Nikolas P. Galatsanos

University of Ioannina, Yannina, Epirus, Greece

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Publications (90)58.08 Total impact

  • S.P. Belekos · N.P. Galatsanos · A.K. Katsaggelos
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    ABSTRACT: In this paper we propose a class of SR algorithms for compressed video using the maximum a posteriori (MAP) approach. These algorithms utilize a novel multichannel image prior model which has already been presented mainly for uncompressed video, along with a new hierarchical Gaussian nonstationary version of the state-of-the-art quantization noise model. The relationship between model components and the decoded bitstream is also demonstrated. An additional novelty of this framework pertains to the transition flexibility from totally nonstationary algorithms used for compressed video to fully stationary algorithms used for raw video. Numerical simulations comparing the proposed models among themselves, verify the efficacy of the adopted multichannel nonstationary prior for different compression ratios, and the significant role of the nonstationary observation term.
    No preview · Article · Jan 2011
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    Ashraf A. Tahat · Nikolas P. Galatsanos

    Full-text · Article · Jan 2010
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    Dimitris Tzikas · Aristidis Likas · Nikolas P. Galatsanos

    Full-text · Article · Jan 2009 · IEEE Transactions on Image Processing
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    ABSTRACT: A relevance feedback (RF) approach for content-based image retrieval (CBIR) is proposed, which is based on Support Vector Machines (SVMs) and uses a feature selection technique to reduce the dimensionality of the image feature space. Specifically, each image is described by a multidimensional vector combining color, texture and shape information. In each RF round, the positive and negative examples provided by the user are used to determine a relatively small number of the most important features for the corresponding classification task, via a feature selection methodology. After the feature selection has been performed, an SVM classifier is trained to distinguish between relevant and irrelevant images according to the preferences of the user, using the restriction of the user examples on the set of selected features. The trained classifier is subsequently used to provide an updated ranking of the database images represented in the space of the selected features. Numerical experiments are presented that demonstrate the merits of the proposed relevance feedback methodology.
    Full-text · Conference Paper · Jan 2009
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    Nikolas P Galatsanos · Miles N Wernick · Aggelos K Katsaggelos · Rafael Molina
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    ABSTRACT: Multichannel images refer to collections of image channels that are not identical but exhibit strong between-channel correlations. Image recovery refers to the computation of an image from observed data that alone do not uniquely define the desired image. Important examples are image denoising, image deblurring, decoding of compressed images, and medical image reconstruction. This chapter focuses on the problem of image recovery as it applies specifically to multichannel images. It presents the multichannel observation model and reviews basic image recovery approaches. It also describes the explicit approach and illustrates it using an example of restoration of video image sequences. Further, the implicit approach is explained and is illustrated using an example of the reconstruction of time-varying medical images.
    Full-text · Article · Dec 2008
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    ABSTRACT: In this paper a relevance feedback (RF) approach for content based image retrieval (CBIR) is described and evaluated. The approach uses Gaussian mixture (GM) models of the image features and a query that is updated in a probabilistic manner. This update reflects the preferences of the user and is based on the models of both positive and negative feedback images. Retrieval is based on a recently proposed distance measure between probability density functions (pdfs), which can be computed in closed form for GM models. The proposed approach takes advantage of the form of this distance measure and updates it very efficiently based on the models of the user specified relevant and irrelevant images. For evaluation purposes, comparative experimental results are presented that demonstrate the merits of the proposed methodology.
    Full-text · Conference Paper · Nov 2008
  • Dimitris Tzikas · Aristidis Likas · Nikolas P. Galatsanos
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    ABSTRACT: Recently, sparse kernel methods such as the Relevance Vector Machine (RVM) have become very popular for solving regression problems. The sparsity and performance of these methods depend on selecting an appropriate kernel function, which is typically achieved using a cross-validation procedure. In this paper we propose a modification to the incremental RVM learning method, that also learns the location and scale parameters of Gaussian kernels during model training. More specifically, in order to effectively model signals with different characteristics at various locations, we learn different parameter values for each kernel, resulting in a very flexible model. In order to avoid overfitting we use a sparsity enforcing prior that controls the effective number of parameters of the model. Finally, we apply the proposed method to one-dimensional and two-dimensional artificial signals, and evaluate its performance on two real-world datasets.
    No preview · Conference Paper · Oct 2008
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    Ahmad Abu-Naser · Nikolas P Galatsanos · Miles N Wernick
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    ABSTRACT: Herein we investigate the problem of detecting and localizing a known signal in a photon-limited image, where Poisson noise is the dominant source of image degradation. For this purpose we developed and evaluated three new algorithms. The first two are based on the impulse restoration (IR) principle and the third is based on the generalized likelihood ratio test (GLRT). In the IR approach, the problem is formulated as one of restoring a delta function at the location of the desired object. In the GLRT approach, which is a well-known variation on the optimal likelihood ratio test, the problem is formulated as a hypothesis testing problem, in which the unknown background intensity of the image and the intensity scale of the object are obtained by maximum-likelihood estimation. We used Monte Carlo simulations and localization receiver operating characteristic curves (LROC) to evaluate the proposed algorithms quantitatively. LROC curves demonstrate the ability of an algorithm to detect and locate objects in a scene correctly. Our simulations demonstrate that the GLRT approach is superior to all other tested algorithms. 1.
    Preview · Article · Jul 2008
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    ABSTRACT: We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.
    Full-text · Article · Jul 2008 · IEEE Transactions on Medical Imaging
  • Antonis K. Mairgiotis · Nikolas P. Galatsanos · Yongyi Yang
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    ABSTRACT: In this paper, we propose a new family of watermark detectors for additive watermarks in digital images. These detectors are based on a recently proposed hierarchical, two-level image model, which was found to be beneficial for image recovery problems. The top level of this model is defined to exploit the spatially varying local statistics of the image, while the bottom level is used to characterize the image variations along two principal directions. Based on this model, we derive a class of detectors for the additive watermark detection problem, which include a generalized likelihood ratio, Bayesian, and Rao test detectors. We also propose methods to estimate the necessary parameters for these detectors. Our numerical experiments demonstrate that these new detectors can lead to superior performance to several state-of-the-art detectors.
    No preview · Article · Mar 2008 · IEEE Transactions on Information Forensics and Security
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    Giannis K. Chantas · Nikolas P. Galatsanos · Aristidis Likas · Michael Saunders

