Publications
186 results found
ElMikaty M, Stathaki P, 2017, Detection of cars in high-resolution aerial images of complex urban environments, IEEE Transactions on Geoscience and Remote Sensing, Vol: 55, Pages: 5913-5924, ISSN: 0196-2892
Detection of small targets, more specifically cars, in aerial images of urban scenes, has various applications in several domains, such as surveillance, military, remote sensing, and others. This is a tremendously challenging problem, mainly because of the significant interclass similarity among objects in urban environments, e.g., cars and certain types of nontarget objects, such as buildings' roofs and windows. These nontarget objects often possess very similar visual appearance to that of cars making it hard to separate the car and the noncar classes. Accordingly, most past works experienced low precision rates at high recall rates. In this paper, a novel framework is introduced that achieves a higher precision rate at a given recall than the state of the art. The proposed framework adopts a sliding-window approach and it consists of four stages, namely, window evaluation, extraction and encoding of features, classification, and postprocessing. This paper introduces a new way to derive descriptors that encode the local distributions of gradients, colors, and texture. Image descriptors characterize the aforementioned cues using adaptive cell distributions, wherein the distribution of cells within a detection window is a function of its dominant orientation, and hence, neither the rotation of the patch under examination nor the computation of descriptors at different orientations is required. The performance of the proposed framework has been evaluated on the challenging Vaihingen and Overhead Imagery Research data sets. Results demonstrate the superiority of the proposed framework to the state of the art.
ElMikaty M, Stathaki P, 2017, Detection of cars in complex urban areas, IAPR Conference on Machine Vision Applications, Publisher: IEEE
Detection of cars in airborne images of typical urbanareas has various applications in several domains,such as surveillance, military and remote sensing. Itis a tremendously-challenging problem, mainly becauseof the significant inter-class similarity among variousobjects in urban environments. In this paper, a novelframework is introduced that adopts a sliding-windowapproach and it depicts, in a novel way, the local distributionof gradients, colours and texture. A linear supportvector machine classifier is used to differentiatebetween descriptors that belong to cars and descriptorsthat belong to other objects in a hyperspace of 3838dimensions. Descriptors are computed over a newlyproposedadaptive distribution of cells that enables theuse of various rotation-variant image descriptors. Theproposed framework has been evaluated on the Vaihingendataset and results corroborate its superiority as itachieves a higher precision for a given recall than thestate of the art.
Liu T, Stathaki, 2017, Fast Head-Shoulder Proposal for Scare-aware Pedestrian Detection, International Conference on Pervasive Technologies Related to Assistive Environments
Huang, Liu T, Dragotti, et al., 2017, SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests, Computer Vision and Pattern Recognition Workshops
Liu T, Stathaki T, 2017, Fast Head-Shoulder Proposal for Deformable Part Model based Pedestrian Detection, International Conference on Digital Signal Processing, Publisher: IEEE, Pages: 457-461, ISSN: 2165-3577
In this paper we propose a fast head-shoulder detector as a means to facilitating faster pedestrian detection. The proposed approach is based on the observation that human head-shoulder regions share relatively robust features. The purpose is to address the problem of high computational speed of the deformable part model (DPM) detector by selecting candidate regions with higher likelihood to contain pedestrians. The proposed head-shoulder detector is based on the simple, yet effective normed gradient features. Head-shoulder detector outputs regions which are strong candidates for the presence of pedestrians and therefore, pedestrian detection processes are performed only within these regions, avoiding exhaustive sliding window search across the entire test image. Additionally, a two-pedestrian detector is applied to reinforce the detection accuracy especially in scenarios where pedestrians are close to each other. Our experiments on the INRIA dataset indicate that the proposed pedestrian detection method achieves comparable detection rate to the DPM detector, with improved speed of implementation.
