MRI-CT融合示例

基于bp神经网络的车牌字符识别源程序，采用字符比对的方式进行切割(The structures of the neural networks were designed using a constructive algorithm where the basic idea was to start with a small network,then add hidden units and weights incrementally until a satisfactory solution be found .The higher efficiencies were for a 30×17×10 number network and letter network)

完整实现Fisherface人脸识别，能够在AR、ORL、FERET等使用，采用彩色图像进行测试，实验效果好，有的能达到100 (This package implements a well-known FLD-based face recognition method, which is called Fisherface )

说明：  模式识别-贝叶斯最小错误率判别一幅图像是属于人，还是属于背景。图像经过预处理。(pattern recognition- Bayesian smallest error rate discriminant image is a person, or belong to the background. After pretreatment images.)

高速机动目标检测跟踪的程序，好不容易写的，请珍惜(High-speed target detection)

摄影测量的一个开源代码，内部含有后方交会、前方交会、相对定向、立体量测、像点匹配等功能源代码。(An Photogrametry open source project, including intersection, relative oriention, stereo measure, image matching source code.)

基于马尔科夫随机场的，例子学习超分辨率复原代码。(This is an implementation of the example-based super-resolution algorithm. Although the applications of MSFs have now extended beyond example-based super resolution and texture synthesis, it is still of great value to revisit this problem, especially to share the source code and examplar images with the research community. We hope that this software package can help to understand Markov random fields for low-level vision, and to create benchmark for super-resolution algorithms.)

粒子滤波器（particle filter）程序，用粒子滤波器跟踪运动物体。(Particle filter (particle filter) process, with particle filter to track moving objects.)

这MFC图像处理源代码总集包括了均值滤波，直方图均衡，高斯平滑，，锐化，拉普拉斯锐化（边缘检测），阈值变(With the MFC implementation of an image processing example code)

1阶HMM模型多个观察值序列的评估和训练问题模型。适合用于图像纹理的去噪，分割等问题。程序用于为网友提供学习之用，由于是自编自用，所以如有不通的地方仅供参考。另由于数据较大，没有上传。(Evaluation and Training of multiObservation sequence Used by 1-order HMM)

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. )