A novel algorithm for edge enhancement based on Hilbert Matrix

Golpayegani, Neda; Ashoori, Ahmad

doi:10.1109/iccet.2010.5485960

Cited by 8 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using diverse type of filters or transformations like morphological analysis [34], Hilbert transform [35], and multiple radon or beamlet transform [36] is not suitable to handle noise. Indeed, excessive use of these transformations and filters or the use of large scales could reduce their ability to detect short edges and create noncontinuous edges.…”

Section: Review Of Edge Detection Algorithmsmentioning

confidence: 99%

GPU acceleration of edge detection algorithm based on local variance and integral image: application to air bubbles boundaries extraction

Bettaieb

Filali

et al. 2019

Computer Optics

View full text Add to dashboard Cite

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.

show abstract

Section: Review Of Edge Detection Algorithmsmentioning

confidence: 99%