基于分层次模型的自动目标识别方法

由于存在目标结构复杂、类间差异影响、图像发生变化、背景干扰等问题，复杂场景中结构复杂目标的完整轮廓信息难以准确描述。针对该问题，提出了一种分层次模型：低层中引入条件随机场融合多类特征,获取目标存在的候选区域，为高层处理提供外观特性方面的辅助；高层中通过对模型各部分特性统计建模，实现对目标形状特征的描述。实验结果表明，该模型灵活性强，普适性高，识别结果准确稳定。

Automatic approach to object recognition based on hierarchical model

It is difficult to accurately describe the full contour of targets with a complex structure in the cluttered scene, because of the complexity of structures, inter class differences, variation of conditional of images, background interference and so on. In order to overcome the shortages, a novel hierarchical model is proposed: in the lower layer, the conditional random fields model is used to fuse sorts of features to get the candidate regions of targets, which is helpful for high level processing; in the higher one, the distribution of shape of targets can be computed through statistic of the characteristics of parts of the model. Competitive results demonstrate the precision, robustness, and effectiveness of the proposed method.