T-Invariants

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The Experts below are selected from a list of 1953 Experts worldwide ranked by ideXlab platform

Tomas Suk - One of the best experts on this subject based on the ideXlab platform.

Jie Chen - One of the best experts on this subject based on the ideXlab platform.

  • Affine curve moment invariants for shape recognition
    Pattern Recognition, 1997
    Co-Authors: Dongming Zhao, Jie Chen
    Abstract:

    This paper presents a method of affine curve moment invariants for shape recognition. The proposed method extends affine moment invariants from an area domain to a curve domain. First, a new type of curve moments is defined on a parameterized boundary description of an object. A set of affine moment invariants are then derived based on the theory of algebraic invariants. The proposed moment invariants only deal with pixels on a shape boundary and are mathematically proven to be invariant under arbitrary affine transformations. Furthermore, after a parameterized object boundary description is acquired through curve fitting, the newly defined curve moments can be represented by an integration of the fitting function, so that the computation of moment invariants can be obtained from either one-by-one computation of boundary pixels or a direct integration of the fitting function, which makes the computation efficient while at the same time reducing noise effect. Affine curve moment invariants work well in recognition of affine-deformed objects, which is demonstrated by processing various 2-D shapes under affine transformations. © 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.

Jan Flusser - One of the best experts on this subject based on the ideXlab platform.

J. van de Weijer - One of the best experts on this subject based on the ideXlab platform.

  • edge and corner detection by photometric quasi invariants
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
    Co-Authors: J. van de Weijer, Th. Gevers, Jan-mark Geusebroek
    Abstract:

    Feature detection is used in many computer vision applications such as image segmentation, object recognition, and image retrieval. For these applications, robustness with respect to shadows, shading, and specularities is desired. Features based on derivatives of photometric invariants, which we is called full invariants, provide the desired robustness. However, because computation of photometric invariants involves nonlinear transformations, these features are unstable and, therefore, impractical for many applications. We propose a new class of derivatives which we refer to as quasi-invariants. These quasi-invariants are derivatives which share with full photometric invariants the property that they are insensitive for certain photometric edges, such as shadows or specular edges, but without the inherent instabilities of full photometric invariants. Experiments show that the quasi-invariant derivatives are less sensitive to noise and introduce less edge displacement than full invariant derivatives. Moreover, quasi-invariants significantly outperform the full invariant derivatives in terms of discriminative power.

  • Color edge detection by photometric quasi-invariants
    Proceedings Ninth IEEE International Conference on Computer Vision, 2003
    Co-Authors: J. van de Weijer, Th. Gevers, Jm. Geusebroek
    Abstract:

    Photometric invariance is used in many computer vision applications. The advantage of photometric invariance is the robustness against shadows, shading and illumination conditions. However, the drawbacks of photometric invariance are the loss of discriminative power and the inherent instabilities caused by the nonlinear transformations to compute the invariants. In this paper, we propose a new class of derivatives which we refer to as photometric quasi-invariants. These quasi-invariants share with full invariants the nice property that they are robust against photometric edges, such as shadows or specular edges. Further, these quasi-invariants do not have the inherent instabilities of full photometric invariants. We will apply these quasi-invariant derivatives in the context of photometric invariant edge detection and classification. Experiments show that the quasi-invariant derivatives are stable and they significantly outperform the full invariant derivatives in discriminative power.

Jan-mark Geusebroek - One of the best experts on this subject based on the ideXlab platform.

  • edge and corner detection by photometric quasi invariants
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
    Co-Authors: J. van de Weijer, Th. Gevers, Jan-mark Geusebroek
    Abstract:

    Feature detection is used in many computer vision applications such as image segmentation, object recognition, and image retrieval. For these applications, robustness with respect to shadows, shading, and specularities is desired. Features based on derivatives of photometric invariants, which we is called full invariants, provide the desired robustness. However, because computation of photometric invariants involves nonlinear transformations, these features are unstable and, therefore, impractical for many applications. We propose a new class of derivatives which we refer to as quasi-invariants. These quasi-invariants are derivatives which share with full photometric invariants the property that they are insensitive for certain photometric edges, such as shadows or specular edges, but without the inherent instabilities of full photometric invariants. Experiments show that the quasi-invariant derivatives are less sensitive to noise and introduce less edge displacement than full invariant derivatives. Moreover, quasi-invariants significantly outperform the full invariant derivatives in terms of discriminative power.

  • Color invariance
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
    Co-Authors: Jan-mark Geusebroek, Rein Van Den Boomgaard, Arnold W. M. Smeulders, Hugo Geerts
    Abstract:

    This paper presents the measurement of colored object reflectance,\nunder different, general assumptions regarding the imaging conditions.\nWe exploit the Gaussian scale-space paradigm for color images to define\na framework for the robust measurement of object reflectance from color\nimages. Object reflectance is derived from a physical reflectance model\nbased on the Kubelka-Munk theory for colorant layers. Illumination and\ngeometrical invariant properties are derived from the reflectance model.\nInvariance and discriminative power of the color invariants is\nexperimentally investigated, showing the invariants to be successful in\ndiscounting shadow, illumination, highlights, and noise. Extensive\nexperiments show the different invariants to be highly discriminative,\nwhile maintaining invariance properties. The presented framework for\ncolor measurement is well-founded in the physics of color as well as in\nmeasurement science. Hence, the proposed invariants are considered more\nadequate for the measurement of invariant color features than existing\nmethods