Tania Stathaki

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.

Robust FFT-Based Scale-Invariant Image Registration with Image Gradients

We present a robust FFT-based approach to scale-invariant image registration. Our method relies on FFT-based correlation twice: once in the log-polar Fourier domain to estimate the scaling and rotation and once in the spatial domain to recover the residual translation. Previous methods based on the same principles are not robust. To equip our scheme with robustness and accuracy, we introduce modifications which tailor the method to the nature of images. First, we derive efficient log-polar Fourier representations by replacing image functions with complex gray-level edge maps. We show that this representation both captures the structure of salient image features and circumvents problems related to the low-pass nature of images, interpolation errors, border effects, and aliasing. Second, to recover the unknown parameters, we introduce the normalized gradient correlation. We show that, using image gradients to perform correlation, the errors induced by outliers are mapped to a uniform distribution for which our normalized gradient correlation features robust performance. Exhaustive experimentation with real images showed that, unlike any other Fourier-based correlation techniques, the proposed method was able to estimate translations, arbitrary rotations, and scale factors up to 6.

Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and Verification

We propose a robust approach to discriminant kernel-based feature extraction for face recognition and verification. We show, for the first time, how to perform the eigen analysis of the within-class scatter matrix directly in the feature space. This eigen analysis provides the eigenspectrum of its range space and the corresponding eigenvectors as well as the eigenvectors spanning its null space. Based on our analysis, we propose a kernel discriminant analysis (KDA) which combines eigenspectrum regularization with a feature-level scheme (ER-KDA). Finally, we combine the proposed ER-KDA with a nonlinear robust kernel particularly suitable for face recognition/verification applications which require robustness against outliers caused by occlusions and illumination changes. We applied the proposed framework to several popular databases (Yale, AR, XM2VTS) and achieved state-of-the-art performance for most of our experiments.

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.

A normalized robust mixed-norm adaptive algorithm for system identification

A normalized robust mixed-norm (NRMN) algorithm for system identification in the presence of impulsive noise is introduced. The standard robust mixed-norm (RMN) algorithm exhibits slow convergence, requires a stationary operating environment, and employs a constant step-size that needs to be determined a priori. To overcome these limitations, the proposed NRMN algorithm introduces a time-varying learning rate and, thus, no longer requires a stationary environment, a major drawback of the RMN algorithm. The proposed NRMN exhibits increased convergence rate and substantially reduces the steady-state coefficient error, as compared to the least mean square (LMS), normalized LMS (NLMS), least absolute deviation (LAD), and RMN algorithm.

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

Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations

In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) framework (EM-MSDA), which simultaneously derives the discriminant subspace and the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional DA. FSMSDA solves the subclass separation problem, that is, the situation in which the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other linear discriminant analysis variants.

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.

Iterative regularized least-mean mixed-norm image restoration

We develop a regularized mixed-norm image restoration algorithm to deal with various types of noise. A mixed-norm functional is introduced, which combines the least mean square (LMS) and the least mean fourth (LMF) functionals, as well as a smoothing functional. Two regularization parameters are introduced: one to determine the relative importance of the LMS and LMF functionals, which is a function of the kurtosis, and another to determine the relative importance of the smoothing functional. The two parameters are chosen in such a way that the proposed functional is convex, so that a unique minimizer exists. An iterative algorithm is utilized for obtaining the solution, and its convergence is analyzed. The novelty of the proposed algorithm is that no knowledge of the noise distribution is required, and the relative contributions of the LMS, the LMF, and the smoothing functionals are adjusted based on the partially restored image. Experimental results demonstrate the effectiveness of the proposed algorithm.