Biosketch

I am a PhD student in the Computer Vision, Speech Communication and Signal Processing Group of the school of ECE of NTUA since 2001. My main research interests are image segmentation, object recognition and statistical pattern recognition.

During a considerable part of my first graduate years I explored, under the supervision of prof. P. Maragos, the use of nonlinear function approximation/Machine learning techniques, applied to capturing and analysing the nonlinear dynamics of speech signals.

During a 4 month stay at the Odyssee team of INRIA, under the supervision of Dr. R.Deriche, I studied biological models of vision, and tried to see how they can be linked with the variational approach to vision. Specifically, I studied the FACADE model of vision proposed by S.Grossberg, and tried to see how it relates to some more commonly used biological and computer vision models. My research efforts in this direction are along two interwoven paths:

• Building a link with variational methods for computer vision.
• Interpreting the network probabilistically, thereby facilitating the use of ground-truth data to learn the network's weights.

Another research direction where I have been working together with G. Evangelopoulos concerns the use of AM-FM functions for image segmentation and texture analysis. My research has focused on three aspects of the problem:

• Casting the Dominant Components Analysis algorithm in a detection-theoretic framework, using generative models to phrase the channel selection problem.
• Using the AM-FM features to drive the unsupervised segmentation of images.
• Combining the above two aspects of the problem by fusing the texture and non-texture cues for natural image segmentation.

At the core of my PhD research is the interaction between the segmentation and recognition processes and mainly the feedback from recognition to segmentation. This is conjectured to play a significant role in the biological vision system, yet has only recently been tackled by people working in the computer vision community. My research focuses on the probabilistic aspects of this problem and specifically the Expectation Maximization algorithm, which fits hand-in-glove with this problem. Along a different path, yet with the same motivation, I have explored the potential of combining point-of-interest detection results with generative models in a graphical model setting, thus enabling the combination of bottom-up with top down cues for object detection.

Journal Papers

• Nonlinear Speech Analysis Using Models for Chaotic Systems.
   I.Kokkinos and P.Maragos, IEEE Transactions on Speech and Audio, November 2005

Conference Papers

• Iasonas Kokkinos, Petros Maragos and Alan Yuille,
  Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models,
  IEEE Conf. on Computer Vision and Pattern Recognition, 2006, to appear
• Iasonas Kokkinos and Petros Maragos,
  An Expectation Maximization Approach to the Synergy Between Image Segmentation and Object Categorization,
  Intl. Conf. on Computer Vision, 2005
• Iasonas Kokkinos and Petros Maragos,
  A Detection-Theoretic Approach to Texture and Edge Discrimination,
  4th Int.l workshop on Texture Analysis and Synthesis, in conjunction with ICCV 05
• Georgios Evangelopoulos, Iasonas Kokkinos and Petros Maragos,
  Advances in Variational Image Segmentation using AM-FM models: Regularized Demodulation and Probabilistic Cue Integration ,
  3rd VLSM workshop, in conjunction with ICCV 05
• I. Kokkinos, G. Evangelopoulos, P. Maragos,
  Advances in Texture Analysis: Energy Dominant Component & Multiple Hypothesis Testing,
  Proc. Int' l Conf. on Image Processing (ICIP-2004), Singapore, Oct. 2004.
  
• I. Kokkinos, G. Evangelopoulos, P. Maragos,
  Modulation-Feature Based Textured Image Segmentation Using Curve Evolution,
  Proc. Int' l Conf. on Image Processing (ICIP-2004), Singapore, Oct. 2004.
  
• A Biologically Motivated and Computationally Tractable Model of Low- and Mid-Level Vision Tasks.
  I.Kokkinos, R.Deriche, P.Maragos and O. Faugeras, European Conference on Computer Vision 2004
• Nonlinear Analysis of Speech Signals: Generalized Dimensions and Lyapunov Exponents.
  V.Pitsikalis, I.Kokkinos and P.Maragos, European Conference on Speech Processing, 2003
• Some Advances in Nonlinear Speech Modeling Using Modulations, Fractals and Chaos.
  P.Maragos, A.Dimakis and I.Kokkinos, IEEE Int. Conf. on DSP, pp.  2002.

Technical Reports

• Towards bridging the gap between biological and computational segmentation.
  I.Kokkinos, R.Deriche, Olivier Faugeras and P.Maragos, INRIA RR 2004 (under preparation)
• Modeling and Prediction of Speech Signals Using Chaotic Time-Series Analysis Techniques (in greek).
  I.Kokkinos and P.Maragos, TR for the basic research Programme 'ARCHIMEDES'  of ICCS-NTUA, 2002

  Diploma Thesis

• Modeling and Prediction of Speech Signals Using Chaotic Time-Series Analysis Techniques (in greek).  2001,  Thesis supervisor: P. Maragos