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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2014.10
- 수록면
- 146 - 149 (4page)
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초록· 키워드
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The object detection method employing the 1D feature has benefit that the calculating speed is fast. However, detection accuracy and performance is low in complex background. Therefore, in this paper, we propose an ensemble learning algorithm that combines 1D feature classifier and 2D DNF cell classifier to improve the performance of object detection in single input image. The reason for selecting 2D DNF classifier is that the classifier is able to classify the object not categorized in traditional weak classifier. And we proposed method to choose the feature for reducing the time of learning. In the experiment, we select the haar-like feature as input of 1D feature, and prove the performance of algorithm for face data.
목차
Abstract
1. INTRODUCTION
2. RECOGNITION ALGORITHM
3. SELECTING 2D DNF FEATURES
4. EXPERIMENTS
5. CONCLUSION
REFERENCES
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