Multi-Scale Maximally Stable Extremal Regions for Object Recognition

摘要：

To solve the problem that maximally stable extremal regions (MSER) will become unstable when the image is blurred due to the change of scale, a novel affine invariant feature called Multi-Scale Maximally Stable Extremal Region (MMSER) which is maximally stable both in the image space and the scale space is proposed by defining a criterion to evaluate the stability of extremal regions in scale space. And a fast extraction algorithm based on N-tree disjoint set forest and seeded growing algorithm is designed for MMSER. At the same time, according to the property that MMSER can describe the contour of local features fairly well, a new kind of descriptor is designed for MMSER by combining the local gray grads and the shape context information. The experimental results prove that MMSER is much more stable and discernable under different affine transformation conditions.

关键词： multi-scale maximum stable extremal region feature descriptor affine invariant feature

作者: Luo Ronghua Min Huaqing

作者单位: Department of Computer Science and Engineering South China University of Technology Guangzhou,Guangd Department of Software South China University of Technology Guangzhou,Guangdong 510006,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）