Omid Aghazadeh

Education:

• Ph.D in Computer Vision (ongoing)
  2011 - present/ Computer Vision and Active Perception Laboratory
• M.Sc in Systems, Control and Robotics
  Sept 2008 - June 2010/ Royal Institute of Technology(KTH)
• B.Sc in Computer Engineering(Software)
  2001 - 2006 / Azad University of Qazvin

Publications:

• Multi View Registration for Novelty/Background Separation/ Omid Aghazadeh, Josephine Sullivan, Stefan Carlsson, CVPR, 2012. [PDF] , [Supp Mat]
• Novelty Detection from an Ego-Centric Perspective/ Omid Aghazadeh, Josephine Sullivan, Stefan Carlsson, CVPR, 2011. [PDF]
• Reading Street Signs Using a Generic Structured Object and Signature Recognition/Sobhan Naderi Parizi, Alireza Tavakoli Targhi,Omid Aghazadeh and Jan-Olof Eklundh, VISAPP, 2009. [PDF]

Thesis:

• MSc thesis: Object Segmentation Using Spatial and Spatio-Temporal Features, 2010

Selected Projects:

• Visual Diaries:
  

  Project Page

• Foreground/Background Object Segmentation:
• Object Boundary Detection using Spatio-Temporal(Optical Flow) and Visual(Texture and Color) Cues:


• Non-Linear and Anisotropic Image Denoising:


• Scale-Space Methods:

Teaching Experiences:

• 2009, Teaching Assistant in "Applied Estimation", KTH.
• 2010, Teaching Assistant(one session) in "Applied Estimation", KTH. Introduction to Vectorized Processing in MATLAB , Sample Codes for Vectorized processing in MATALB

Code:

• Iterative methods for computation of Fundamental matrix: The package contains MATLAB implementation of the iterative algorithms suggested in Hartley and Zisserman's Multi View Geometry.
• Nonlinear Coupled Diffusion: The package contains MATLAB implementation of Weickert's Nonlinear Coupled Diffusion.
• A Sparse Set of Features for Texture Discrimination: The package contains MATLAB implementation of Brox's feature set.
• Gaussian Mixture Models on Cuda Mex(Binary) [Requires Fermi Cards]: The package contains linux 64 bit binaries for learning(Expectation Maximization) and evaluating multi dimensional gaussian mixture models using GPU acceleration.
• Isotropic Multi Dimensional Kernel Density Estimation on Cuda Mex(Binary) [Requires Fermi Cards]: The Linux 64 bit binary for MDKDE(Isotropic) using GPU acceleration.
• Dense distance computations between two sets of points(Binary) [Requires Fermi Cards]: The Linux 64 bit binary for dense distance computations including P norms(L_1, L_2 and L_inf), Chi squared distance and Histogram Intersection between two sets of points(or histograms) using GPU acceleration. The code is tuned to handle large dimensions and large sets (when the distance matrix does not fit on device memory).