Nikolaos Mitianoudis

• BEng, MSc, PhD
• Professor (Associate) at Democritus University of Thrace

Biometrics play an important role in modern access control and security systems. The need of novel biometrics to complement traditional biometrics has been at the forefront of research. The Facial Blood Flow (FBF) biometric trait, recently proposed by our team, is a spatio-temporal representation of facial blood flow, constructed using motion magni...

A fundamental task in computer vision is the process of differentiation and identification of different objects or entities in a visual scene using semantic segmentation methods. The advancement of transformer networks has surpassed traditional convolutional neural network (CNN) architectures in terms of segmentation performance. The continuous pur...

This study investigates the potential of low-cost infrared cameras for non-contact monitoring of blood pressure (BP) in individuals with fragile health, particularly the elderly. Previous research has shown success in developing non-contact BP monitoring using RGB cameras. In this study, the Eulerian Video Magnification (EVM) technique is employed...

Estimation of depth in two-dimensional images is among the challenging topics in Computer Vision. This is a well-studied but also an ill-posed problem, which has long been the focus of intense research. This paper is an in-depth review of the topic, presenting two aspects, one that considers the mechanisms of human depth perception, and another tha...

This work describes a methodology for sound event detection in domestic environments. Efficient solutions in this task can support the autonomous living of the elderly. The methodology deals with the “Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE)” 2023, and more specifically with Task 4a “Sound event detection of d...

This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video M...

The limited depth of field of optical lenses, makes multi-focus image fusion (MFIF) algorithms of vital importance. Lately, Convolutional Neural Networks (CNN) have been widely adopted in MFIF methods, however their predictions mostly lack structure and are limited by the size of the receptive field. Moreover, since images have noise due to various...

We are happy to announce that our (National Technical University of Athens & Democritus University of Thrace) new International MSc Program on “TECHNOLOGICAL AND MANAGEMENT ADVANCES ON INTELLIGENT TRANSPORTATION ELECTRIFICATION SYSTEMS” has published its 1st Call for Applicants! Perspective Candidates from Greece, the EU and abroad, are welcome to...

The market efficiency theory suggests that stock market pricing reflects all publicly available information regarding a given stock. To outperform the market, a shareholder must study the markets price volume patterns and predict human behaviour and tendencies. Except for conventional approaches based on fundamental or technical analysis, new tools...

The market efficiency theory suggests that stock market pricing reflects all publicly available information regarding a given stock. To outperform the market, a shareholder must study the markets price volume patterns and predict human behaviour and tendencies. Except for conventional approaches based on fundamental or technical analysis, new tools...

Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important. Transform-domain methods have been applied to image fusion, however, they are likely to produce artifacts. In order to cope with...

The recent boom of artificial Neural Networks (NN) has shown that NN can provide viable solutions to a variety of problems. However, their complexity and the lack of efficient interpretation of NN architectures (commonly considered black box techniques) has adverse effects on the optimization of each NN architecture. One cannot simply use a generic...

The music source separation problem, where the task at hand is to estimate the audio components that are present in a mixture, has been at the centre of research activity for a long time. In more recent frameworks, the problem is tackled by creating deep learning models, which attempt to extract information from each component by using Short-Time F...

In neural networks, a vital component in the learning and inference process is the activation function. There are many different approaches, but only nonlinear activation functions allow such networks to compute non-trivial problems by using only a small number of nodes, and such activation functions are called nonlinearities. With the emergence of...

Managing random hardware faults requires the faults to be detected online, thus simplifying recovery. Algorithm-based fault tolerance has been proposed as a low-cost mechanism to check online the result of computations against random hardware failures. In this case, the checksum of the actual result is checked against a predicted checksum computed...

Pattern Recognition and Classification is considered one of the most promising applications in the scientific field of Artificial Neural Networks (ANN). However, regardless of the vast scientific advances in almost every aspect of the technology and mathematics, neural networks still need to be fairly large and complex (i.e., deep), in order to pro...

BibTeX file: A. Maniatopoulos, A. Gazis, V.P. Pallikaras and N. Mitianoudis, "Artificial Neural Network Performance Boost using Probabilistic Recovery with Fast Cascade Training", in NAUN International Journal of Circuits, Systems and Signal Processing, vol. 14, pp. 847-854, November, 2020

The audio source separation problem is a well-known problem that was addressed using a variety of techniques. A common setback in these techniques is that the total number of sound sources in the audio mixture must be known beforehand. However, this knowledge is not always available and thus needs to be estimated. Many approaches have attempted to...

