Hybrid Region Merging For Image Segmentation Using Optimal Global Feature With Global Merging Criterion Approach

Vadiveloo, Mogana (2020) Hybrid Region Merging For Image Segmentation Using Optimal Global Feature With Global Merging Criterion Approach. PhD thesis, Universiti Sains Malaysia.

[img]
Preview
PDF
Download (751kB) | Preview

Abstract

Region merging approach is used to reduce over segmented regions produced by region-based image segmentation algorithms. It is performed by merging the over segmented regions progressively to produce the final segmentation as spatially contiguous regions with closed boundaries. Predominantly, region merging is performed between two neighboring regions solely on a local merging criterion. This may fail most existing region merging approaches to detect large non-homogeneous visual objects that have global semantic similarity but consist of diverse set of over segmented regions. Besides that, improper selection of global feature information by partitional clustering algorithm in turn affects the merging criterion derivation in region merging eventually causing leakages into adjacent visual object regions. Consequently, this thesis aims to solve these two issues by proposing a region merging approach to merge the over segmented regions producing semantic segments of visual objects regions.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: Mr Aizat Asmawi Abdul Rahim
Date Deposited: 11 Nov 2022 02:00
Last Modified: 11 Nov 2022 02:00
URI: http://eprints.usm.my/id/eprint/55616

Actions (login required)

View Item View Item
Share