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Architectures flot de données dédiées au traitement d'images par morphologie mathématique

Abstract : This thesis report is focused on studying data flow accelerators dedicated to image using mathematical morphology. The main objective is to provide a programmable and efficient implementation of basic morphological operators, and to assemble them in such a way as to provide complex operators with fast operation. In recent years, morphological algorithm research has been oriented towards finding elegant algorithms to compute these complex operators, such as watershed using priority queues. These complex algorithms often use specific data structures that are hard to deploy on platforms other than single-core, general-purpose processors. Moreover, these processors continue their development in the field of parallelism by heightening the number of cores. And because the frequency wall seems to have been reached, the best way to optimise performance is to use parallelising techniques. Consequently, we decided on fast implementations of complex mathematical morphology operators, based on highly parallel simpler operations. In the first part, we study existing computational kernels for neighbourhood processors and suggest new ones based on recent advances in mathematical morphology. In the second part, we use the neighbourhood processors as building blocks to generate and manage pipeline using high-level tools in a system on chip context. In the third part, we present a description of a basic VLIW processor using vector instructions deployed in a dataflow context to exploit spatial and temporal parallelism. Finally, we analyse the performance of our system against a multi-core workstation processor, and against a graphics processor to show the relevance of our approach.
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Submitted on : Wednesday, January 27, 2010 - 8:00:00 AM
Last modification on : Wednesday, November 17, 2021 - 12:27:08 PM
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  • HAL Id : pastel-00005758, version 1


Christophe Clienti. Architectures flot de données dédiées au traitement d'images par morphologie mathématique. Mathématiques [math]. École Nationale Supérieure des Mines de Paris, 2009. Français. ⟨NNT : 2009ENMP1654⟩. ⟨pastel-00005758⟩



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