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周方正, 周治平. 融合边缘特征的Prim快速目标定位算法[J]. 计算机辅助设计与图形学学报, 2017, 29(1): 108-114.
引用本文: 周方正, 周治平. 融合边缘特征的Prim快速目标定位算法[J]. 计算机辅助设计与图形学学报, 2017, 29(1): 108-114.
Zhou Fangzheng, Zhou Zhiping. A Novel Prim's Algorithm Merged with Edge Feature for Fast Object Localization[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(1): 108-114.
Citation: Zhou Fangzheng, Zhou Zhiping. A Novel Prim's Algorithm Merged with Edge Feature for Fast Object Localization[J]. Journal of Computer-Aided Design & Computer Graphics, 2017, 29(1): 108-114.

融合边缘特征的Prim快速目标定位算法

A Novel Prim's Algorithm Merged with Edge Feature for Fast Object Localization

  • 摘要: 为了解决目标检测中物体定位效率不高的问题,针对现有多数方法采用的穷举式的滑动窗口检测的不足,提出一种改进的Prim算法融合超像素而产生推荐窗口的算法.先采用快速的结构化边缘检测算子产生边缘结构,然后通过非极大值抑制产生各边缘集合,再由边界集合构建物体轮廓确定Prim算法融合超像素初始的条件,最终由超像素间的边界强度和开始产生的边缘轮廓判断终止条件,产生图像中各物体的定位区域.在Pascal VOC 2007数据集上进行实验的结果表明,在一致的检测标准下,文中改进的Prim算法在平均重叠率达到0.78的情况下,将原有算法产生的推荐窗口减少了60%左右,同时算法时间提升近1倍.

     

    Abstract: This paper addresses the low-efficiency of generating possible object locations for use in object detection. In consideration of the existing deficiency of sliding window detection using an exhaustive method, we propose a method of improved Prim's algorithm which combines super pixels and generates proposals windows. The method employs a structured edge detection operator to fast gauging the edge structure, and through the non-maximum suppression engender each edge group, then those groups build object contours to determine the initial locations of random Prim's algorithm combining each super pixel inside, at the end, frontier magnitude between super pixels with the edge initialization can determine the algorithm's termination. Experiments on the challenging PASCAL VOC 2007 dataset show that improved Prim's algorithm's average best overlap has achieved 0.78, the number of proposal windows which are generated by original algorithm is reduced about 60%, while the algorithm's speed is also increased.

     

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