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
A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to de...A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.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。
基金Project(61101185)supported by the National Natural Science Foundation of China
文摘A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.
