Researchers at the Lulea University of Technology (Lulea, Sweden) led by associate professor Matthew Thurley are involved in a project to improve the existing software in the Compute Unified Device Architecture (CUDA) image processing library by reducing the processing times of the morphological operators erosion and dilation.
CUDA was developed by NVIDIA to allow general parallel computation to be performed on CUDA enabled GPU graphics cards. Although NVIDIA's Performance Primitives (NPP) library contains fundamental morphological operators, the Swedish researchers want to improve their performance.
As such, they are developing freely available GPU morphological extensions (referred to as LTU-CUDA) to NVIDIA CUDA by determining where the current bottlenecks in performance are and where they might be improved.
Recently, Lulea University researcher Thurley and his student Victor Danell described an implementation of fast morphological erosion and dilation based on CUDA/NPP using the vHGW algorithm -- one of the fastest for computing grayscale image dilations and erosions on a serial CPU. The researchers say that their vHGW algorithm showed significant performance improvements over the generic CUDA library NPP.
A paper describing their work -- "Fast Morphological Image Processing Open-Source Extensions for GPU processing with CUDA" -- can be found here.
The LTU-CUDA is an ongoing project and the code is freely available here.
Editor's note: Associate professor Matthew Thurley is founder of a group in industrial image analysis at Lulea University of Technology where he has developed a series of industrial machine vision prototypes for the Swedish mining, aggregates and steel industries.
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