Object segmentation based on split and merge algorithm
- Authors: Faruquzzaman, A. B. M. , Paiker, Nafize , Arafat, Jahidul , Karim, Ziaul , Ali, Mortuza
- Date: 2008
- Type: Text , Conference proceedings
- Relation: 2008 IEEE Region 10 Conference, TENCON 2008; Hyderabad; India; 19th -21st Nov published in IEEE Region 10 Annual International Conference, Proceedings/TENCON p. 1-4
- Full Text: false
- Reviewed:
- Description: Image segmentation is a feverish issue as it is a challenging job and most digital imaging applications require it as a preprocessing step. Among various algorithms, although split and merge (SM) algorithm is highly lucrative because of its simplicity and effectiveness in segmenting homogeneous regions, however, it is unable to segment all types of objects in an image using a general framework due to not most natural objects being homogeneous. Addressing this issue, a new algorithm namely object segmentation based on split and merge algorithm (OSSM) is proposed in this paper considering image feature stability, inter- and intra-object variability, and human visual perception. The qualitative analysis has been conducted and the segmentation results are compared with the basic SM algorithm and a shape-based fuzzy clustering algorithm namely object based image segmentation using fuzzy clustering (OSF). The OSSM algorithm outperforms both the SM and the OSF algorithms and hence increases the application area of segmentation algorithms.
- Description: IEEE Region 10 Annual International Conference, Proceedings/TENCON
Robust object segmentation using split-and-merge
- Authors: Faruquzzaman, A. B. M. , Paiker, Nafize , Arafat, Jahidul , Ali, Mortuza , Sorwar, Golam
- Date: 2009
- Type: Text , Journal article
- Relation: International Journal of Signal and Imaging Systems Engineering Vol. 2, no. 1-2 (2009), p. 70-80
- Full Text: false
- Reviewed:
- Description: In spite of simplicity and effectiveness in segmenting homogeneous regions in an image, Split-and-Merge (SM) algorithm is unable to segment all types of objects due to huge number of objects with myriad variations among them and due to high dependability on the threshold values used in splitting and merging techniques. Addressing these issues, a novel Robust Object Segmentation using Split-and-Merge (ROSSM) is proposed in this paper considering image feature stability, inter- and intra-object variability, and human visual perception. The qualitative analysis proves the superior performance of ROSSM in comparison with the basic SM algorithm and a recently developed shape-based fuzzy clustering algorithm namely Object-based image Segmentation using Fuzzy clustering (OSF). Copyright © 2009 Inderscience Enterprises Ltd.