An Automatic Method for Targets Detection Using a Component-Based Model
基于部件的自动目标检测方法研究

A novel target automatic detection algorithm is proposed in this paper, and it is mainly used for the processing of the man-made targets with a relatively complex structure in natural scenes images and high-resolution remote sensing images. Based on each geometric component of objects, this method needs less training samples. First of all, it selects two sorts of typical features and trains classifiers by machine learning correspondingly, which can effectively prevent the decrease of accuracy for the similarities between interest objects and some objects in background. Then the method detects targets top-down and automatically with the marked point process model, whose data terms consist of the priori constraint on the objects distribution and respondences of trained classifiers. The experimental results demonstrate the precision, robustness, and effectiveness of the proposed method.