buckboost

通过normxcorr2算法进行目标的识别。(By normxcorr2 target recognition algorithm.)

open the zip file .. here you will find some help documents and then you can use those to get some valuable indformation about image processing

Here the syms toolbox is used and if you try to print any other values than lambda then MATLAB will print the value in fractions and that is unavoidable. So if you need to know the value in decimal, then use the command vpa() on command window

很好的小波应用实例文档，介总结内容有小波用于去噪、图像压缩、边界扭曲、图像融合、小波包的分解和重构等等，附详细的MAMTLAB代码(Application of wavelet good document, refer to summarize the contents of wavelet denoising, image compression, border distortions, image fusion, wavelet packet decomposition and reconstruction, etc., with detailed MAMTLAB code)

CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori estimation of two chain Coupled Hidden Markov Models. (CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori es timation of two chain Coupled Hidden Markov Mod els.)

Simple but effective example of "Region Growing" from a single seed point. The region is iteratively grown by comparing all unallocated neighbouring pixels to the region. The difference between a pixel s intensity value and the region s mean, is used as a measure of similarity. The pixel with the smallest difference measured this way is allocated to the region. This process stops when the intensity difference between region mean and new pixel becomes larger than a certain treshold

两个关于小球的 matlab 示例 小程序(Two examples of small balls small program matlab)

BER-SNR curve of BPSK modulator with second order diversity

可以进行离散信号与连续信号的卷积演示的界面，包括各种信号的演算，可展示结果。(Can be discrete convolution signal with a continuous signal interface presentation, including the calculation of various signals, display the results.)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.