基于改进霍夫变换算法的手势识别

随着人们对人机交互的简单便捷性需求的不断提升，基于视觉的手势识别在许多领域都引起了足够的重视.由于深度图像在识别中的良好表现，其在领域内广受青睐.从深度图像中分割出手势图像区域并对其进行归一化处理得到统一规格的手势二值图像，然后进行手势边缘的检测.针对手指轮廓特性提出了改进的霍夫变换算法，提取图像中的手指信息特征.同时提取基于边缘曲线特征，并建立3D直方图进行统计.最终对两种特征进行融合，根据所得到的特征向量通过最小闭包球支持向量机（MEB-SVM）进行手势分类，测试集上识别率为96.6%.该方法不依赖于颜色、细节纹理等信息，对光照等条件不敏感，有着良好的鲁棒性.且识别速度较快能满足一般应用的需求.

Gesture Recognition Based on Progressive Hough Transform Algorithm

With the increasing demand for human-computer interaction, the vision-based gesture recognition has attracted much attention in many fields. The depth image is more and more popular in target recognition field for its good performance. The gesture region is divided from the depth image and normalized to obtain a unified specification gesture binary image, and then it detects the gesture edge. A progressive Hough transform algorithm is proposed to detect the finger edge curve and extract the finger information in the gesture image. Then it extracts the features based on the edge curve and accords this to establish 3D histogram. Finally, the two features are fused, and the gesture classification is carried out by the minimum closed ball support vector machine (MEB-SVM) according to the obtained eigenvector. And the recognition rate on the test set is 96.6%. The new method does not depend on color, detail texture, and other information, with good robustness. And the method is faster to meet the needs of general applications.