against noisy data is given. We demonstrate that Timoshenko beam model is more robust and precise for tracking large deformations in the presence of clutter and partial

crucial

Designing for Learning in Coupled Contextsy. Quadratic snakes allow global interactions between points along a contour, and are well suited to segmentation of linear structures such as roads. However, a single quadratic snake is unable to extract disconnected road networks and enclosed regions. We propose to use a family of cooperating snak