tailieunhanh - Báo cáo hóa học: " High-resolution image segmentation using fully parallel mean shift"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : High-resolution image segmentation using fully parallel mean shift | Varga and Karacs EURASIP Journal on Advances in Signal Processing 2011 2011 111 http content 2011 1 111 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access High-resolution image segmentation using fully parallel mean shift Balazs Varga and Kristdf Karacs Abstract In this paper we present a fast and effective method of image segmentation. Our design follows the bottom-up approach first the image is decomposed by nonparametric clustering then similar classes are joined by a merging algorithm that uses color and adjacency information to obtain consistent image content. The core of the segmenter is a parallel version of the mean shift algorithm that works simultaneously on multiple feature space kernels. Our system was implemented on a many-core GPGPU platform in order to observe the performance gain of the data parallel construction. Segmentation accuracy has been evaluated on a public benchmark and has proven to perform well among other data-driven algorithms. Numerical analysis confirmed that the segmentation speed of the parallel algorithm improves as the number of utilized processors is increased which indicates the scalability of the scheme. This improvement was also observed on real life high-resolution images. Keywords High resolution imaging Parallel processing Image segmentation multispectral imaging Computer vision 1 Introduction Thanks to the mass production of fast memory devices state of the art semiconductor manufacturing processes and vast user demand most contemporary photograph sensors built into mainstream consumer cameras or even smartphones are capable of recording images of up to a dozen megapixels or more. In terms of computer vision tasks such as segmentation image size is in most cases highly related to the running time of the algorithm. To maintain the same speed on increasingly large images the image processing algorithms have to run on increasingly powerful processing units.