Neurocomputing 2009
National Chung-Hsing University, Taiwan
내용
기본적으로 histogram + template matching.
Contributions:
- The proposed non-uniform partition (in histogram)
- The use of Self-organizing Takagi-Sugeno-type fuzzy network with support vector learning (SOTFN-SV) classifier
- A splitting K-means clustering algorithm is proposed (to eliminate false alarms and determine object location).
비교:
- template matching method using color histogram. histogram intersection (HI).
- OpenCV (Haar-like features)
- SIFT (grey)
- SONFIN (classifier 비교 - 이런 방법들이 등장하는 건 역시 neural networks를 다루는 저널이기 때문에)
- Gaussian-kernel based SVM (역시 classifier)