Hossein Izadi
University of Alberta
18 Papers
21 Citations
Hossein Izadi is an academic researcher from University of Alberta. The author has contributed to research in topics: Particle swarm optimization & Viscosity. The author has an hindex of 7, co-authored 18 publications. Previous affiliations of Hossein Izadi include Iranian Offshore Oil Company & University College of Engineering.
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Papers
An intelligent system for mineral identification in thin sections based on a cascade approach
TL;DR: An intelligent system for mineral identification in thin sections based on RGB and HSI color spaces and texture features in plane and cross polarized light is proposed and a real time and reliable segmentation and identification map is created.
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Shear wave velocity prediction using Elman artificial neural network
Behzad Mehrgini,Behzad Mehrgini,Hossein Izadi,Hossein Izadi,Hossein Izadi,Hossein Memarian,Hossein Memarian +6 more
TL;DR: In this paper, a new method as an alternative way is proposed based on Elman artificial neural network to predict Shear Wave Velocity (Vs) using well log data including gamma ray (GR), resistivity (LLD), neutron porosity (NPHI), bulk density (RHOB), compression wave velocity (Vp), and water saturation (Sw).
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A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering
TL;DR: A novel method based on incremental learning for clustering pixels is proposed in order to segment index minerals in both thin sections with and without altered minerals, which outperforms the results of other similar methods in the literature.
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The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity
TL;DR: A comparison between directly measured and image-derived porosities clearly exhibited that the application of different segmentation methods produced vastly different results, demonstrating the importance of the segmentation step for quantitative pore space analysis.
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Intelligent mineral identification using clustering and artificial neural networks techniques
Hossein Izadi,Javad Sadri,Nosrat-Agha Mehran +2 more
- 06 Mar 2013
TL;DR: The high accuracy and precision of minerals identification in this study, have given the proposed intelligent system remarkable capabilities.
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