NDLI: Feature-based cluster segmentation of image sequences

Content Provider	IEEE Xplore Digital Library
Author	Ohm, J.-R. Phuong Ma
Copyright Year	1997
Description	Author affiliation: Image Process. Dept., Heinrich-Hertz-Inst., Germany (Ohm, J.-R.)
Abstract	One of the crucial points in object segmentation within image sequences is the interdependence of different features that classify some area as an object. This paper introduces a concept of cluster segmentation, which acquires different features on a pixel basis. Weighting of these features based on predefined rules is applied, in order to judge the evidence of each particular feature for the final classification. To determine the various clusters, we use a procedure which is similar to vector quantization. This allows the tracking of classification results over time, because cluster labels change only gradually from frame to frame. Furthermore, a technique for local feature analysis is applied for segment merging after global classification. The most common features used for object separation in image sequences are color and motion. The results indicate that reliable segmentation and tracking of objects can be accomplished, using this low-complexity technique.
Starting Page	178
Ending Page	181
File Size	607472
Page Count	4
File Format	PDF
ISBN	0818681837
DOI	10.1109/ICIP.1997.632045
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image sequences Object segmentation Merging Iterative algorithms Motion estimation Image color analysis Pixel Image processing Application software
Content Type	Text
Resource Type	Article

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