Titlebook: Boosting-Based Face Detection and Adaptation; Cha Zhang Book 2010 Springer Nature Switzerland AG 2010

Melanin

書目名稱Boosting-Based Face Detection and Adaptation影響因子(影響力)




書目名稱Boosting-Based Face Detection and Adaptation影響因子(影響力)學(xué)科排名




書目名稱Boosting-Based Face Detection and Adaptation網(wǎng)絡(luò)公開度




書目名稱Boosting-Based Face Detection and Adaptation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Boosting-Based Face Detection and Adaptation被引頻次




書目名稱Boosting-Based Face Detection and Adaptation被引頻次學(xué)科排名




書目名稱Boosting-Based Face Detection and Adaptation年度引用




書目名稱Boosting-Based Face Detection and Adaptation年度引用學(xué)科排名




書目名稱Boosting-Based Face Detection and Adaptation讀者反饋




書目名稱Boosting-Based Face Detection and Adaptation讀者反饋學(xué)科排名

Jogging

Cascade-based Real-Time Face Detection, and Maydt, 2002) used manual tuning or heuristics to set the intermediate rejection thresholds for the detector, which is inefficient and suboptimal. Recently, various approaches has been proposed to address this issue. Notably, Bourdev and Brandt (Bourdev and Brandt, 2005) proposed a method for se

爭(zhēng)議的蘋果

Multiple Instance Learning for Face Detection,ion results surrounding the ground truth rectangle are plausible. Such an observation is indeed quite general. In many object recognition tasks, it is often extremely tedious to generate large training sets of objects because it is not easy to specify exactly where the objects are. For instance, giv

星球的光亮度

Detector Adaptation,l-known that the performance of such a learned classifier will depend heavily on the representativeness of the labeled data used during training. If the training data contains only a small number of examples sampled in a particular test environment, the learned classifier may be too specific to be g

WITH

榮幸

LBS and TeleCartography II: About the bookWe have focused on face detection almost exclusively in the previous chapters. In this chapter, we will present two other applications of boosting learning. These two applications extend the above algorithms in two ways: the learning algorithm itself, and the features being used for learning.

Allowance

一個(gè)攪動(dòng)不安

Conclusions and FutureWork,ne learning literature, such as the confidence rated boosting (Schapire and Singer, 1999), the statistical view of boosting (Friedman et al., 1998), the AnyBoost framework (Mason et al., 2000), which views boosting as a gradient decent process, and the general idea of multiple instance learning (Nowlan and Platt, 1995).

Acetaminophen

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