raw2rgb24

关于elastix医学图像配准算法库的例子程序，有助于分析elastix如何操作，是一个很好的教程(this is a example set of elastix library)

立体视觉代码。主要过程包括：harris角点检测，zncc最优策略匹配，扩散得到更多特征对应，计算disparity和对应颜色值。另外提供了两幅图片以及对应的特征匹配的结果和disparity图像。(Three-dimensional visual codes. The main process includes: harris corner detection, zncc optimal strategy for matching, the proliferation of more features of the corresponding calculation of disparity and the corresponding color value. Also provides a picture and two corresponding feature matching results and disparity images.)

利用棋盘格对相机进行标定，加入误差判定及像素空间到实际物理空间的距离计算(The camera is calibrated by checkerboard, the error is added and the distance of pixel space to the actual physical space is calculated)

推荐火灾检测的参考文献，有火焰多个特征提取的算法。(the article is about how to detect fire，using many algorithms to get the features of fire.)

1 完成图象处理的基本功能2：二值化、边缘检测、滤波。 2 熟悉dc的部分常用功能，学习图标控制弹出框。(a complete image processing of the two basic functions : values, edge detection, filtering. 2 familiar with the dc part of the commonly used function, learning to control pop icon frame.)

我改写后的graphbrian类，原本支持柱状图饼图和折线图的，现在添加了对这些图的风格调整，主要是增加了调整绘图区背景色和文本背景的功能，还增加了把图片保存为bmp文件的功能，以便用其他软件方便的打印。(I would then graphbrian category, which originally supported the pie chart histogram and line graph, and now added to adjust the style of these plans, the main adjustment is to increase the drawing area background color and text background function, but also an increase of the picture saved as bmp files, in order to use other software to facilitate printing.)

基于Sift算法开发商标图案匹配程序，(1)各个CBIR模块及其技术； (2)Sift算法及其在图像匹配、目标识别中的应用； (3)Sift算法在图像检索中的应用。 (Sift algorithm development based on pattern matching program mark (1) Each CBIR module and its technology (2) Sift algorithm and its application in image matching, object recognition application (3) Sift algorithm in image retrieval applications.)

说明：  在地震处理中，非常经典的进行时深转换的程序，通常比较好用(In seismic processing, the very classic procedure for time-depth conversion, usually more useful)

本书系统地介绍了图像处理与识别的基本原理、典型方法和实用技术。全书共分12章，第1章～第6章是图像处理与识别的基础内容，包括图像科学综述、MATLAB语言图像编程、图像增强、图像分割、图像特征提取和图像识别；第7章～第10章是图像处理与识别的工程实例，涵盖了医学图像处理、文字识别和自导引小车路径识别等应用实例，并结合理论算法，提供了大量MATLAB代码程序，以帮助读者掌握如何使用MATLAB语言快速进行算法的仿真、调试和估计等方法。第11章～第12章，是两个综合性较强的实例，分别是Visual C++实现的基于神经网络的文字识别系统和车牌定位系统。 本书附带的光盘给出了各个章节列举的实例的源代码，同时赠送了28个常用数字图像处理算法的Visual C++代码实现。 本书讲解深入浅出，实例程序丰富，注重理论与实践相结合。本书可作为计算机应用、自动化、图像处理与模式识别、机电一体化专业的高年级本科生或研究生的参考书，也可供从事图像处理与识别的研究人员和工程技术人员阅读参考。(This book introduces the basic principles of image processing and recognition of the typical methods and practical skills . The book is divided into 12 chapters , Chapter 1- Chapter 6 is the basis of the content of image processing and recognition , including images scientific overview , MATLAB programming language images, image enhancement, image segmentation, image feature extraction and image recognition Chapter 7- Section Chapter 10 is a project example image processing and recognition , covering the medical image processing, character recognition and self- guided trolley path recognition example , the combination of theory and algorithms, for a lot of MATLAB code procedures to help readers learn how to use MATLAB language fast simulation , debugging and estimation methods algorithm. Chapter 11- Chapter 12 , are two examples of highly integrated , namely Visual C++ implementation based on neural network character recognition system and a license plate positioning system. The book)

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. )