Stelios Krinidis

Technological Educational Institute of Kavala, Neapolis, East Macedonia and Thrace, Greece

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Publications (31)29.76 Total impact

  • Stelios Krinidis, Michail Krinidis
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    ABSTRACT: This paper presents a novel skeleton pruning approach based on a 2D empirical mode like decomposition (EMD-like). The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the object contour is decomposed by empirical mode like decomposition (EMD-like), the IMFs of the object provide a workspace with very good properties for obtaining the object's skeleton. The theoretical properties and the performed experiments demonstrate that the obtained skeletons match to hand-labeled skeletons provided by human subjects. Even in the presence of significant noise and shape variations, cuts and tears, the resulted skeletons have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches as many existing skeleton pruning methods and moreover, does not displace the skeleton points, which are all centers of maximal disks.
    Image and Vision Computing 08/2013; 31(8):533-541. · 1.96 Impact Factor
  • Stelios Krinidis, Michail Krinidis, Vassilios Chatzis
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    ABSTRACT: This paper presents a new approach for the detection of image objects, based on active contours by exploiting techniques of curve evolution. The proposed model of active contours is based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem. Unlike to existing methods, the adopted fuzzy energy does not depend on the gradient of the image, but is related to the image color and spatial segments. The proposed fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. The fuzziness of the energy and the exploitation of both local gray-scale and spatial information provide a balanced technique with a strong ability to reject "weak", as well as, "strong" local minima. The proposed approach is faster comparing to existing active contour models, due to the fact that the fuzzy energy calculations are computed directly instead of solving the Euler-Lagrange equations. The theoretical properties and the obtained results show that the proposed fuzzy energy-based active contour provides good object detection robustness when compared to other state of the art snake methods based on the gradient or other kind of energies.
    Engineering Intelligent Systems. 06/2013; 21(2-3):133-146.
  • S. Krinidis, M. Krinidis, V. Chatzis
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    ABSTRACT: This study presents a new approach for image clustering, which is based on a novel workspace derived from the empirical mode decomposition (EMD). The proposed algorithm exploits the EMD, which can decompose any non-linear and non-stationary data into a number of intrinsic mode functions (IMFs). The intermediate IMFs of the image histogram have very good characteristics and provide a robust workspace that is utilised in order to detect the clusters of an image in a fast way. The proposed method was applied to several images and the obtained results show good image clustering robustness and low computational time, overcoming the disadvantages of the existing image clustering algorithms.
    IET Image Processing 01/2012; 6(6):778-785. · 0.90 Impact Factor
  • S. Krinidis, M. Krinidis
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    ABSTRACT: Fuzzy C-means is an efficient algorithm for data clustering. The fuzzy local information C-means (FLICM) algorithm was introduced by Krinidis and Chatzis for image clustering and it was proved that it has very good properties. Presented is a generalisation of the fuzzy local information C-means clustering algorithm, in order to be applicable to any kind of input data sets instead of images. The generalisation of FLICM maintains the properties of the original algorithm, and it is also effective and efficient, providing robustness against noise. Furthermore, it is fully free of any kind of empirically adjusted parameters, contrary to all other fuzzy C-means algorithms that have been proposed in the literature.
    Electronics Letters 01/2012; 48(23):1468-1470. · 1.04 Impact Factor
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    Stelios Krinidis
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    ABSTRACT: The performance of a number of image processing methods depends on the output quality of a distance transformation (DT) process. Most of the fast DT methodologies are not accurate, whereas other error-free DT algorithms are not very fast. In this paper, a novel, fast, simple, and error-free DT algorithm is presented. By recording the relative x- and y-coordinates of the examined image pixels, an optimal algorithm can be developed to achieve the DT of an image correctly and efficiently in constant time without any iteration. Furthermore, the proposed method is general since it can be used by any kind of distance function, leading to accurate image DTs.
    IEEE Transactions on Image Processing 11/2011; 21(4):2178-86. · 3.20 Impact Factor
  • M. Krinidis, S. Krinidis, V. Chatzis
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    ABSTRACT: A novel image clustering method based on the image histogram, which is processed by the empirical mode decomposition (EMD) is presented. An intermediate step derived from the EMD, which can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs) is exploited. The IMFs of the image histogram have interesting characteristics and provide a novel workspace that is utilized in order to automatically detect the different clusters into the image under examination. The proposed method was applied to several real and synthetic images and the obtained results show good image clustering robustness.
    Digital Signal Processing (DSP), 2011 17th International Conference on; 01/2011
  • Source
    Stelios Krinidis, Vassilios Chatzis
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    ABSTRACT: This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.
