canon-camera-src-0.2

指纹识别demo 演示方向图、roc曲线(fingerprint demo show orientation and ROC curve)

说明：  JPEG2000 的一个实现，期中，wavelet.dll是提供的功能接口。(An implementation of JPEG2000, period, wavelet.dll is to provide a functional interface.)

像素级融合实现 pca his，brovery加权融合方法，很不错(Pixel level fusion to achieve pca his, brovery weighted fusion method, very good)

VisualC++数字图像处理技术详解第2版光盘-第六章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter VI)

说明：  压缩感知的经典代码，包括观测，稀疏表示，重建，其中观测用的高斯观测矩阵，稀疏用的DCT，重建用的OMP算法(Compressed sensing of the classic code, including observations, sparse representation, reconstruction, observed with a Gaussian observation matrix, sparse use of the DCT, re-use of the OMP algorithm)

基于双目视觉的三维重建，包含了重建过程中的基本步骤。(Binocular vision-based three-dimensional reconstruction, including the reconstruction of the basic steps of the process.)

vc+matlab的混合编程三维图像方面的。很有用。还不错。(Hybrid programming to achieve three-dimensional image reconstruction )

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. )

粒子滤波器使用示例，用于跟踪效果好，用VC++实现(example of particle filter)

提取SIFT特征，进行特征提取和匹配，包括特征点的定位，特征点描述子的生成，特征点的匹配，(extract SIFT feature，and feature matching)