Abstract
Segmentation of digital images is an important issue of object recognition. This method of image processing allows to determine single object areas in images. This paper presents an improved segmentation method which gives a possibility to detect single objects in images by using the disparity map algorithm in connection with the mean shift pixel grouping algorithm. Images are processed in grayscale where range of colors is in from 0 to 255. Grayscale allows to detect objects on the basis of pixels brightness. To achieve this purpose we used one of grouping algorithms known as mean shift. Images obtained from mean shift are in the form of separated images which could be subject of further processing. Important feature of mean shift processing is that we obtain the results in the form of backgroundless images containing important objects from the input image.
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The project was funded by the National Center for Science under decision number DEC-2011/01/D/ST6/06957.
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Grycuk, R., Gabryel, M., Korytkowski, M., Romanowski, J., Scherer, R. (2014). Improved Digital Image Segmentation Based on Stereo Vision and Mean Shift Algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_41
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DOI: https://doi.org/10.1007/978-3-642-55224-3_41
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