    Full-text · Article · Jan 2008 · IEEE Transactions on Image Processing
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    Vasileios Chasanis · Aristidis Likas · Nikolas P. Galatsanos
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    ABSTRACT: In this paper we describe a system for video rushes summarization. The basic problems of rushes videos are three. First, the presence of useless frames such as colorbars, monochrome frames and frames containing clapboards. Second, the repetition of similar segments produced from multiple takes of the same scene and finally, the efficient representation of the original video in the video summary. In the method we proposed herein, the input video is segmented into shots. Then, colorbars and monochrome frames are removed by checking their edge direction histogram, whereas frames containing clapboards are removed by checking their SIFT descriptors. Next, an enhanced spectral clustering algorithm that both estimates the number of clusters and employs the fast global k-means algorithm in the clustering stage after the eigenvector computation of the similarity matrix is used to extract the key-frames of each shot, to efficiently represent shot content. Similar shots are clustered in one group by comparing their key-frames using a sequence alignment algorithm. Each group is represented from the shot with the largest duration and the final video summary is generated by concatenating frames around the key-frames of each shot. Experiments on TRECVID 2008 Test Data indicate that our method exhibits good performance.
    Full-text · Conference Paper · Jan 2008
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    Konstantinos Blekas · Nikolas P. Galatsanos · Aristidis Likas

