中文摘要：

关于从图像中定位物体轮廓的问题,目前所采用的活动轮廓模型和基于自组织神经网络的算法,存在能量泛函优化容易陷入局部极值和演化过程依赖于初始轮廓的选取等问题。提出了一种基于RBF神经网络的轮廓定位算法。首先,通过自适应梯度阈值方法来获取图像特征点。然后,通过特征点的聚类建立一组基函数,把图像像素点的像素值和梯度构造输入向量空间,在网络权值训练完成后,利用网络的预测功能来准确判断物体轮廓。与传统算法相比,仿真结果表明提出的轮廓定位算法可以高效地实现目标轮廓定位。

英文摘要：

For the object contour location in the images,traditional active contour models and self-organized mapping neural network based algorithms are prone to trap into local minimum in their optimizations and depend on the initial contour selection.A contours location algorithm based on RBF neural network is proposed in this paper.Firstly,image feature points are obtained by adaptive gradient threshold method.After the feature points are clustered,a group of radial basis functions are constructed.Using the pixels’intensities and gradients as the input vector,the final object contour can be obtained by the predicting ability of the neural network.Compared with traditional algorithm,simulation results show that the proposed contours location algorithm can realize the object contour detection in high efficiency.