经典广义Hough变换可以较好地解决非形变目标定位问题，但对于存在形变的目标定位问题存在不少困难。为解决该问题，同时考虑如何提高检测定位速度与减少存储消耗，在粗定位与精确定位两级框架下提出基于改进GHT形变目标两层定位快速算法。粗定位过程首先利用图像的局域二进制模式的直方图特征对图像进行全局搜索，检测出目标大致范围；在精确定位过程中，通过建立模板图像边缘像素的R表，使待检测图像边缘像素在约束的参数范围内依据该R表进行局部搜索，并通过一个投票结果散布窗对得到的累积矩阵进行集中化处理，达到把每一点邻域内投票结果集中在某点的目的，从而给出最后的检测结果。实验表明，本文算法能够较好的解决一定程度形变目标的定位问题，同时减少了运算时间以及存储消耗，检测稳定性高，具有一定应用意义。

Classic generalized Hough transform (GHT) can locate non-deformed shape object, while it is difficult to solve the problem when the target is similar but not necessarily identical to the user or deformation. A two level deformed target locate algorithm based on variant of the well-known GHT for solving this problem is presented. Firstly a two-level locationscheme from coarse to fine strategy is introduced to reduce search range from whole image space, in coarse location step, the edge local binary pattern (LBP) histogram features are extracted to detect the range of the target. In fine location step, making use of the edge points of the image detected and the R-table obtained from the template image to search the feasible parameter, and a large dispersion window used to merge vote results because there is non-perfectly aligned points near the optimal parameters. The experiment results demonstrate that the method is effective to the deformed target locating while time and memory cost is much less, and it is valuable in many applications.