{"title":"将距离图像分割为平面区域","authors":"P. Checchin, L. Trassoudaine, J. Alizon","doi":"10.1109/IM.1997.603861","DOIUrl":null,"url":null,"abstract":"This paper presents a hybrid approach to the segmentation of range images into planar regions. The term hybrid refers to a combination of edge- and region-based considerations. A reliable computational procedure which takes the range image discontinuities into account is presented for computing the pixel's normal. The segmentation algorithm consists of two parts. In the first one, the pixels are aggregated according to local properties derived from the input data and are represented by a region adjacency graph (RAG). At this stage, the image is still over-segmented. In the second part, the segmentation is refined thanks to the construction of an irregular pyramid. The base of the pyramid is the RAG previously extracted. The over-segmented regions are merged using a surface-based description. This algorithm has been evaluated on 80 real images acquired by two different range sensors using the methodology proposed in (Hoover et al., 1996). Experimental results are presented and compared to others obtained by four research groups.","PeriodicalId":337843,"journal":{"name":"Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Segmentation of range images into planar regions\",\"authors\":\"P. Checchin, L. Trassoudaine, J. Alizon\",\"doi\":\"10.1109/IM.1997.603861\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a hybrid approach to the segmentation of range images into planar regions. The term hybrid refers to a combination of edge- and region-based considerations. A reliable computational procedure which takes the range image discontinuities into account is presented for computing the pixel's normal. The segmentation algorithm consists of two parts. In the first one, the pixels are aggregated according to local properties derived from the input data and are represented by a region adjacency graph (RAG). At this stage, the image is still over-segmented. In the second part, the segmentation is refined thanks to the construction of an irregular pyramid. The base of the pyramid is the RAG previously extracted. The over-segmented regions are merged using a surface-based description. This algorithm has been evaluated on 80 real images acquired by two different range sensors using the methodology proposed in (Hoover et al., 1996). Experimental results are presented and compared to others obtained by four research groups.\",\"PeriodicalId\":337843,\"journal\":{\"name\":\"Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IM.1997.603861\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IM.1997.603861","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
摘要
提出了一种将距离图像分割为平面区域的混合分割方法。术语混合指的是基于边缘和基于区域的考虑的组合。提出了一种考虑范围图像不连续的可靠计算方法来计算像素的法向。分割算法由两部分组成。在第一种方法中,像素根据从输入数据导出的局部属性进行聚合,并用区域邻接图(RAG)表示。在这个阶段,图像仍然是过度分割的。在第二部分中,由于不规则金字塔的构造,分割得到了改进。金字塔的底部是之前提取的RAG。使用基于表面的描述合并过度分割的区域。使用(Hoover et al., 1996)中提出的方法,对两种不同距离传感器获得的80幅真实图像进行了算法评估。介绍了实验结果,并与四个研究小组获得的其他结果进行了比较。
This paper presents a hybrid approach to the segmentation of range images into planar regions. The term hybrid refers to a combination of edge- and region-based considerations. A reliable computational procedure which takes the range image discontinuities into account is presented for computing the pixel's normal. The segmentation algorithm consists of two parts. In the first one, the pixels are aggregated according to local properties derived from the input data and are represented by a region adjacency graph (RAG). At this stage, the image is still over-segmented. In the second part, the segmentation is refined thanks to the construction of an irregular pyramid. The base of the pyramid is the RAG previously extracted. The over-segmented regions are merged using a surface-based description. This algorithm has been evaluated on 80 real images acquired by two different range sensors using the methodology proposed in (Hoover et al., 1996). Experimental results are presented and compared to others obtained by four research groups.