Otsu

运用粒子群优化算法对PID控制器进行优化设计，通过matlab实现(The use of particle swarm optimization algorithm to optimize the design of PID controller, through matlab to achieve)

为GABOR小波的详细MATLAB实现代码，详细分析了该小波的各种性能()

用来进行多目标跟踪的JPDA算法的的MATLAB程序包. 跟踪效果还行. (Used for multi-target tracking JPDA algorithm of the MATLAB package tracking performance is okay.)

This book is targeted at R programmers who want to learn the graphing capabilities of R. This book will presume that you have working knowledge of R.

对任意一副灰度图像进行拉普拉斯金字塔分解。(For any gray-scale image of a Laplacian pyramid decomposition.)

高斯平滑滤波实例，高斯滤波实质上是一种信号的滤波器，其用途是信号的平滑处理，我们知道数字图像用于后期应用，其噪声是最大的问题，，由于误差会累计传递等原因，很多图像处理教材会在很早的时候介绍Gauss滤波器，用于得到信噪比SNR较高的图像（反应真实信号）。 (Instance of the Gaussian smoothing filter, Gaussian filter is essentially a signal filter, its purpose is to smooth processing of the signal, we know that the digital image for the late application, the noise is the biggest problem, the error accumulated transfer reason, a lot of image processing textbooks will be introduced at an early Gauss filter for higher SNR of the image (real response signal).)

说明：  双目测距bm算法，已经实现,基于vs2015+opencv3.2,测距精度很高，一米内精度一厘米误差(Binocular ranging bm algorithm, which has been implemented, is based on vs2015 + opencv3.2, which has high accuracy and one centimeter error in one meter)

利用灰度积分投影直接对输入的人脸图像进行检测，并进行眼睛的定位(Using gray integral projection directly on the input of face image detection, and eye location)

基于fcm的图像分割方法，效果好，希望能给大家提供一些建议。(FCM-based image segmentation method, the effect of good, I hope everyone can offer some suggestions.)

提出了一种基于图像边缘连接的线段检测方法。数字图像中的线段是由很多线段图元组成,在同一 条线段中,它们的方向是相同的并且受到真实线段的方向约束。因此可以通过提取小的线段图元, 然后分析它们 的连接特性, 进行线段的检测。首先用ADM 算法对图像进行边缘检测, 得到单像素宽的边缘 然后在4 个方向上 进行线段的检测 对初步得到的结果进行合并和验证, 去掉短的线段, 连接临近的线段。经过实验证明, 该方法能 够快速地提取图像中的线段,并且对噪声具有一定的鲁棒性(A line segment connecting a detection method based on image edges. The segment of the digital image is composed by many segments elements, their orientation is the same and by the real line segment direction constraint in the same segment. By extracting a small segment Element can then analyze their connection characteristics for the detection of the line segment. First with ADM algorithm for edge detection on the image to obtain a single-pixel-wide edge then in the four directions on the detection of the line segments merger and verification of the preliminary results obtained, removing the short line segments, the line segments connected near. The experiments show that this method can quickly extract the line segments in the image, and certain robustness to noise)