This paper presents a novel scheme for speech dereverberation. The core of our method is a two-stage single-channel speech enhancement scheme. Degraded speech obtains a sparser representation of the linear prediction residual in the first stage of our proposed scheme by applying orthogonal matching pursuit on overcomplete bases, trained by the K-SV...

In this paper, a novel multi-focus image fusion algorithm based on Conditional Random Field optimization (mf-CRF) is proposed. It is based on an unary term that includes the combined activity estimation of both high and low frequencies of the input images, while a spatially varying smoothness term is introduced, in order to align the graph-cut solu...

Modern imaging applications have increased the demand for High-Definition Range (HDR) imaging. Nonetheless, HDR imaging is not easily available with low-cost imaging sensors, since their dynamic range is rather limited. A viable solution to HDR imaging via low-cost imaging sensors is the synthesis of multiple-exposure images. A low-cost sensor can...

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem’s scalabity...

In this paper, a thorough theoretical analysis on the construction of multi-dimensional directional steerable filters is given. Steerable filters have been constructed for up to three dimensions. We extend the relevant theory to multiple dimensions and construct multi-dimensional steerable filters, as well as quadrature pairs of such filters. Formu...

New Special Issue at the Journal of Imaging

In this paper, the problem of joint disparity and motion estimation from stereo image sequences is formulated in the spatiotemporal frequency domain, and a novel steerable filter-based approach is proposed. Our rationale behind coupling the two problems is that according to experimental evidence in the literature, the biological visual mechanisms f...

This paper investigates the potential of using a novel Hermite polynomial neural network to model shoreline realignment along an urban beach fronted by a highly irregular beachrock reef. Modeling takes place on the basis of a number of input variables related to reef morphology and wave forcing, whereas the output variable is time series of shoreli...

Cellular Automata (CA) have been considered one of the most pronounced parallel computational tools in the recent era of nature and bio-inspired computing. Taking advantage of their local connectivity, the simplicity of their design and their inherent parallelism, CA can be effectively applied to many image processing tasks. In this paper, a CA app...

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 Generalised Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fishe...

The instantaneous underdetermined audio source separation problem of K-sensors, L-sources mixing scenario (where K < L) has been addressed by many different approaches, provided the sources remain quite distinct in the virtual positioning space spanned by the sensors. This problem can be tackled as a directional clustering problem along the source...

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem's scalabity...

Accepting non-linearities as an endemic feature of financial data, this paper re-examines Cochrane's "new fact in finance" hypothesis (Cochrane, Economic Perspectives -FRB of Chicago 23, 36-58, 1999). By implementing two methods, frequently encountered in digital signal processing analysis, (Undecimated Wavelet Transform and Empirical Mode Decompos...

The latest advancement in imaging applications has increased the need for more High Definition Range (HDR) imaging, which is not easily attainable by common imaging sensors. However, the use of multiple exposure images, that cover multiple exposure settings for the captured scene, and their combination in a single image via image fusion has been pr...

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem’s scalabity...

The instantaneous underdetermined audio source separation problem of K-sensors, L-sources mixing scenario (where K < L) has been addressed by many different approaches, provided the sources remain quite distinct in the virtual positioning space spanned by the sensors. This problem can be tackled as a directional clustering problem along the source...

The aim of this research is to achieve spatial consistency of the UV map. We present an approach to produce a fully spatially consistent UV mapping based on the planar parameterisation of the mesh. We apply our method on a 3D digital replica of an ancient Greek Lekythos vessel. We parameterise the mesh of a 3D model onto a unit square 2D plane usin...

Thanks to their ability to store information in a continuous (analog) form, memristors are termed as well-suited for several real-time signal processing tasks. In this context, here we present a memristive circular buffer, using memristor and its multi-bit storage ability to temporarily store encoded information in a compact form, thus improving th...

Electric potential (EP) signals are produced in plants through intracellular processes, in response to external stimuli (e.g., watering, mechanical stress, light, and acquisition of nutrients). However, wireless transmission of a massive amount of biologic EP signals (from one or multiple plants) is hindered by existing battery-operated wireless te...

