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说明：  这是论文“Saliency Detection: A Spectral Residual Approach”的matlab代码，其中包含两种代码，都可以运行，结果有点差异，大家可以试试。(This is the code of the paper“Saliency Detection: A Spectral Residual Approach”,and it includes two different codes which can be both run successfully ,and the results have some differences)

FilterTitleOverlay字幕叠加的filter(FilterTitleOverlay subtitles superimposed filter)

该程序是MATLAB源程序，可以用来求纹理图像的方向。(MATLAB source code of the procedure can be used to seek the direction of texture images.)

说明：  这个资料是学习kalman滤波的基础材料，里面有程序和文档介绍，适合初学的童鞋(The attachment is the material for Kalman learning, especally for beginners.Hopefully, it will help)

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)

三次B样条曲线、一阶二阶三阶导矢曲线。可用于绘制机器人B样条轨迹的位移、速度、加速度、加加速度曲线图(Cubic B-spline curve, a second-order third-order derivative vector curve. Can be used to draw the trajectory of the robot B-spline displacement, velocity, acceleration, jerk curve)

动态系统建模方法介绍，学习怎样实现用MATLAB对非线性动态系统的建模(Simulink MATLAB)

盲信号分离(BSS)是指在对彼此独立的源信号混合过程及各源信号本身均未知的情况下，从混合信号中分离出这些源信号的方法。BSS可以用来从多个麦克风混合语音信号中提炼出单个语音信号。本文简要阐述LMS、RLS算法，并通过仿真实验来分析比较这两类方法的性能，并利用此方法对一实际的语音信号进行分离。(Blind Signal Separation (BSS) is defined as independent of each other mixed-signal process and the source of the signal itself are unknown circumstances, from the mixed-signal to isolate the source of these signals. BSS can be used from multiple microphones mixed voice signal to extract a single speech signal. This paper briefly described LMS, RLS algorithm, and through simulation experiments to analyze the comparative performance of these two types of methods and take advantage of this method on a real speech signal separation.)

关关于最短路的Dijkstra算法的matalab程序源代码!(Off on the Dijkstra shortest path algorithm matalab source code!)

基于Belief Propagation算法的立体匹配(Belief Propagation algorithm based on stereo matching)