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  • 1.
    Julià, Carme
    et al.
    Rovira i Virgili University, Spain.
    Moreno, Rodrigo
    Rovira i Virgili University, Spain.
    Puig, Domenec
    Rovira i Virgili University, Spain.
    Garcia, Miguel Angel
    Autonomous University of Madrid, Spain.
    Shape-based image segmentation through photometric stereo2011In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 115, no 1, p. 91-104Article in journal (Refereed)
    Abstract [en]

    This paper describes a new algorithm for segmenting 2D images by taking into account 3D shape information. The proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering. One of the advantages of the proposed approach is that, although the segmentation is based on the 3D shape of the objects, the photometric stereo stage used to estimate the 3D normals only requires a set of 2D images. This paper provides an extensive validation of the proposed approach by comparing it with several image segmentation algorithms. Particularly, it is compared with both appearance-based image segmentation algorithms and shape-based ones. Experimental results confirm that the latter are more suitable when the objective is to segment the objects or surfaces present in the scene. Moreover, results show that the proposed approach yields the best image segmentation in most of the cases.

  • 2.
    Moreno, Rodrigo
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Garcia, Miguel Angel
    Department of Informatics Engineering, Autonomous University of Madrid, Madrid, Spain.
    Puig, Domenec
    Intelligent Robotics and Computer Vision Group at the Department of Computer Science and Mathematics, Rovira i Virgili University, Tarragona, Spain.
    Julià, Carme
    Intelligent Robotics and Computer Vision Group at the Department of Computer Science and Mathematics, Rovira i Virgili University, Tarragona, Spain.
    Edge-Preserving Color Image Denoising Through Tensor Voting2011In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 115, no 11, p. 1536-1551Article in journal (Refereed)
    Abstract [en]

    This paper presents a new method for edge-preserving color image denoising based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information through tensors in order to propagate them in a neighborhood by using a specific voting process. This voting process is specifically designed for edge-preserving color image denoising by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Measurements of removed noise, edge preservation and undesirable introduced artifacts, additionally to visual inspection, show that the proposed method has a better performance than the state-of-the-art image denoising algorithms for images contaminated with CCD camera noise.

  • 3.
    Nordberg, Klas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    The triangulation tensor2009In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 113, no 9, p. 935-945Article in journal (Refereed)
    Abstract [en]

    This article presents a computationally efficient approach to the triangulation of 3D points from their projections in two views. The homogenous coordinates of a 3D point is given as a multi-linear mapping on its homogeneous image coordinates, a computation of low computational complexity. The multi-linear mapping is a tensor, and an element of a projective space, that can be computed directly from the camera matrices and some parameters. These parameters imply that the tensor is not unique: for a given camera pair the subspace K of triangulation tensors is six-dimensional. The triangulation tensor is 3D projective covariant and satisfies a set of internal constraints. Reconstruction of 3D points using the proposed tensor is studied for the non-ideal case, when the image coordinates are perturbed by noise and the epipolar constraint exactly is not satisfied exactly. A particular tensor of K is then the optimal choice for a simple reduction of 3D errors, and we present a computationally efficient approach for determining this tensor. This approach implies that normalizing image coordinate transformations are important for obtaining small 3D errors.

    In addition to computing the tensor from the cameras, we also investigate how it can be further optimized relative to error measures in the 3D and 2D spaces. This optimization is evaluated for sets of real 3D + 2D + 2D data by comparing the reconstruction to some of the triangulation methods found in the literature, in particular the so-called optimal method that minimizes 2D L2 errors. The general conclusion is that, depending on the choice of error measure and the optimization implementation, it is possible to find a tensor that produces smaller 3D errors (both L1 and L2) but slightly larger 2D errors than the optimal method does. Alternatively, we may find a tensor that gives approximately comparable results to the optimal method in terms of both 3D and 2D errors. This means that the proposed tensor based method of triangulation is both computationally efficient and can be calibrated to produce small reconstruction or reprojection errors for a given data set.

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