Object Detection Using SURF and Superpixels
Object Detection Using SURF and Superpixels
复制标题
DOI：
10.4236/jsea.2013.69061
复制
发表时间：
2013-08
期刊：
影响因子：
--
通讯作者：
Miriam Lopez-de-la-Calleja;T. Nagai;M. Attamimi;M. Nakano-Miyatake;H. Perez-Meana
中科院分区：
文献类型：
--
作者：
Miriam Lopez-de-la-Calleja;T. Nagai;M. Attamimi;M. Nakano-Miyatake;H. Perez-Meana
本文提出了一种新的目标检测方法，在该方法中，超像素内部的一组局部特征被提取从由3D视觉传感器获取的分析图像。为了提高分割精度，该方法首先使用简单线性迭代聚类（SLIC）超像素方法对分析下的图像进行分割。接下来，使用加速鲁棒特征（SURF）来估计每个超像素内的关键点。然后，这些关键点被用于对估计的超像素内的场景的每个检测到的关键点执行匹配任务。此外，引入概率图来描述目标检测结果的准确性。实验结果表明，该方法提供了相当好的目标检测，并证实了优越的性能相比，最近提出的其他方法，如由Mae等人提出的计划，建议的场景。
This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.