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Aggelos K Katsaggelos - One of the best experts on this subject based on the ideXlab platform.

  • shape error concealment based on a shape preserving Boundary approximation
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Evaggelia Tsiligianni, Lisimachos P Kondi, Aggelos K Katsaggelos
    Abstract:

    In object-based video representation, video scenes are composed of several arbitrarily shaped video objects (VOs), defined by their texture, shape and motion. In error-prone communications, packet loss results in missing information at the decoder. The impact of transmission errors is minimized through error concealment. In this paper, we propose a spatial error concealment technique for recovering lost shape data. We consider a geometric shape representation consisting of the object Boundary, which can be extracted from the α-plane. Missing macroblocks result in a broken Boundary. A B-spline curve is constructed to replace a missing Boundary Segment, based on a T-spline representation of the received Boundary. We use T-splines because they produce shape-preserving approximations and do not change the characteristics of the original Boundary. The representation ensures a good estimation of the first derivatives at the points touching the missing Segment. Applying smoothing conditions, we manage to construct a new spline that joins smoothly with the received Boundary, leading to successful concealment results. Experimental results on object shapes with different concealment difficulty demonstrate the performance of the proposed method. Comparisons with prior proposed methods are also presented.

  • shape error concealment based on a shape preserving Boundary approximation
    International Conference on Image Processing, 2010
    Co-Authors: Evaggelia Tsiligianni, Lisimachos P Kondi, Aggelos K Katsaggelos
    Abstract:

    In error-prone communications, packet loss results in missing information of shape, motion and texture of a video object (VO). Error concealment refers to the recovery of lost information at the decoder. In this paper, we propose a spatial shape error concealment technique. We consider a geometric representation of the shape of a VO consisting of its Boundary, which can be extracted from the received a-plane. Some Boundary parts are missing due to errors. We propose a method for modeling the received Boundary based on a shape-preserving approximation that uses T-splines. Such an approximation provides a good estimation of the direction of a missing Boundary Segment, which we use to construct a concealment spline that joins smoothly with the received Boundary parts.

Alfredo Petrosino - One of the best experts on this subject based on the ideXlab platform.

  • Distributed recursive learning for shape recognition through multiscale trees
    Image and Vision Computing, 2007
    Co-Authors: Luca Lombardi, Alfredo Petrosino
    Abstract:

    The paper reports an efficient and fully parallel 2D shape recognition method based on the use of a multiscale tree representation of the shape Boundary and recursive learning of trees. Specifically, the shape is represented by means of a tree where each node, corresponding to a Boundary Segment at some level of resolution, is characterized by a real vector containing curvature, length, symmetry of the Boundary Segment, while the nodes are connected by arcs when Segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by a Fuzzy recursive neural network followed by a testing procedure over unknown tree structured patterns. The proposed neural network model is able to facilitate the exchange of information between symbolic and sub-symbolic domains and deal with structured organization of information, that is typically required by symbolic processing.

  • WILF - Object recognition by recursive learning of multiscale trees
    Fuzzy Logic and Applications, 2006
    Co-Authors: Luca Lombardi, Alfredo Petrosino
    Abstract:

    In this paper we present an efficient and fully parallel 2D object recognition method based on the use of a multiscale tree representation of the object Boundary and recursive learning of trees. Specifically, the object is represented by means of a tree where each node, corresponding to a Boundary Segment at some level of resolution, is characterized by a real vector containing curvature, lenght, simmetry of the Boundary Segment, while the nodes are connected by arcs when Segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by Recursive Neural Networks followed by a testing procedure over unknown tree structured patterns.

  • Object Recognition by Recursive Learning of Multiscale Trees
    Lecture Notes in Computer Science, 2006
    Co-Authors: Luca Lombardi, Alfredo Petrosino
    Abstract:

    In this paper we present an efficient and fully parallel 2D object recognition method based on the use of a multiscale tree representation of the object Boundary and recursive learning of trees. Specifically, the object is represented by means of a tree where each node, corresponding to a Boundary Segment at some level of resolution, is characterized by a real vector containing curvature, lenght, simmetry of the Boundary Segment, while the nodes are connected by arcs when Segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by Recursive Neural Networks followed by a testing procedure over unknown tree structured patterns.

  • ICIAP - Shape recognition by distributed recursive learning of multiscale trees
    12th International Conference on Image Analysis and Processing 2003.Proceedings., 1
    Co-Authors: Luca Lombardi, Alfredo Petrosino
    Abstract:

    We present an efficient and fully parallel 2D object recognition method based on the use of a multiscale tree representation of the object Boundary and recursive learning of trees. Specifically, the object is represented by means of a tree where each node, corresponding to a Boundary Segment at some level of resolution, is characterized by a real vector containing curvature, length, and symmetry of the Boundary Segment, while the nodes are connected by arcs when Segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by recursive neural networks followed by a testing procedure over unknown tree structured patterns.

