gurobi_mex_v1.61

说明：  本程序包括：论文SVM 用于基于块划分特征提取的图像分类，和相应的matlab实现其中图像划分以及特征提取、聚类均利用matlab6.5完成。 (The procedures include: paper by SVM for feature extraction based on block classification, and the corresponding realization of one image into matlab, and feature extraction, clustering were done using matlab6.5.)

说明：  using matlab in template matching

图像图像的多聚焦融合，既两幅图片不同焦距的融合(used in imagefusion)

说明：  MATLAB编写的求单位脉冲响应曲线的性能参数指标程序(MATLAB units for the preparation of the impulse response curve of performance parameters indicators procedures)

悬架系统的天棚阻尼控制策略，有效减低系统的动载荷，加速度(Ceiling suspension system damping control strategy to effectively reduce the system dynamic loads, accelerations)

CEC2010测试函数代码，由C++和Matlab 2010编写而成。(CEC2010 test function code, and by the C++ written in Matlab 2010.)

method getOwnerDocument returns the Document object associated with this node.

Capability Maturity Model Integration, Version 1.1 CMMI for Systems Engineering and Software Engineering(Capability Maturity Model Integration, Version 1.1 CMMI for Systems Engineering and So Industrial Engineering)

自然一次样条几乎等同于样条设备生成的曲线。在这些所有的二次连续可导函数中，钳位与自然三次样条可以得到相对于待插值函数 f 的最小值(spline interpolation)

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)