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Descripción
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| Hyperspectral Imaging (HI) collects high resolution spectral information consisting of hundred of bands raging from the infrared to the ultraviolet wave lengths. In the medical field, specifically, in the cancer tissue identification at the operating room, the potential of HI is huge. However, given the data volume of HI and the computational complexity and cost of identification algorithms, real-time processing is the key, differential feature that brings value to surgeons. In order to achieve real-time implementations, the parallelism available in a specification needs to be explicitly highlighted. Data-flow programming languages, like RVC-CAL, are able to accomplish this goal. In this paper, an RVC-CAL library to implement dimensionality reduction and endmember extraction is presented. The results obtained show significant improvements with regard to a state-of-the-art analysis tool. A speedup of 30% is carried out using the complete processing chain and, in particular, a speedup of 5% has been achieved in the dimensionality reduction step. This dimensionality reduction takes ten of the thirteen seconds that the whole system needs to analyze one of the images. In addition, the RVC-CAL library is an excellent tool to simplify the implementation process of HI algorithms. Effectively, during the experimental test, the potential of the RVC-CAL library to reveal possible bottlenecks present in the HI processing chain and, therefore, to improve the system performance to achieve real-time constraints has been shown. Furthermore, the RVC-CAL library provides the possibility of system performance testing. | |
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Internacional
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Si |
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Nombre congreso
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SPIE 9646, High-Performance Computing in Remote Sensing V |
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Tipo de participación
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960 |
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Lugar del congreso
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Toulouse, Francia |
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Revisores
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Si |
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ISBN o ISSN
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9781628418569 |
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DOI
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10.1117/12.2195093 |
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Fecha inicio congreso
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21/09/2015 |
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Fecha fin congreso
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21/09/2015 |
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Desde la página
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1 |
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Hasta la página
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8 |
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Título de las actas
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Proc. SPIE 9646, High-Performance Computing in Remote Sensing V |