Yutaka S Sato - One of the best experts on this subject based on the ideXlab platform.

Evaggelia Tsiligianni - One of the best experts on this subject based on the ideXlab platform.

  • shape error concealment based on a shape preserving Boundary approximation
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Evaggelia Tsiligianni, Lisimachos P Kondi, Aggelos K Katsaggelos
    Abstract:

    In object-based video representation, video scenes are composed of several arbitrarily shaped video objects (VOs), defined by their texture, shape and motion. In error-prone communications, packet loss results in missing information at the decoder. The impact of transmission errors is minimized through error concealment. In this paper, we propose a spatial error concealment technique for recovering lost shape data. We consider a geometric shape representation consisting of the object Boundary, which can be extracted from the α-plane. Missing macroblocks result in a broken Boundary. A B-spline curve is constructed to replace a missing Boundary Segment, based on a T-spline representation of the received Boundary. We use T-splines because they produce shape-preserving approximations and do not change the characteristics of the original Boundary. The representation ensures a good estimation of the first derivatives at the points touching the missing Segment. Applying smoothing conditions, we manage to construct a new spline that joins smoothly with the received Boundary, leading to successful concealment results. Experimental results on object shapes with different concealment difficulty demonstrate the performance of the proposed method. Comparisons with prior proposed methods are also presented.

  • shape error concealment based on a shape preserving Boundary approximation
    International Conference on Image Processing, 2010
    Co-Authors: Evaggelia Tsiligianni, Lisimachos P Kondi, Aggelos K Katsaggelos
    Abstract:

    In error-prone communications, packet loss results in missing information of shape, motion and texture of a video object (VO). Error concealment refers to the recovery of lost information at the decoder. In this paper, we propose a spatial shape error concealment technique. We consider a geometric representation of the shape of a VO consisting of its Boundary, which can be extracted from the received a-plane. Some Boundary parts are missing due to errors. We propose a method for modeling the received Boundary based on a shape-preserving approximation that uses T-splines. Such an approximation provides a good estimation of the direction of a missing Boundary Segment, which we use to construct a concealment spline that joins smoothly with the received Boundary parts.

Lucile M. Jones - One of the best experts on this subject based on the ideXlab platform.

  • Seismicity, Stress State, and Style of Faulting of the Ridgecrest‐Coso Region from the 1930s to 2019: Seismotectonics of an Evolving Plate Boundary Segment
    Bulletin of the Seismological Society of America, 2020
    Co-Authors: Egill Hauksson, Lucile M. Jones
    Abstract:

    ABSTRACT Decadal scale variations in the seismicity rate in the Ridgecrest-Coso region, part of the Eastern California Shear Zone, included seismic quiescence from the 1930s to the early 1980s, followed by increased seismicity until the 2019 Mw 6.4 and 7.1 Ridgecrest sequence. This sequence exhibited complex rupture on almost orthogonal faults and triggered aftershocks over an area of ∼90  km long by ∼5–10  km wide, which is a fraction of the area of the previously seismically active Indian Wells Valley and Coso range region. During the last 40 yr, the seismicity has been predominantly the result of strike-slip motion, extending north from the Garlock fault, along the Little Lake and Airport Lake fault zones, and approaching the southernmost Owens Valley fault to the north. The Coso range forms an extensional stepover between these two strike-slip fault systems. This evolution of a plate Boundary zone is driven by the northwestward motion of the Sierra Nevada, and crustal extension along the southwestern edge of the Basin and Range Province. Stress inversion of focal mechanisms shows that the postseismic stress state consists of almost horizontal σ1 and vertical σ2. The σ1 is spatially rotated across the Coso range stepover with σ1-trending ∼N17° E to the north, whereas, along the Mw 7.1 mainshock rupture, the trend is ∼N6° E. The friction angles as measured between fault strikes and the σ1 trends correspond to a frictional coefficient of 0.75, suggesting average fault strength. In comparison, the mature Garlock fault has a smaller frictional coefficient of 0.28, similar to weak faults like the San Andreas fault. Thus, it appears that the heterogeneously oriented and spatially distributed but strong Ridgecrest-Coso faults accommodate seismicity at seemingly random places and times within the region and are in the process of self-organizing to form a major throughgoing plate-Boundary Segment.