CHRISTOPHOROS NIKOU

Dr. CHRISTOPHOROS NIKOU

University of Ioannina, Department of Computer Science and Engineering
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Academic Career Length

29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 November 2015 in Frontiers in Robotics and AI
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Journal Article
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Michalis VrigkasMichalis Vrigkas
Michalis Vrigkas
CHRISTOPHOROS NIKOUCHRISTOPHOROS NIKOU
CHRISTOPHOROS NIKOU
IKIoannis A. Kakadiaris
Ioannis A. Kakadiaris
Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.
19 March 2007 in IEEE Transactions on Image Processing
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Journal Article
Closed access
CHRISTOPHOROS NIKOUCHRISTOPHOROS NIKOU
CHRISTOPHOROS NIKOU
NGNikolaos P. Galatsanos
Nikolaos P. Galatsanos
ALAristidis C. Likas
Aristidis C. Likas
We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation
8 April 2010 in IEEE Transactions on Image Processing
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CHRISTOPHOROS NIKOUCHRISTOPHOROS NIKOU
CHRISTOPHOROS NIKOU
ALAristidis C. Likas
Aristidis C. Likas
NGNikolaos P. Galatsanos
Nikolaos P. Galatsanos
A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet parameters to impose smoothness. The main advantages of this model are two. First, it explicitly models the mixing proportions as probability vectors and simultaneously imposes spatial smoothness. Second, it results in closed form parameter updates using a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Previous efforts on this problem used models that did not model the mixing proportions explicitly as probability vectors or could not be solved exactly requiring either time consuming Markov Chain Monte Carlo (MCMC) or inexact variational approximation methods. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation compared to other GMM-based approaches. The model is also successfully compared to state of the art image segmentation methods in clustering both natural images and images degraded by noise.
1 July 1998 in NeuroImage
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CHRISTOPHOROS NIKOUCHRISTOPHOROS NIKOU
CHRISTOPHOROS NIKOU
FHFabrice Heitz
Fabrice Heitz
JAJean-Paul Armspach
Jean-Paul Armspach
INIzzie Jacques NAMER
Izzie Jacques NAMER
DGDaniel Grucker
Daniel Grucker
1 August 1999 in Pattern Recognition
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Journal Article
Closed access
CHRISTOPHOROS NIKOUCHRISTOPHOROS NIKOU
CHRISTOPHOROS NIKOU
FHFabrice Heitz
Fabrice Heitz
JAJean-Paul Armspach
Jean-Paul Armspach
1 September 2007 in IEEE International Conference on Image Processing (ICIP)
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Conference Paper
Closed access
GSGiorgos Sfikas
Giorgos Sfikas
CHRISTOPHOROS NIKOUCHRISTOPHOROS NIKOU
CHRISTOPHOROS NIKOU
NGNikolaos Galatsanos
Nikolaos Galatsanos
Gaussian mixture models have been widely used in image segmentation. However, such models are sensitive to outliers. In this paper, we consider a robust model for image segmentation based on mixtures of Student's t-distributions which have heavier tails than Gaussian and thus are not sensitive to outliers. The t-distribution is one of the few heavy tailed probability density functions (pdf) closely related to the Gaussian, that gives tractable maximum likelihood inference via the Expectation-Maximization (EM) algorithm. Numerical experiments that demonstrate the properties of the proposed model for image segmentation are presented.