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Descripción
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| In this paper, a study of the parallel adaptation of a spatial-spectral classifier which consist of a Principal Component Analysis (PCA) algorithm, a Support Vector Machine (SVM) classifier and a K-Nearest Neighbors filter, running on a Massively Parallel Processor Array (MPPA) platform ?a system that joins 256 cores distributed among 16 clusters?, is presented. This research work is aimed at exploiting the MPPA platform potential to implement in parallel these algorithms, so as to minimize the required time to analyze a hyperspectral image. The spatial-spectral classifier algorithm is intended to discriminate between cancer and normal tissues during neurosurgical procedures. Experimenting with medical brain images captured in one operating theater, the processing time measured when parallelizing the processing chain has been compared to the one obtained when executing them sequentially. As a result, an average speedup of more than 140x has been achieved. Consequently, the hyperspectral images are processed in less than a 4% of the available time, considering this time as that required for the hyperspectral sensor to capture a new image. | |
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Internacional
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Si |
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Nombre congreso
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XXXII Conference on Design of Circuits and Integrated Systems (DCIS 2017) |
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Tipo de participación
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960 |
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Lugar del congreso
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Barcelona |
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Revisores
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Si |
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ISBN o ISSN
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DOI
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Fecha inicio congreso
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22/11/2017 |
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Fecha fin congreso
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24/11/2017 |
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Desde la página
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1 |
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Hasta la página
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6 |
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Título de las actas
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Design and Architectures for Signal and Image Processing (DASIP), 2017 Conference on |