A Comparison of Regional Feature Detectors in Panoramic Images
We present a novel approach to detect and describe visual features in panoramic image data. For various applications, especially computer and robot vision, robust and invariant features are key paths to explore scenes and objects. Most features applied in the literature can commonly be classified either as being local or being global. Local features characterize a significant point in the image like an edge. Global features describe a general property of the whole image like the color distribution. In this paper, we propose an in-between representation using region-based symmetry features. We compare the approach to a set of state-of-the-art affine feature detectors. Experiments show that the symmetry features are sparse, distinctive and robust to changes in panoramic image warp. Therefore, they are well applicable to robot vision tasks.
Kai Huebner Daniel Westhoff Jianwei Zhang
University of Bremen Department of Mathematics and Computer Science D-28359 Bremen, Germany University of Hamburg Technical Aspects of Multimodal Systems D-22527 Hamburg, Germany
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
666-671
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）