0000000001001098

AUTHOR

Xinbo Gao

showing 2 related works from this author

Multi-Kernel Implicit Curve Evolution for Selected Texture Regions Segmentation in VHR Satellite Images

2014

Very high resolution (VHR) satellite images provide a mass of detailed information which can be used for urban planning, mapping, security issues, or environmental monitoring. Nevertheless, the processing of this kind of image is timeconsuming, and extracting the needed information from among the huge quantity of data is a real challenge. For some applications such as natural disaster prevention and monitoring (typhoon, flood, bushfire, etc.), the use of fast and effective processing methods is demanded. Furthermore, such methods should be selective in order to extract only the information required to allow an efficient interpretation. For this purpose, we propose a texture region segmentat…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR]Pixelbusiness.industryComputer science0211 other engineering and technologiesGraphics processing unitBoundary (topology)Scale-space segmentation02 engineering and technologyImage segmentationFuzzy logicImage texture11. Sustainability0202 electrical engineering electronic engineering information engineeringGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingComputer visionSegmentation[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]Artificial intelligenceElectrical and Electronic EngineeringbusinessComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering
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Architecture-Driven Level Set Optimization: From Clustering to Sub-pixel Image Segmentation

2016

Thanks to their effectiveness, active contour models (ACMs) are of great interest for computer vision scientists. The level set methods (LSMs) refer to the class of geometric active contours. Comparing with the other ACMs, in addition to subpixel accuracy, it has the intrinsic ability to automatically handle topological changes. Nevertheless, the LSMs are computationally expensive. A solution for their time consumption problem can be hardware acceleration using some massively parallel devices such as graphics processing units (GPUs). But the question is: which accuracy can we reach while still maintaining an adequate algorithm to massively parallel architecture? In this paper, we attempt to…

Level set methodComputer science0211 other engineering and technologiesInitialization02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingLevel setgraphics processing units0202 electrical engineering electronic engineering information engineeringLevel set methodComputer visionElectrical and Electronic EngineeringCluster analysisMassively parallelimage segmentation021101 geological & geomatics engineeringActive contour modelhybrid CPU-GPU architecturebusiness.industryImage segmentationSubpixel renderingComputer Science ApplicationsHuman-Computer InteractionControl and Systems EngineeringHardware acceleration020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftwareInformation Systems
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