标题：This paper aims to propose a fast image searching method from environmental observation images even in the presence of scale changes. A new scheme has been proposed for extracting feature areas as tags based on a robust image registration algorithm called Orientation code matching. Extracted tags are stored as reference images and utilized in tag searching. As the number of tags grows, the searching cost becomes a serious problem. Additionally, change in viewing positions cause scale change of an image and matching failure. In our scheme, richness in features is important for tag generation and the entropy is used to evaluate the diversity of edge directions which are stable to scale change of the image. This characteristic contributes to limitation of searching area and reduction in calculation costs. Scaling factors are estimated by orientation code density which means the percentage of effective codes in fixed size tag areas. An estimated scaling factor is applied to matching a scale of reference images to one of observation images. Some experiments are performed in order to compare computation time and verify effectiveness of estimated scaling factor using real scenes
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2006
卷号：XXXVI Part 5
出版社：Copernicus Publications
摘要：This paper aims to propose a fast image searching method from environmental observation images even in the presence of scale changes. A new scheme has been proposed for extracting feature areas as tags based on a robust image registration algorithm called Orientation code matching. Extracted tags are stored as reference images and utilized in tag searching. As the number of tags grows, the searching cost becomes a serious problem. Additionally, change in viewing positions cause scale change of an image and matching failure. In our scheme, richness in features is important for tag generation and the entropy is used to evaluate the diversity of edge directions which are stable to scale change of the image. This characteristic contributes to limitation of searching area and reduction in calculation costs. Scaling factors are estimated by orientation code density which means the percentage of effective codes in fixed size tag areas. An estimated scaling factor is applied to matching a scale of reference images to one of observation images. Some experiments are performed in order to compare computation time and verify effectiveness of estimated scaling factor using real scenes
关键词：complex regions extraction; image searching; image scaling; orientation code entropy; mobile robot
