CDKFflying

1st lecture of OOP beginners can find it very useful

在基于Mean-Shift的目标跟踪算法中，尺度自适应机制是算法研究的一个重要方向。一种典型的方法采 用Lindeberg的尺度空间理论以获取目标尺度信息。但现有算法中将尺度由2．D矢量压缩为1一D量，未能精细地 刻画目标仿射变换时的尺度变化，从而限制了算法的适用范围。为此，该文将尺度维1．D滤波推广至2一D，构造得 到了相应的垂D尺度空间，并利用窄间维和尺度维的Mean-Shift交替迭代，实现了同时在空间位置和尺度方向对 目标的有效跟踪，提高了算法在目标尺度变化时的自适应性，并扩大了算法的适用范围。 关键(Scale adaptive mechanism in the target tracking algorithm based on Mean-Shift algorithm is an important direction of research. A typical method using Lindeberg' s scale space theory to obtain the target scale. However, the existing algorithms will scale from 2. D vector compression for 1-D volume, not fine to portray the target affine transformation of scale changes, thus limiting the scope of application of the algorithm. To this end, the paper will scale dimension. D filter to the 2-D structure has been the vertical D scale space and the use of narrow inter-dimension and scale dimension of the Mean-Shift alternating iteration, effective tracking of the target position in space and scale direction to improve the self-adaptive algorithm when the target scale changes, and to expand the scope of application of the algorithm. Key)

本工具箱描述了非线性盲源分离的原理及算法(This toolkit described the principle and algorithm of nonlinear blind source separation)

关于数据压缩的DCT压缩和hoffman压缩算法，有详细的例子，运行有效，推荐下载(DCT hoffman data compressing)

introduction to water level control system

三维重建，利用matlab实现ct影像重建(Three-dimensional reconstruction)

说明：  基于模型预测控制算法研究自动驾驶车辆轨迹跟踪(Trajectory tracking of autonomous vehicle based on model predictive control algorithm)

matlab的常用指针表，综合辅导与指南，符号语言工具箱(matlab common pointer table, comprehensive counseling and guidance, sign language toolbox)

说明：  维纳率波器的仿真研究，一个做圆周运动的物体，经观测后有一定的误差和噪声影响，经维纳滤波后轨迹更加清(Wiener rate of wave device simulation, a circular motion of the object to do by observing the error and after some noise by Wiener filtering path is more clear)

A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.(A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.)