sift2

用于BMP图像文件的滚动显示，带上下和水平滚动条。(This file is used to display BMP image files, with up-down and horizontal scroll bars.)

该数字图像处理文件有很强的修改能力，可以仔细的分析每一个频域不同条件下的实验结果分析。有很强的处理图像能力(The digital image processing of documents has a strong ability to change and be more careful analysis of each under different conditions, a frequency-domain analysis of the experimental results. Has a strong ability to deal with images)

在GDAL库下，采用主成分变换实现多光谱影像与全色高分辨影像的融合。(Under the GDAL library, the principal component transformation is used to fuse multispectral images with panchromatic high resolution images.)

静态画图，可以改变图形边框颜色与矩形或椭圆的填充颜色(Static drawing, you can change the fill color graphics border color and rectangular or oval)

基于颜色来检测火焰，将是频频分帧，然后逐帧检测，碧昂切将火焰全画出来，基础编程。(Based on color to test the flame, basic programming )

小波学习资料 供初学者和小波爱好者参考用()

基于最小二乘法的相位解缠VC++算法 (least squares method based on the VC Phase Unwrapping Algorithms)

vc++6.0进行编程实现对图像的平移处理(vc++6.0 program to achieve the translation of the image processing)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )

基于otsu的自适应阀值canny边缘检测(adaptive canny edge detection based on otsu)