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Projects Here are listed all UPCV group Projects. At the moment, this page is under maintenance. In a few days we will add more projects.


Pose-based Human Action Recognition via Sparse Representation in Dissimilarity Space

Projects

Human actions can be considered as a sequence of body poses over time, usually represented by coordinates corresponding to human skeleton models. Recently, a variety of low-cost devices have been released, able to produce markerless real time pose estimation. Nevertheless, limitations of the incorporated RGB-D sensors can produce inaccuracies, necessitating the utilization of alternative representation and classification schemes in order to boost performance. In this context, we propose a method for action recognition where skeletal data are initially processed in order to obtain robust and invariant pose representations and then vectors of dissimilarities to a set of prototype actions are computed. The task of recognition is performed in the dissimilarity space using Sparse Representation. A new publicly available dataset is introduced in this paper, created for evaluation purposes. The proposed method was also evaluated on other public datasets, and the results are compared to those of similar methods.
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Gait-based Gender Recognition using Pose Information for Real Time Applications

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Biological cues inherent in human motion play an important role in the context of social communication. While recognizing the gender of other people is important for humans, security, advertisement and population statistics systems could also benefit from such kind of information. In this work for first time we propose a method suitable for real time gait based gender recognition relying on poses estimated from depth images. We provide evidence that pose based representation estimated by depth images could greatly benefit the problem of gait analysis. Given a gait sequence, in every frame the dynamics of gait motion are encoded using an angular representation. In particular several skeletal primitives are expressed as two Euler angles that cast votes into aggregated histograms. These histograms are then normalized, concatenated and projected onto a PCA basis in order to form the final sequence descriptor. We evaluated our method on a newly created dataset -UPCVgait - captured with Microsoft Kinect, consisting of 5 gait sequences performed by 30 subjects. An RBF kernel SVM used for classification in a leave one person out scheme on gait sequences of arbitrary length as well as on variable
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Unsupervised Music Segmentation via Multi-Scale Processing of Compressive Features’ Representation

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We present an automated method for unsupervised detection of structural boundaries in musical recordings. The proposed method utilizes a compressed representation of features capturing timbre and chroma, in an 1-D time series derived via PCA. Time delay embedding and multi-scale comparison using the Wald–Wolfowitz statistical test are incorporated in order to calculate a Self Dissimilarity Matrix. A novelty curve is estimated by convolving an appropriate kernel along the main diagonal of the matrix, while the structural boundaries are located on the local maxima of the derived curve. We evaluate the proposed method on a popular dataset, using two different ground truth annotations. We demonstrate that the 1-D compressed representation of features contains enough information in order to detect boundaries with high precision, outperforming several methods from the literature.
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Low-level Visual Saliency with Application on Aerial Imagery

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In this work, a method for the construction of low-level saliency maps is presented in tandem with their evaluation on a set of aerial images. One of the key inspirations for the current research lies on the observation that, usually, the most significant manmade structures in a wide-field aerial image resemble the low-level features that can be detected with a bottom-up saliency map. Aerial photography comprises, hence, a natural domain of application for a method that computationally models low-level saliency. With the employment of mechanisms analogous to the neural functions that drive human attention, we propose a bio-inspired framework based on sparse coding for the extraction of information about saliency. The suggested algorithm is then evaluated on a novel dataset that has been constructed with the utilization of aerial images and the corresponding manually designed ground truth binary maps of salient structures. The results demonstrate the efficiency of the proposed scheme to highlight conspicuous locations in aerial images, revealing the perspectives on the employment of low-level saliency maps in aerial imaging systems.
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Human eye movements as a trait for biometrical identification

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This research work proposes an innovative processing scheme for the exploitation of eye movement dynamics on the field of biometrical identification. As the mechanisms that derive eye movements highly depend on each person's idiosyncrasies, cues that reflect at a certain extent individual characteristics may be captured and subsequently deployed for the implementation of a robust identification system. Our methodology involves the employment of a non - parametric statistical test, the multivariate Wald - Wolfowitz test (WW - test), in order to compare the distributions of saccadic velocity and acceleration features, which are extracted while a person fixates on visual stimuli. In the evaluation section we use two publicly available datasets that supply recorded eye movements from a number of subjects during the observation of a moving spot on a computer screen. The resulting identification rates exhibit the efficacy of the suggested scheme to adequately segregate people according to their eye movement traits.
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HEp-2 Cells Classification via fusion of morphological and textural features

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Abstract- Autoimmune diseases are proven to be connected with the occurrence of autoantibodies in patient serum. Antinuclear autoantibodies (ANAs) identification can be accomplished in a laboratory using indirect immunofluorescence (IIF) imaging. ANAs are characterized by specific "visual" patterns on a humane epithelial cell line (HEp-2). The identification stage is usually done by trained and highly qualified physicians through visual inspection of slides using a fluorescence microscope. The presence of subjectivity in the identification process, the inter- observer variability, the increasing demand of highly specialized personnel, suggest that a realization of an automatic classification system is of great significance for the field of autoimmune diseases diagnosis. Moreover CAD systems can be used in a collaborative scheme in order to augment the physicians' capabilities. In this paper a system for automatic classification of staining patterns on single-cell fluorescence images is proposed. Our method utilizes morphological features extracted from a set of binary images derived via multi-level thresholding of fluorescence images. Furthermore, a modified version of Uniform Local Binary Patterns descriptor is incorporated in order to capture local textural information. The classification is performed using a non-linear SVM Classifier. The proposed method is evaluated using a publicly available dataset, recently released for the purposes of HEP-2 Cells classification competition at ICPR 2012, achieving up to 95.9% overall classification accuracy.
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Biometric identification based on the eye movements and graph matching techniques

Projects

Abstract- The last few years a growing research interest has aroused in the field of biometrics, concerning the use of brain dependent characteristics generally known as behavioral features. Human eyes, often referred as the gates to the soul, can possibly comprise a rich source of idiosyncratic information which may be used for the recognition of an individual's identity. In this paper an innovative experiment and a novel processing approach for the human eye movements is implemented, ultimately aiming at the biometric segregation of individual persons. In our experiment, the subjects observe face images while their eye movements are being monitored, providing information about each participant's attention spots. The implemented method treats eye trajectories as 2-D distributions of points on the image plane. The efficiency of graph objects in the representation of structural information motivated us on the utilization of a non-parametric multivariate graph-based measure for the comparison of eye movement signals, yielding promising results at the task of identification according to behavioral characteristics of an individual.
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