NDLI: A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Jundi Ding Runing Ma Songcan Chen
Copyright Year	1992
Abstract	This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsiv-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: (1) its fundamental idea, epsiv-neighbor coherence segmentation criterion, is easy to interpret and comprehend; (2) it is efficient due to a linear computational complexity in the number of image pixels; (3) both subjective comparisons and objective evaluation have shown that it is effective for the tasks of semantic object segmentation and figure-ground separation in a wide variety of images. Those images either contain tiny, long and thin objects or are severely degraded by noise, uneven lighting, occlusion, poor illumination, and shadow.
Sponsorship	IEEE Signal Processing Society
Page Count	13
File Size	1567905
Starting Page	204
Ending Page	216
File Format	PDF
ISSN	10577149
Volume Number	17
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Pixel Tree graphs Carbon capture and storage Tree data structures Image analysis Computational complexity Object segmentation Degradation Lighting semantic segmentation $\varepsilon $ -neighbor coherence segmentation criterion connected coherence tree figure-ground separation object segmentation
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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