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Erschienen in:
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2012 | OriginalPaper | Buchkapitel

8. Object Detection

verfasst von : Jianxin Wu, James M. Rehg

Erschienen in: Ensemble Machine Learning

Verlag: Springer New York

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Abstract

Over the past twenty years, data-driven methods have become a dominant paradigm for computer vision, with numerous practical successes. In difficult computer vision tasks, such as the detection of object categories (for example, the detection of faces of various gender, age, race, and pose, under various illumination and background conditions), researchers generally learn a classifier that can distinguish an image patch that contains the object of interest from all other image patches. Ensemble learning methods have been very successful in learning classifiers for object detection.

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Vorheriges Kapitel Ensemble Nyström
Nächstes Kapitel Classifier Boosting for Human Activity Recognition
Fußnoten
1
After training the AdaBoost classifier (i.e., a node classifier in the cascade), one can adjust the threshold θ to meet the learning goal of a node classifier (e.g., a fixed detection rate or a fixed false positive rate.)
 
2
Special care is required for computing \({\Sigma }_{{\mathbf{x}}^{{\prime}}}\) efficiently. However, we omit these details. The readers may refer to Sect. 3.2 of [28] for more information.
 
3
In addition to the level 4 nodes shown in Fig. 8.4, Huang et al. rotate their features (called Granule features) by 90 ∘ , 180 ∘ , and − 90 ∘  for the level 4 nodes. This strategy effectively covers the entire 360 ∘  range in-plane rotation.
 
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Metadaten
Titel
Object Detection
verfasst von
Jianxin Wu
James M. Rehg
Copyright-Jahr
2012
Verlag
Springer New York
Buch
Ensemble Machine Learning

Print ISBN: 978-1-4419-9325-0

Electronic ISBN: 978-1-4419-9326-7

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-1-4419-9326-7_8

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