In this paper, we address the document image binarization problem with a three-stage procedure. First, possible stains and general document background information are removed from the image through a background removal stage. The remaining misclassified background and character pixels are then separated using a Local Co-occurrence Mapping, local co...

In this paper, the authors exploit a multispectral image representation to perform more accurate document image binarisation compared to previous color representations. In the first stage, image fusion is employed to create a 'document' and a 'background' image. In the second stage, the FastICA algorithm is used to perform background subtraction. I...

In this work, the 3D flow estimation problem is formulated in the 4D spatiotemporal frequency domain, and it is shown that 3D motion manifests itself as energy concentration along hyper-planes in that domain. Based on this, the construction and use of appropriate directional multidimensional 'steerable' filters, which can extract directional energy...

Hand gesture recognition has gained the interest of many researchers in recent years as it has become one of the most popular Human Computer Interfaces. The first step in most of the vision based gesture recognition systems is the hand region detection and segmentation. This segmentation can be a particularly challenge task when it comes to complex...

Document Image Binarization refers to the task of transforming a scanned image of a handwritten or printed document into a bi-level representation containing only charac- ters and background. Here, we address the historic document image binarization problem using a three-stage methodology. Firstly, we remove possible stains and noise from the docum...

The problem of underdetermined audio source separation has been explored in the literature for many years. The instantaneous \(K\)-sensors, \(L\)-sources mixing scenario (where \(K<L\)) has been tackled by many different approaches, provided the sources remain quite distinct in the virtual positioning space spanned by the sensors. In this case, the...

The Virtual Labs material is available for download at: http://vlabs.ihu.edu.gr/index.php?id=30 Please, cite the work: MASTERS: A Virtual Lab on Multimedia Systems for Telecommunications, Medical, and Remote Sensing Applications Dimitrios S. Alexiadis, Nikolaos Mitianoudis IEEE Transactions on Education 05/2013 56(2):227-234.

Image Fusion is the procedure of combining useful features from multiple sensor image inputs to form a single composite image. In this work, the authors extend the previously proposed Image Fusion framework, based on self-trained Independent Component Analysis (ICA) bases, to a more sophisticated region-based Image Fusion system. The input images a...

In an audio fingerprinting system, the song identification task should be performed within a few seconds. To address the need for fast and robust song identification system, we design fingerprints based on Gaussian Mixture Modeling (GMM) of delta Mel-frequency cepstrum coefficients (ΔMFCC) or delta chroma features (Δchroma). In order to summarize t...

Digital signal processing (DSP) has been an integral part of most electrical, electronic, and computer engineering curricula. The applications of DSP in multimedia (audio, image, video) storage, transmission, and analysis are also widely taught at both the undergraduate and post-graduate levels, as digital multimedia can be encountered in most huma...

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-Fishe...

The aim of this paper is to provide an algorithm for image fusion which combines the techniques of Chebyshev polynomial (CP) approximation and independent component analysis (ICA), based on the regional information of input images. We present a region-based method that combines the merits of both techniques. It utilises segmentation to identify edg...

This report documents in detail the research carried out by the author throughout his first year. The paper presents a novel method for fusing images in a domain concerning multiple sensors and modalities. Using Chebyshev polynomials as basis functions, the image is decomposed to perform fusion at feature level. Results show favourable performance...

In this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian distribution and the von Mises distribution, proposed to model sparse directional data. The distribution’s parameters are estimated using Maximum-Likelihood Estimation ove...

Remote Sensing systems enhance the spatial quality of low-resolution Multi-Spectral (MS) images using information from Pan-chromatic (PAN) images under the pansharpening framework. Most decimated multi-resolution pansharpening approaches upsample the low-resolution MS image to match the resolution of the PAN image. Consequently, a multi-level wavel...

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-...

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 image's intensity. Even though this assumption is valid for all applications invol...

Image fusion systems aim at transferring \interesting" information from the input sensor images to the fused image. The common assumption for most fusion ap- proaches is the existence of a high-quality reference image signal for all image parts in all input sensor images. In the case that there are common degraded areas in at least one of the input...

The purpose of image fusion is to create a perceptually enhanced image from a set of multi-focus or multi-sensors images. In the methods we are about to describe we do not a priori know the ground truth image: these are blind fusion methods. There are mainly two groups of fusion methods depending on the signal domain they are applied: spatial domai...