    Full-text · Conference Paper · Jan 2008
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    Dimitris Tzikas · Aristidis Likas · Nikolas P. Galatsanos
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    ABSTRACT: x n, tn)}N n=1. The weights are typically assigned those values that maximize the likelihood of the training set, however the training examples must be significantly more than the parameters in order to achieve good generalization performance. The RVM overcomes this limita- tion by following Bayesian principles and assuming prior knowledge for the model. Specifically, a suitable hierarchical prior distribution is assumed for the weights of
    Full-text · Article · Dec 2007 · International Journal of Artificial Intelligence Tools
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    ABSTRACT: Advancements in the diagnosis and prognosis of brain tumor patients, and thus in their survival and quality of life, can be achieved using biomarkers that facilitate improved tumor typing. We introduce and implement a combinatorial metabolic and molecular approach that applies state-of-the-art, high-resolution magic angle spinning (HRMAS) proton (1H) MRS and gene transcriptome profiling to intact brain tumor biopsies, to identify unique biomarker profiles of brain tumors. Our results show that samples as small as 2 mg can be successfully processed, the HRMAS 1H MRS procedure does not result in mRNA degradation, and minute mRNA amounts yield high-quality genomic data. The MRS and genomic analyses demonstrate that CNS tumors have altered levels of specific 1H MRS metabolites that directly correspond to altered expression of Kennedy pathway genes; and exhibit rapid phospholipid turnover, which coincides with upregulation of cell proliferation genes. The data also suggest Sonic Hedgehog pathway (SHH) dysregulation may play a role in anaplastic ganglioglioma pathogenesis. That a strong correlation is seen between the HRMAS 1H MRS and genomic data cross-validates and further demonstrates the biological relevance of the MRS results. Our combined metabolic/molecular MRS/genomic approach provides insights into the biology of anaplastic ganglioglioma and a new potential tumor typing methodology that could aid neurologists and neurosurgeons to improve the diagnosis, treatment, and ongoing evaluation of brain tumor patients.
    Full-text · Article · Sep 2007 · International Journal of Molecular Medicine
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    Dimitris Tzikas · Aristidis Likas · Nikolas P. Galatsanos
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    ABSTRACT: In this paper we present a new Bayesian model for the blind image deconvolution (BID) problem. The main novelties of this model are three. The first one is the use of a sparse kernel-based model for the point spread function (PSF) that allows estimation of both PSF shape and support. The second one is a robust distribution of the BID model errors and the third novelty is an image prior that preserves edges of the reconstructed image. Sparseness, robustness and preservation of edges is achieved by using priors that are based on the Student-t probability density function (pdf). The Variational methodology is used to solve the corresponding Bayesian model. Numerical experiments are presented that demonstrate the advantages of this model as compared to previous Gaussian based ones.
    Full-text · Conference Paper · Sep 2007
  • Aristidis Likas · Nikolas Galatsanos

    No preview · Chapter · May 2007
  • Dimitris Tzikas · Aristidis Likas · Nikolas P. Galatsanos
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    ABSTRACT: In this paper we present a new Bayesian model for the blind image deconvolution (BID) problem. The main novelties of this model are two. First, a sparse kernel based representation of the point spread function (PSF) that allows for the first time estimation of both PSF shape and support. Second, a non Gaussian heavy tail prior for the model noise to make it robust to large errors encountered in BID when little prior knowledge is available about both image and PSE Sparseness and robustness are achieved by introducing Student-t priors both for the PSF and the noise. A Variational methodology is proposed to solve this Bayesian model. Numerical experiments are presented both with real and simulated data that demonstrate the advantages of this model as compared to previous Gaussian based ones.
    No preview · Conference Paper · Jan 2007
  • Christophoros Nikou · Nikolas P. Galatsanos · Aristidis Likas

    No preview · Article · Jan 2007
  • Dimitris Tzikas · Aristidis Likas · Nikolas P. Galatsanos
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    ABSTRACT: The Relevance Vector Machine(RVM) is a widely accepted Bayesian model commonly used for regression and classification tasks. In this paper we propose a multikernel version of the RVM and present an alternative inference algorithm based on Fourier domain computation to solve this model for large scale problems, e.g. images. We then apply the proposed method to the object detection problem with promising results. where {`m(x)}M m=1 is a set of basis functions. Learning on such a model, is the process of estimating the weights {wm}M m=1 given a training set {(xn,tn)}N n=1. The weights are typically assigned those values that maximize the likelihood of the training set, however the training examples must be significantly more than the parameters in order to achieve good generalization performance. The RVM overcomes this limitation by following Bayesian principles and assuming prior knowledge for the model. Specifically, a suitable hierarchical prior distribution is assumed for the weights of the model, which has most probability mass con- centrated in sparse solutions, meaning that it forces most of the weights to be assigned to zero values (1). This results in pruning basis functions that are not suciently
    No preview · Conference Paper · May 2006

Publication Stats

1k Citations
58.08 Total Impact Points

Institutions

  • 2004-2008
    • University of Ioannina
      • Laboratory of Computer Science
      Yannina, Epirus, Greece
  • 1992-2008
    • Illinois Institute of Technology
      • Department of Electrical & Computer Engineering
      Chicago, Illinois, United States
  • 2002
    • Massachusetts Institute of Technology
      Cambridge, Massachusetts, United States
  • 2000
    • Northeastern University
      • Department of Electrical and Computer Engineering
      Boston, Massachusetts, United States
  • 1994-1996
    • AT&T Labs
      Austin, Texas, United States
  • 1993
    • Northwestern University
      • Department of Electrical Engineering and Computer Science
      Evanston, Illinois, United States