    IEEE Transactions on Image Processing 05/2010; 19(5):1328-37. · 3.20 Impact Factor
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    Stelios Krinidis, Ioannis Pitas
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    ABSTRACT: This paper presents a method for generalizing human facial expressions by means of a statistical analysis of human facial expressions coming from various per-sons. The data used for the statistical analysis are obtained by tracking a generic facial wireframe model in video sequences depicting the formation of the different human fa-cial expressions, starting from a neutral state. Wireframe node tracking is performed by a pyramidal variant of the well-known Kanade-Lucas-Tomasi (KLT) tracker. The loss of tracked features is handled through a model deformation procedure that increases the robustness of the tracking algorithm. Tracking initialization is performed in a semi-automatic fashion, i.e., the facial wireframe model is fitted to an image representing a neutral facial expression, exploiting physics-based deformable shape modeling. The dy-namic facial expression output model is MPEG-4 compliant. The method has been tested on a variety of sequences with very good results, including a database of video sequences representing human faces changing from the neutral state to the one that represents a fully formed human facial expression.
    01/2010;
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    S. Krinidis, V. Chatzis
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    ABSTRACT: This paper presents a novel fast model for active contours to detect objects in an image, based on techniques of curve evolution. The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem. This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. However, the stopping term does not depend on the gradient of the image, as most of the classical active contours, but instead is related to the image color and spatial segments. The fuzziness of the energy provides a balanced technique with a strong ability to reject ldquoweakrdquo local minima. Moreover, this approach converges to the desired object boundary very fast, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly. The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.
    IEEE Transactions on Image Processing 01/2010; · 3.20 Impact Factor
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    Stelios Krinidis, Vassilios Chatzis
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    ABSTRACT: This paper presents a novel approach for object skeleton family extraction. The introduced technique utilizes a 2-D physics-based deformable model that parameterizes the objects shape. Deformation equations are solved exploiting modal analysis, and proportional to model physical characteristics, a different skeleton is produced every time, generating, in this way, a family of skeletons. The theoretical properties and the experiments presented demonstrate that obtained skeletons match to hand-labeled skeletons provided by human subjects, even in the presence of significant noise and shape variations, cuts and tears, and have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches without the need of any known skeleton pruning method.
    IEEE Transactions on Image Processing 02/2009; 18(1):1-11. · 3.20 Impact Factor
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    Stelios Krinidis, Vassilios Chatzis
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    ABSTRACT: This paper addresses the issue of accurate, effective, computationally efficient, fast, and fully automated 2-D object orientation and scaling factor estimation. The object orientation is calculated using object principal axes estimation. The approach relies on the object's frequency-based features. The frequency-based features used by the proposed technique are extracted by a 2-D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations.
    IEEE Transactions on Image Processing 07/2008; 17(6):1007-19. · 3.20 Impact Factor
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    S. Krinidis, V. Chatzis
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    ABSTRACT: A major disadvantage of image retrieval systems is their lack of objects orientation and scaling matching. This paper addresses the issue of accurate, effective, computationally efficient, fast and fully-automated 2D object orientation and scaling determination. The approach relies on the objects frequency-based features. The frequency-based features used by the proposed technique, are extracted by a 2D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations
    Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on; 06/2006
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    Stelios Krinidis, Ioannis Pitas
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    ABSTRACT: This paper presents a method for generalizing human facial ex-pressions or personalizing (cloning) them from one person to com-pletely different persons, by means of a statistical analysis of hu-man facial expressions coming from various persons. The data used for the statistical analysis are obtained by tracking a generic facial wireframe model in video sequences depicting the formation of the different human facial expressions, starting from a neutral state. Wireframe node tracking is performed by a pyramidal variant of the well-known Kanade-Lucas-Tomasi (KLT) tracker. The loss of tracked features is handled through a model deformation procedure increasing the robustness of the tracking algorithm. The dynamic facial expression output model is MPEG-4 compliant. The method has been tested on a variety of sequences with very good results, including a database of video sequences representing human faces changing from the neutral state to the one that represents a fully formed human facial expression.
    01/2006;
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    Stelios Krinidis, Ioannis Pitas
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    ABSTRACT: This paper presents an accurate, very fast approach for the deformations of two-dimensional physically based shape models representing open and closed curves. The introduced models are much faster than other deformable models (e.g., finite-element methods). The approach relies on the determination of explicit deformation governing equations that involve neither eigenvalue decomposition, nor any other computationally intensive numerical operation. The approach was evaluated and compared with another fast and accurate physics-based deformable shape odel, both in terms of deformation accuracy and computation time. The conclusion is that the introduced model is completely accurate and is deformed very fast on current personal computers (Pentium III), achieving more than 380 contour deformations per second.