Blind audio de-reverberation, is the problem of removing reverb from an audio signal without having explicit data regarding the system and/or the input signal. Blind audio de-reverberation is a more difficult signal-processing task than ordinary de-reverberation based on deconvolution. In this paper different blind de-reverberation algorithms deriv...

Image fusion is the procedure of combining useful features from multiple sensor image inputs to a single composite image. In this work, the authors revise 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...

A scheme, based on principal component analysis (PCA), is proposed that can be used for the recognition of 2-D planar shapes under affine transformations. A PCA step is first used to map the object boundary to its canonical form, reducing the problem of the nonuniform sampling of the object contour introduced by the affine transformation. Then, a P...

Image fusion systems aim at transferring ‘interesting’ information from the input sensor images to the fused image. The common assumption for most fusion approaches is the existence of a high-quality reference image signal for all image parts in all input sensor images. In the case that there are common degraded areas in at least one of the input i...

The purpose of the Applied Multi-dimensional Fusion Project is to investigate the benefits that data fusion and related techniques may bring to future military Intelligence Surveillance Target Acquisition and Reconnaissance systems. In the course of this work, it is intended to show the practical application of some of the best multi-dimensional fu...

In a previous work, the authors have introduced a Mixture of Laplacians model in order to cluster the observed data into the sound sources that exist in an underdetermined two-sensor setup. Since the assumed linear support of the ordinary Laplacian distribution is not valid to model angular quantities, such as the Direction of Arrival to the set of...

Independent Component Analysis (ICA) is a statistical method for expressing an ob- served set of random vectors as a linear combination of statistically independent com- ponents. This paper tackles the task of comparing two ICA algorithms, in terms of their efficiency for compact representation of market securities. A recently developed sequential...

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...

The problem of image segmentation using intensity clustering approaches has been addressed in the literature. Grouping pixels of similar intensity to form clusters in an image have been tackled using a number of methods, such as the K-means (KM) algorithm. The K-harmonic means (KHM) was proposed to overcome the sensitivity of KM to centre initialis...

The problem of shape-based recognition of objects under affine transformations is considered. We focus on the construction of a robust and highly discriminative affine invariant function that can be used for within-class object recognition applications. Using the boundaries of the objects of interest, a training scheme, based on principal component...

The problem of blind separation of statistically independent sources from instantaneous mixtures, using the efficient framework of independent component analysis (ICA), has been widely addressed in the literature. In this letter, the authors propose a sequential blind signal extraction algorithm that attempts to identify smooth sources in instantan...

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 comb...

Independent Component Analysis (ICA) is a statistical method for expressing an observed set of random vectors as a linear combination of statistically independent components. This paper tackles the task of comparing two ICA algorithms, in terms of their efficiency for compact representation of market securities. A recently developed sequential blin...

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 problem of source separation of instantaneous mixtures has been addressed thoroughly in literature in the past. The assumption of statistical independence between the source signals, led to the introduction of independent component analysis (ICA). A number of methods, based on the ICA framework, can identify nonGaussian sources in instantaneous...

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 g...

In this paper, the authors address the permutation ambi- guity 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 sub- space methods f...

The problem of separating out a number of audio sources observed from an array of microphones in a real room environment has received a great deal of attention in the past decade. While there are now a number of workable methods that can even deal with relatively high reverberation (IEEE Trans Audio Speech Process, 2003; 11:489–497), a number of in...

The use of mixture of Gaussians (MoGs) for noisy and overcomplete independent component analysis (ICA) when the source distributions are very sparse is explored. The sparsity model can often be justified if an appropriate transform, such as the modified discrete cosine transform, is used. Given the sparsity assumption, a number of simplifying appro...

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 s...

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 emp...

We examine the problem of blind audio source separation using independent component analysis (ICA). In order to separate audio sources recorded in a real recording environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require working in th...

One of the most powerful techniques applied to blind audio source separation is Independent Component Analysis (ICA). For the separation of audio sources recorded in a real environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require work...

The authors present a new method to extract the mutual information for data from any number of channels from either a discrete or continuous system. This generalized mutual information allows for the estimation of the average number of redundant bits in a vector measurement. Thus it provides insight into the information shared between all channels...