Stathaki P, Konstantinidis D, Argyriou V, et al., 2017, Building detection using enhanced HOG-LBP features and region refinement processes, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol: 10, Pages: 888-905, ISSN: 1939-1404
Building detection from two-dimensional high-resolution satellite images is a computer vision, photogrammetry, and remote sensing task that has arisen in the last decades with the advances in sensors technology and can be utilized in several applications that require the creation of urban maps or the study of urban changes. However, the variety of irrelevant objects that appear in an urban environment and resemble buildings, and the significant variations in the shape and generally the appearance of buildings render building detection a quite demanding task. As a result, automated methods that can robustly detect buildings in satellite images are necessary. To this end, we propose a building detection method that consists of two modules. The first module is a feature detector that extracts histograms of oriented gradients (HOG) and local binary patterns (LBP) from image regions. Using a novel approach, a support vector machine classifier is trained with the introduction of a special denoising distance measure for the computation of distances between HOG-LBP descriptors before their classification to the building or nonbuilding class. The second module consists of a set of region refinement processes that employs the output of the HOG-LBP detector in the form of detected rectangular image regions. Image segmentation is performed and a novel building recognition methodology is proposed to accurately identify building regions, while simultaneously discard false detections of the first module of the proposed method. We demonstrate that the proposed methodology can robustly detect buildings from satellite images and outperforms state-of-the-art building detection methods.
Liu T, Stathaki P, Copin V, 2016, Human detection from ground truth cameras through combined use of Histogram of Oriented Gradients and body part models, International Conference and Computer Vision Theory and Applications
Stathaki P, Konstantinidis D, Argyriou V, et al., 2016, A 3D feature for building segmentation based on shape-from-shading, International Conference and Computer Vision Theory and Applications
Stathaki P, Konstantinidis D, Argyriou V, et al., 2016, A probabilistic feature fusion for building detection in satellite images, International Conference and Computer Vision Theory and Applications
Zhao X, Jiang Y, Stathaki T, et al., 2016, Gait recognition method for arbitrary straight walking paths using appearance conversion machine, Neurocomputing, Vol: 173, Pages: 530-540, ISSN: 0925-2312
We investigate the problem of multi-view human gait recognition along any straight walking paths. It is observed that the gait appearance changes as the view changes while certain amount of correlated information exists among different views. Taking advantage of that type of correlation, a multi-view gait recognition method is proposed in this paper. First, we estimate the viewing angle of the monitor equipment in terms of the probe subject. To this end, our method considers this as a classification problem, where the classification signals are the viewing angles, and the classification features are the elements of the transformation matrix that is estimated by the Transformation Invariant Low-Rank Texture (TILT) algorithm. Then, the gallery gait appearances are converted to the view of the probe subject using the proposed Appearance Conversion Machine (ACM), where the gait features of the spatially neighbouring pixels of the gait feature are considered as the correlated information of the two views. In the end, a similarity measurement is applied on the converted gait appearance and the testing gait appearance. Experiments on the CASIA-B multi-view gait database show that the proposed gait recognition method outperforms the state-of-the-art under most views.
Zhao X, Jiang Y, Stathaki T, 2016, A novel low false alarm rate pedestrian detection framework based on single depth images, Image and Vision Computing, Vol: 45, Pages: 11-21, ISSN: 1872-8138
Pedestrian detection is an important image understanding problem with many potential applications. There has been little success in creating an algorithm which exhibits a high detection rate while keeping the false alarm in a relatively low rate. This paper presents a method designed to resolve this problem. The proposed method uses the Kinect or any similar type of sensors which facilitate the extraction of a distinct foreground. Then potential regions, which are candidates for the presence of human(s), are detected by employing the widely used Histogram of Oriented Gradients (HOG) technique, which performs well in terms of good detection rates but suffers from significantly high false alarm rates. Our method applies a sequence of operations to eliminate the false alarms produced by the HOG detector based on investigating the fine details of local shape information. Local shape information can be identified by efficient utilization of the edge points which, in this work, are used to formulate the so called Shape Context (SC) model. The proposed detection framework is divided in four sequential stages, with each stage aiming at refining the detection results of the previous stage. In addition, our approach employs a pre-evaluation stage to pre-screen and restrict further detection results. Extensive experimental results on the dataset created by the authors, involves 673 images collected from 11 different scenes, demonstrate that the proposed method eliminates a large percentage of the false alarms produced by the HOG pedestrian detector.
