For a long time,trouble detection and maintenance of freight cars have been completed manually by inspectors.To realize the transition from manual to computer-based detection and maintenance,we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system.Using mid-level features which are also named flexible edge arrangement(FEA) features,we first build the edge-based 2D model of the dust collectors,and then match target objects by a weighted Hausdorff distance method.The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively,which can truly reflect the most basic property regions of the 3D object.Experimental results indicate that the proposed algorithm has better robustness to variable lighting,different viewing angle,and complex texture,and it shows a stronger adaptive performance.The localization correct rate of the target object is over 90%,which completely meets the need of practical applications.
通过设定图像预测错误门限,并结合支持向量机（Support Vector Machines,SVM）对图像数据进行分类,提出一种二维（2-D）马尔科夫链模型的信息隐藏检测系统。在嵌入率为0.1bpp时,分别应用扩频（spreadspectrum,SS）和量化索引调制（Quantization Index Modulation,QIM）进行实验,系统实现数字水印的正确检测率超过90%;而应用LSB方法,在嵌入率为0.01bpp-0.3bpp条件下,系统实现的数字水印正确检测率在50%~90%以上。
