This algorithm

which is based on both statistical characteristics of complex wavelet energy and SVM

is proposed in order to effectively detect and track targets in image

which may cause changes

such as translation

scaling and rotation. So

the problem of image matching is transformed as that of classification. The transformation of complex wavelet that has properties of scale

shift invariant and directional selectivity effectively extract the statistical characteristics of image

such as mean

standard deviation and skew. The statistical characteristics of sample templates are input into SVM to train support vectors of SVM. Then

those statistical characteristics of any sub-image from original image are input into SVM in order to match target. This is a two-stage algorithm of coarse-to-fine. Firstly

the set of candidates is sifted by SVM. Secondly

a new optimal rule

which is nonlinear distance function

is proposed to decide the optimal matching from the candidate set. Those experimental results show that this algorithm addresses the problem of confidence level

which generally exists in traditional matching methods. This algorithm's performance is superior to those of both learning method of neural network based on RBF and gray-level correlation matching method

which compares with them. Finally

a good matching result is obtained.