针对基于立体视觉的移动机器人导航中摄像机视角及光线亮度的变化会使所获得的场景环境图像发生一定的仿射变换，为了选取稳定的图像局部特征点，提出了一种具有仿射不变的兴趣点检测算法——多项局部方向张量（polynomial local orient...针对基于立体视觉的移动机器人导航中摄像机视角及光线亮度的变化会使所获得的场景环境图像发生一定的仿射变换，为了选取稳定的图像局部特征点，提出了一种具有仿射不变的兴趣点检测算法——多项局部方向张量（polynomial local orientation tensors，PLOT）算子。PLOT算子是基于图像多项扩展式局部方向张量的兴趣点检测算法，它将图像的每一像素点以多项扩展式的方式展开建立局部图像信号模型，求其对应的局部方向张量，搜索局部方向张量最小特征值的局部邻域最大值，获得对图像兴趣点的初始检测定位，并应用仿射递归算法实现兴趣点及对应特征区域的最终准确检测与定位。基于重复率准则的兴趣点检测实验表明。展开更多
For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest p...For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.展开更多
文摘针对基于立体视觉的移动机器人导航中摄像机视角及光线亮度的变化会使所获得的场景环境图像发生一定的仿射变换，为了选取稳定的图像局部特征点，提出了一种具有仿射不变的兴趣点检测算法——多项局部方向张量（polynomial local orientation tensors，PLOT）算子。PLOT算子是基于图像多项扩展式局部方向张量的兴趣点检测算法，它将图像的每一像素点以多项扩展式的方式展开建立局部图像信号模型，求其对应的局部方向张量，搜索局部方向张量最小特征值的局部邻域最大值，获得对图像兴趣点的初始检测定位，并应用仿射递归算法实现兴趣点及对应特征区域的最终准确检测与定位。基于重复率准则的兴趣点检测实验表明。
基金Projects(61203332,61203208) supported by the National Natural Science Foundation of China
文摘For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.
