Image segmentation based on complexity mining and mean-shift algorithm (CROSBI ID 612831)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Sirotković, Jadran ; Dujmić, Hrvoje ; Papić, Vladan
engleski
Image segmentation based on complexity mining and mean-shift algorithm
Mean shift algorithm is a well established method for image segmentation. It is particularly popular technique due to non-parametric nature which enables efficient segmentation of complex arbitrary shapes. Despite such advantage, high computational complexity still makes it unsuitable for segmentation of high resolution images in time critical applications. This paper introduces a new approach which alleviates performance issues of mean shift using complexity reduction based on information theory. Proposed algorithm starts by calculating information potential field of the image in order to get insight into complexity of the regions. Afterwards, only complex regions are segmented by computationally expensive mean shift algorithm, while segmentation of simpler regions is performed by a cheaper method. Performance of our method is additionally improved with execution of the key code sections on the GPGPU platform. Experimental results have shown that our method produces comparable segmentation quality to regular parallel mean shift, but with significant reduction in overall execution time.
GPGPU; image segmentation; CUDA; mean shift algorithm
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Podaci o prilogu
1-6.
2014.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of 19th IEEE Symposium on Computers and Communications
Funchal:
978-1-4799-4278-7
Podaci o skupu
19th IEEE International Symposium on Computers and Communications (ISCC 2014)
predavanje
23.06.2014-26.06.2014
Funchal, Portugal