为了解决低带宽信道传输红外目标图像的问题,在改进的基于过渡区图像分割方法的基础上,将其应用到红外目标图像感兴趣区域的自动提取,最后探讨了基于状态位图提升SPIHT(Set Partitioning in Hierarchical Trees,SPIHT)算法的感兴趣区图...为了解决低带宽信道传输红外目标图像的问题,在改进的基于过渡区图像分割方法的基础上,将其应用到红外目标图像感兴趣区域的自动提取,最后探讨了基于状态位图提升SPIHT(Set Partitioning in Hierarchical Trees,SPIHT)算法的感兴趣区图像压缩。在JPEG2000的基本框架下进行了具体实现,通过图像实验充分验证了该方法的有效性、实时性,具有重要的应用价值。展开更多
The centroid and attitude of target must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting preci...The centroid and attitude of target must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting precision. CMAC estimator is trained with a linear model, then the centroid and attitude are predicted. It is trained once by actual error in each frame to reduce the estimate error. CMAC has excellent predicting precision and small operational counts, it adapts to real time processing for target tracking. The experimental results show that CMAC can accurately estimate the centroid and attitude of target. It adapts to change of model and has robustness.展开更多
文摘为了解决低带宽信道传输红外目标图像的问题,在改进的基于过渡区图像分割方法的基础上,将其应用到红外目标图像感兴趣区域的自动提取,最后探讨了基于状态位图提升SPIHT(Set Partitioning in Hierarchical Trees,SPIHT)算法的感兴趣区图像压缩。在JPEG2000的基本框架下进行了具体实现,通过图像实验充分验证了该方法的有效性、实时性,具有重要的应用价值。
文摘The centroid and attitude of target must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting precision. CMAC estimator is trained with a linear model, then the centroid and attitude are predicted. It is trained once by actual error in each frame to reduce the estimate error. CMAC has excellent predicting precision and small operational counts, it adapts to real time processing for target tracking. The experimental results show that CMAC can accurately estimate the centroid and attitude of target. It adapts to change of model and has robustness.
