Image segmentation with unified region and boundary characteristics within recursive shortest spanning tree [Özyineli en kisa kapsayan aǧaç algoritmasinda bölgesel ve sinirsal bilginin birleçtirimi ile i̇mge bölütleme]
2007 IEEE 15th Signal Processing and Communications Applications, SIU
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/26995
The lack of boundary information in region based image segmentation algorithms resulted in many hybrid methods that integrate the complementary information sources of region and boundary, in order to increase the segmentation performance. In compliance with this trend, we propose a novel method to unify the region and boundary characteristics within the canonical Recursive Shortest Spanning Tree algorithm. The main idea is to incorporate the boundary information in the distance metric of RSST with minor changes in the algorithm. Additionaly, we still benefit from the simple yet powerful structure of RSST. The results indicate the superiority of the proposed algorithm with respect to the conventional RSST. The object boundaries are successfully preserved. Therefore, the proposed algorithm is a candidate for video object segmentation where object boundaries coincide with motion field boundaries.
- Conference Paper 2294
Showing items related by title, author, creator and subject.
Simsek, A.C.; Tosun, A.B.; Aykanat, C.; Sokmensuer, C.; Gunduz-Demir, C. (2012)This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the ...
Gunduz-Demir, C.; Kandemir, M.; Tosun, A.B.; Sokmensuer, C. (2010)Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures ...
Tosun, A.B.; Sokmensuer, C.; Gunduz-Demir, C. (2010)This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use ...