Accurate detection of air bubbles boundaries is of crucial importance in determining the performance and in the study of various gas/liquid two-phase flow systems. The main goal of this Accurate detection of air bubbles boundaries is of crucial importance in determining the performance and in the study of various gas/liquid two-phase flow systems. The main goal of this Accurate detection of air bubbles boundaries is of crucial importance in determining the performance and in the study of various gas/liquid two-phase flow systems. The main goal of this work is edge extraction of air bubbles rising in two-phase flow in real-time. To accomplish this, a fast algorithm based on local variance is improved and accelerated on the GPU to detect bubble contour. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. This algorithm is performed in two steps: in the first step, the local variance of each pixel is computed based on integral image, and
then the resulting contours are thinned to generate the final edge map. We have implemented our algorithm on an NVIDIA GTX 780 GPU. The parallel implementation of our algorithm gives a speedup factor equal to 17x for high resolution images (1024×1024 pixels) compared to the serial implementation. Also, quantitative and qualitative assessments of our algorithm versus the most common edge detection algorithms from the literature were performed. A remarkable performance in terms of results accuracy and computation time is achieved with our algorithm.
work is edge extraction of air bubbles rising in two-phase flow in real-time. To accomplish this, a
fast algorithm based on local variance is improved and accelerated on the GPU to detect bubble
contour. The proposed method is robust against changes of intensity contrast of edges and capable
of giving high detection responses on low contrast edges. This algorithm is performed in two
steps: in the first step, the local variance of each pixel is computed based on integral image, and
then the resulting contours are thinned to generate the final edge map. We have implemented our
algorithm on an NVIDIA GTX 780 GPU. The parallel implementation of our algorithm gives a
speedup factor equal to 17x for high resolution images (1024×1024 pixels) compared to the serial
implementation. Also, quantitative and qualitative assessments of our algorithm versus the most
common edge detection algorithms from the literature were performed. A remarkable performance
in terms of results accuracy and computation time is achieved with our algorithm.
work is edge extraction of air bubbles rising in two-phase flow in real-time. To accomplish this, a
fast algorithm based on local variance is improved and accelerated on the GPU to detect bubble
contour. The proposed method is robust against changes of intensity contrast of edges and capable
of giving high detection responses on low contrast edges. This algorithm is performed in two
steps: in the first step, the local variance of each pixel is computed based on integral image, and
then the resulting contours are thinned to generate the final edge map. We have implemented our
algorithm on an NVIDIA GTX 780 GPU. The parallel implementation of our algorithm gives a
speedup factor equal to 17x for high resolution images (1024×1024 pixels) compared to the serial
implementation. Also, quantitative and qualitative assessments of our algorithm versus the most
common edge detection algorithms from the literature were performed. A remarkable performance
in terms of results accuracy and computation time is achieved with our algorithm