    IEEE Transactions on Image Processing 04/2005; 14(3):281-93. · 3.20 Impact Factor
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    Stelios Krinidis, Chistophoros Nikou, Ioannis Pitas
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    ABSTRACT: This paper presents an accurate, computationally efficient, fast, and fully automated algorithm for the alignment of two-dimensional (2-D) serially acquired sections forming a 3-D volume. The approach relies on the determination of interslice correspondences. The features used for correspondence are extracted by a 2-D physics-based deformable model parameterizing the object shape. Correspondence affinities and global constrains render the method efficient and reliable. The method accounts for one of the major shortcomings of 2-D slices alignment of a 3-D volume, namely variable and nonuniform thickness of the slices. Moreover, no particular alignment direction is privileged, avoiding global offsets, biases, and error propagation. The method was evaluated on real images and the experimental results demonstrated its accuracy, as reconstruction errors were smaller than 1 degree in rotation and smaller than 1 pixel in translation.
    IEEE Transactions on Information Technology in Biomedicine 01/2004; 7(4):394-403. · 1.98 Impact Factor
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    Stelios Krinidis, Ioannis Pitas
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    ABSTRACT: This paper presents an accurate, very fast approach for the deformations of 2D physically based shape models representing open and closed curves. The introduced models overcome the main shortcoming of other deformable models, i.e. computation time. The approach relies on the determination of explicit deformation governing equations that involve neither eigenvalue decomposition nor any other computationally intensive numerical operation. The approach was evaluated and compared with another fast and accurate physics-based deformable shape model, both in terms of deformation accuracy and computation time. The conclusion is that the introduced model is completely accurate and is deformed very fast on current personal computers.
    Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on; 01/2004
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    Stelios Krinidis, Christophoros Nikou, Ioannis Pitas
    [Show abstract] [Hide abstract]
    ABSTRACT: An accurate, computationally efficient, and fully automated algorithm for the alignment of two-dimensional (2-D) serially acquired sections forming a three-dimensional (3-D) volume is presented. The approach relies on the optimization of a global energy function, based on the object shape, measuring the similarity between a slice and its neighborhood in the 3-D volume. Slice similarity is computed using the distance transform measure in both directions. No particular direction is privileged in the method avoiding global offsets, biases in the estimation and error propagation. The method was evaluated on real images [medical, biological, and other computerized tomography (CT) scanned 3-D data] and the experimental results demonstrated its accuracy as reconstuction errors are less than one degree in rotation and less than one pixel in translation.
    IEEE Transactions on Information Technology in Biomedicine 07/2003; 7(2):108-13. · 1.98 Impact Factor
  • Stelios Krinidis, Christophoros Nikou, Ioannis Pitas
    [Show abstract] [Hide abstract]
    ABSTRACT: An accurate, computationally efficient and fully-automated algorithmfor the alignment of 2D serially acquired sections forming a 3D volume ispresented. The method accounts for the main shortcomings of 3D image alignment:corrupted data (cuts and tears), dissimilarities or discontinuities betweenslices, non parallel or missing slices. The approach relies on the optimizationof a global energy function, based on the object shape, measuring the similaritybetween a slice and its...
    IEEE Transactions on Information Technology in Biomedicine 01/2003; 7:108-113. · 1.98 Impact Factor
  • Source
    Stelios Krinidis, Christophoros Nikou, Ioannis Pitas
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents an accurate, computationally efficient, fast and fully-automated algorithm for the alignment of 2D serially ac- quired sections forming a 3D volume. The method accounts for the main shortcomings of 3D image alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices and missing slices. The approach relies on the determination of inter-slice correspondences. The features used for correspondence, are extracted by a 2D physics-based deformable model parame- terizing the object shape. Correspondence affinities and global constrains render the method efficient and reliable. The method was evaluated on real images and the experimental results demon- strated its accuracy, as reconstruction errors were smaller than 1 degree in rotation and smaller than 1 pixel in translation.
    11/2002;
  • Source
    S. Krinidis, C. Nikou, I. Pitas
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents an accurate, computationally efficient, fast and fully-automated algorithm for the alignment of 2D serially acquired sections forming a 3D volume. The method accounts for the main shortcomings of 3D image alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices and missing slices. The approach relies on the determination of inter-slice correspondences. The features used for correspondence are extracted by a 2D physics-based deformable model parameterizing the object shape. Correspondence affinities and global constraints render the method efficient and reliable. The method has been evaluated on real images and the experimental results demonstrate its accuracy, as reconstruction errors are smaller than 1 degree in rotation and smaller than 1 pixel in translation.
    Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on; 02/2002