Izhar LI, Elamvazuthi I, Stathaki T, et al., 2016, Multimodal Image Registration for Potential Diagnosis and Monitoring of Morphoea using a Hybrid NGC Method, 1st International Conference on Biomedical Engineering (IBIOMED) - Empowering Biomedical Technology for Better Future, Publisher: IEEE, Pages: 47-50
Izhar LI, Stathaki T, Howell K, 2015, Global Based Thermal Image Registration for Diagnosis of Morphoea, International Conference for Innovation in Biomedical Engineering and Life Sciences (ICIBEL), Publisher: SPRINGER, Pages: 76-81, ISSN: 1680-0737
Analyzing and interpreting of thermograms have been increasingly employed in the diagnosis and monitoring of diseases thanks to its non-invasive, non-harmful nature and low cost. This paper presents a thermal image analysis system based on image registration for morphoea disease diagnosis. A novel system is proposed to improve the diagnosis and monitoring of morphoea based on integration with the published lines of Blaschko. In this application, image registration based on global and local registration methods are found inevitable. A modified normalized gradient cross-correlation (NGC) method to reduce large geometrical differences between two multimodal images of different subjects that are represented by smooth gray edge maps is proposed for the global registration approach. It is shown in this paper that the NGC method outperforms phase correlation (PC) method by a lower rate of misregistration. This demonstrates that by using the gradients of the gray edge maps, the performance of the PC based image registration method can be greatly improved.
Petrou ZI, Manakos I, Stathaki T, 2015, Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets, Biodiversity and Conservation, Vol: 24, Pages: 2333-2363, ISSN: 1572-9710
Recognizing the imperative need for biodiversity protection, the Convention onBiological Diversity (CBD) has recently established new targets towards 2020, the so-calledAichi targets, and updated proposed sets of indicators to quantitatively monitor the progresstowards these targets. Remote sensing has been increasingly contributing to timely, accurate,and cost-effective assessment of biodiversity-related characteristics and functionsduring the last years. However, most relevant studies constitute individual research efforts,rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore,systematic operational use of remote sensing data by managing authorities has stillbeen limited. In this study, the Aichi targets and the related CBD indicators whose monitoringcan be facilitated by remote sensing are identified. For each headline indicator anumber of recent remote sensing approaches able for the extraction of related propertiesare reviewed. Methods cover a wide range of fields, including: habitat extent and conditionmonitoring; species distribution; pressures from unsustainable management, pollution andclimate change; ecosystem service monitoring; and conservation status assessment of protectedareas. The advantages and limitations of different remote sensing data and algorithmsare discussed. Sorting of the methods based on their reported accuracies is attempted, whenpossible. The extensive literature survey aims at reviewing highly performing methods thatcan be used for large-area, effective, and timely biodiversity assessment, to encourage themore systematic use of remote sensing solutions in monitoring progress towards the Aichitargets, and to decrease the gaps between the remote sensing and management communities.
Stathaki P, Omar Z, Hamzah N, 2015, Adaptive Chebyshev fusion of vegetation imagery based on SVM classifier, International Conference on Enhancing Collaborative Networks for the Innovation Ecosystem
Lee S, Stathaki T, 2015, Two-dimensional autoregressive modelling using joint second and third order statistics and a weighting scheme, Pages: 2111-2114, ISSN: 2219-5491
The two-dimensional autoregressive modelling problem is attempted using a combination of the Yule-Walker system of equations and the Yule-Walker system of equations in the third-order statistical domain. A novel weighting scheme that relates the contribution of the two systems is proposed and some simulations results are provided to verify the improved estimations.
Petrou ZI, Manakos I, Stathaki T, et al., 2015, Discrimination of vegetation height categories with passive satellite sensor imagery using texture analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol: 8, Pages: 1442-1455, ISSN: 1939-1404
Vegetation height is a crucial factor in environmental studies, landscape analysis, and mapping applications. Its estimation may prove cost and resource demanding, e.g., employing light detection and ranging (LiDAR) data. This study presents a cost-effective framework for height estimation, built around texture analysis of a single very high-resolution passive satellite sensor image. A number of texture features are proposed, based on local variance, entropy, and binary patterns. Their potential in discriminating among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m) is tested in an area with heath, tree, and shrub vegetation. A number of missing data handling, outlier removal, and data normalization methods are evaluated to enhance the proposed framework. Its performance is tested with different classifiers, including single and ensemble tree ones and support vector machines. Furthermore, dimensionality reduction (DR) is applied to the full feature set (192 features), through both data transformation and filter feature selection methods. The proposed approach was tested in two WorldView-2 images, representing the peak and the decline of the vegetative period. Vegetation height categories were accurately distinguished, reaching accuracies of over 90% for six height classes, using the images either individually or in synergy. DR achieved similarly high, or higher, accuracies with even a 3% feature subset, increasing the processing efficiency of the framework, and favoring its use in height estimation applications not requiring particularly high spatial resolution data, as a cost-effective surrogate of more expensive and resource demanding approaches.
