1 December 2007 Unified mean shift segmentation and graph region merging algorithm for infrared ship target segmentation
Author Affiliations +
Optical Engineering, 46(12), 127002 (2007). doi:10.1117/1.2823159
We propose a unified approach that incorporates the mean shift-based image segmentation algorithm and the SST (shortest spanning tree)-minmax-based graph grouping method to achieve effective IR object segmentation performance amenable for real-time application. It preprocesses an image by using the mean shift algorithm to form segmented regions that can not only remove the noise, but also preserve the desirable discontinuity characteristics of the ship object. The segmented regions can then effectively represent the original image by using the graph structures, and we apply the SST-minmax method to perform merging procedure to form the final segmented regions. Due to the good discontinuity-preserving filtering characteristic, we can effectively remove the clutter disturbance of the sea background without loss of the IR ship object information, and significantly reduce the number of basic image entities. Therefore, the region merging based on SST-minmax can produce excellent segmentation performance at low computational cost due to smaller clutter disturbance and less region nodes. The superiority of the proposed method is examined and demonstrated through a large number of experiments using a real IR ship image sequence.
Wenbing Tao, Hai Jin, Jin Liu, "Unified mean shift segmentation and graph region merging algorithm for infrared ship target segmentation," Optical Engineering 46(12), 127002 (1 December 2007). http://dx.doi.org/10.1117/1.2823159

Image segmentation

Image processing algorithms and systems

Infrared imaging

Target detection

Automatic target recognition

Image filtering

Detection and tracking algorithms


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