Nikolaos Mitianoudis

Pixel-based and region-based image fusion schemes using ICA bases

The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this paper, the authors test the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases in image fusion. The bases are obtained by offline training with images of similar context to the observed scene. The images are fused in the transform domain using novel pixel-based or region-based rules. The proposed schemes feature improved performance compared to traditional wavelet approaches with slightly increased computational complexity.

Audio source separation of convolutive mixtures

The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is blind source separation (BSS) using independent component analysis (ICA). The recording environment is usually modeled as convolutive. Previous research on ICA of instantaneous mixtures provided solid background for the separation of convolved mixtures. The authors revise current approaches on the subject and propose a fast frequency domain ICA framework, providing a solution for the apparent permutation problem encountered in these methods.

Optimal Contrast Correction for ICA-Based Fusion of Multimodal Images

In this paper, the authors revisit the previously proposed Image Fusion framework, based on self-trained independent component analysis (ICA) bases. In the original framework, equal importance was given to all input images in the reconstruction of the ldquofusedrdquo images intensity. Even though this assumption is valid for all applications involving sensors of the same modality, it might not be optimal in the case of multiple modality inputs of different intensity range. The authors propose a method for estimating the optimal intensity range (contrast) of the fused image via optimization of an image fusion index. The proposed approach can be employed in a general fusion scenario including multiple sensors.

Adaptive Image Fusion Using Ica Bases

Image fusion can be viewed as a process that incorporates essential information from different modality sensors into a composite image. The use of bases trained using independent component analysis (ICA) for image fusion has been highlighted recently. Common fusion rules can be used in the ICA fusion framework with promising results. In this paper, the authors propose an adaptive fusion scheme, based on the ICA fusion framework, that maximises the sparsity of the fusion image in the transform domain

Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models

In this paper, we explore the problem of sound source separation and identification from a two-sensor instantaneous mixture. The estimation of the mixing and the sources is performed using Laplacian mixture models (LMM). The proposed algorithm fits the model using batch processing of the observed data and performs separation using either a hard or a soft decision scheme. An extension of the algorithm to online source separation, where the samples are arriving in a real-time fashion, is also presented. The online version demonstrates several promising source separation possibilities in the case of nonstationary mixing.

Overcomplete source separation using Laplacian mixture models

The authors explore the use of Laplacian mixture models (LMMs) to address the overcomplete blind source separation problem in the case that the source signals are very sparse. A two-sensor setup was used to separate an instantaneous mixture of sources. A hard and a soft decision scheme were introduced to perform separation. The algorithm exhibits good performance as far as separation quality and convergence speed are concerned.

Permutation Alignment for Frequency Domain ICA Using Subspace Beamforming Methods

In this paper, the authors address the permutation ambiguity that exists in frequency domain Independent Component Analysis of convolutive mixtures. Many methods have been proposed to solve this ambiguity. Recently, a couple of beamforming approaches have been proposed to address this ambiguity. The authors explore the use of subspace methods for permutation alignment, in the case of equal number of sources and sensors.

A Unifying Approach to Moment-Based Shape Orientation and Symmetry Classification

In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods.

Using beamforming in the audio source separation problem

The problem of separating audio sources observed in a real room environment is a very challenging task, also known as the cocktail party problem. Much work has been presented on audio separation, even in cases of high reverb. However, various problems remain unsolved in a real-world scenario. In this paper, the authors review proposed solutions employing independent component analysis (ICA), discussing possible solutions to various problems that arise during the analysis (i.e. the permutation problem). In particular, the use of beamforming techniques in parallel with the ICA framework is discussed. Finally, some of the open problems in audio source separation are considered.

A Generalized Directional Laplacian Distribution : Estimation, Mixture Models and Audio Source Separation

Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations. In this work, a novel probability distribution is proposed to model multidimensional sparse directional data. The Generalized Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution. The distributions parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture. The proposed model can solve the general