Petrou ZI, Kosmidou V, Manakos I, et al., 2014, A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic, PATTERN RECOGNITION LETTERS, Vol: 48, Pages: 24-33, ISSN: 0167-8655
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- Citations: 24
Elmikaty M, Stathaki T, 2014, Car Detection in High-Resolution Urban Scenes using Multiple Image Descriptors, 22nd International Conference on Pattern Recognition (ICPR), Publisher: IEEE COMPUTER SOC, Pages: 4299-4304, ISSN: 1051-4651
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- Citations: 15
Omar Z, Stathaki T, 2014, Image Fusion: An Overview, 5th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Publisher: IEEE, Pages: 306-310, ISSN: 2166-0662
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- Citations: 14
Petrou ZI, Manakos I, Stathaki T, et al., 2014, A vegetation height classification approach based on texture analysis of a single VHR image, 35th International Symposium on Remote Sensing of Environment (ISRSE35), Publisher: IOP PUBLISHING LTD, ISSN: 1755-1307
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- Citations: 5
Omar Z, Hamzah N, Stathaki T, 2014, Adaptive Chebyshev Polynomial Analysis for Fusion of Remote Sensing Vegetation Imagery, IEEE Region 10 Symposium, Publisher: IEEE, Pages: 546-550, ISSN: 2640-821X
Petrou ZI, Stathaki T, Manakos I, et al., 2014, LAND COVER TO HABITAT MAP CONVERSION USING REMOTE SENSING DATA: A SUPERVISED LEARNING APPROACH, IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing, Publisher: IEEE, ISSN: 2153-6996
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- Citations: 1
Alexiadis D, Mitianoudis N, Stathaki T, 2014, MULTIDIMENSIONAL STEERABLE FILTERS AND 3D FLOW ESTIMATION, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 2012-2016, ISSN: 1522-4880
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- Citations: 2
Elmikaty M, Stathaki T, Kimber P, et al., 2013, A novel two-level shape descriptor for pedestrian detection, SSPD 2012
The demand for pedestrian detection and tracking algorithms is rapidly increasing with applications in security systems, human computer interaction and human activity analysis. A pedestrian is a person standing in an upright position. Previous work involves using various types of image descriptors to detect humans. However, the existing approaches, although exhibit low misdetection rate, result in high rate of false alarms in the case of complex image backgrounds. In this work, a novel approach for pedestrian detection is proposed which is based on the combined use of two object detection approaches with the aim of reducing the false alarm rate of the individual detectors. These are the Histogram of Oriented Gradients (HOG) and a Shape Context based object detector (SC). Preliminary results are very encouraging and demonstrate clearly the ability of the proposed system to reduce the number of false alarms without significant increase in the processing time.
Gkalelis N, Mezaris V, Kompatsiaris I, et al., 2013, Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 24, Pages: 8-21, ISSN: 2162-237X
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- Citations: 30
Mitianoudis N, Antonopoulos S-A, Stathaki T, 2013, Region-based ICA Image Fusion using Textural Information, 18th International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Gkalelis N, Mezaris V, Dimopoulos M, et al., 2013, VIDEO EVENT DETECTION USING A SUBCLASS RECODING ERROR-CORRECTING OUTPUT CODES FRAMEWORK, IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Publisher: IEEE, ISSN: 1945-7871
Gkalelis N, Mezaris V, Kompatsiaris I, et al., 2013, VIDEO EVENT RECOUNTING USING MIXTURE SUBCLASS DISCRIMINANT ANALYSIS, 20th IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 4372-4376, ISSN: 1522-4880
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- Citations: 4
Gkalelis N, Mezaris V, Kompatsiaris I, et al., 2012, Linear Subclass Support Vector Machines, IEEE Signal Processing Letters, Vol: 19, Pages: 575-578, ISSN: 1070-9908
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