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Affine Invariance

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Andrew Zisserman – One of the best experts on this subject based on the ideXlab platform.

  • an Affine invariant salient region detector
    European Conference on Computer Vision, 2004
    Co-Authors: Timor Kadir, Andrew Zisserman, Michael Brady
    Abstract:

    In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to Affine Invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

  • ECCV (1) – An Affine Invariant Salient Region Detector
    Lecture Notes in Computer Science, 2004
    Co-Authors: Timor Kadir, Andrew Zisserman, Michael Brady
    Abstract:

    In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to Affine Invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

  • A framework for spatiotemporal control in the tracking of visual contours
    International Journal of Computer Vision, 1993
    Co-Authors: Andrew Blake, Rupert Curwen, Andrew Zisserman
    Abstract:

    There has been a great deal of research interest in contour tracking over the last five years. This article combines themes from tracking theory—elastic models and stochastic filtering—with the notion of Affine Invariance to synthesize a substantially new and demonstrably effective framework for contour tracking. A mechanism is developed for incorporating a shape template into a contour tracker via an Affine invariant coupling. In that way the tracker becomes selective for shape and therefore able to ignore background clutter. Affine Invariance ensures that the effect of varying viewpoint is accommodated. Use of a standard statistical filtering framework allows uncertainties to be treated systematically, which accommodates object flexibility and un-modeled distortions such as the deformation of a silhouette under motion. The statistical framework also facilitates a further development. In place of heuristically determined spatial scale for feature search, both spatial scale and temporal memory are controlled automatically and in a way that is responsive to the tracking process. Typically, the tracker operates initially in a coarse scale/short memory mode while it searches for a feature. Then spatial scale diminishes to allow more precise localization while memory (temporal scale) lengths to take advantage of motion coherence. All system parameters are determined by natural assumptions and desired tracking performance, leaving none to be fixed heuristically. Versions of the tracker have been implemented at video rate, both on SUN 4 and in parallel, using a network of 11 transputers. The theoretically established properties of automatic control of spatiotemporal scale and of Affine Invariance are demonstrated using the implemented tracker.

Michael Brady – One of the best experts on this subject based on the ideXlab platform.

  • an Affine invariant salient region detector
    European Conference on Computer Vision, 2004
    Co-Authors: Timor Kadir, Andrew Zisserman, Michael Brady
    Abstract:

    In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to Affine Invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

  • ECCV (1) – An Affine Invariant Salient Region Detector
    Lecture Notes in Computer Science, 2004
    Co-Authors: Timor Kadir, Andrew Zisserman, Michael Brady
    Abstract:

    In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to Affine Invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

Zhiqian Wang – One of the best experts on this subject based on the ideXlab platform.

  • Pictorial recognition of objects employing Affine Invariance in the frequency domain
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
    Co-Authors: Jezekiel Ben-arie, Zhiqian Wang
    Abstract:

    Describes an efficient approach to pose invariant pictorial object recognition employing spectral signatures of image patches that correspond to object surfaces which are roughly planar. Based on singular value decomposition (SVD), the Affine transform is decomposed into slant, tilt, swing, scale, and 2D translation. Unlike previous log-polar representations which were not invariant to slant, our log-log sampling configuration in the frequency domain yields complete Affine Invariance. The images are preprocessed by a novel model-based segmentation scheme that detects and segments objects that are Affine-similar to members of a model set of basic geometric shapes. The segmented objects are then recognized by their signatures using multidimensional indexing in a pictorial dataset represented in the frequency domain. Experimental results with a dataset of 26 models show 100 percent recognition rates in a wide range of 3D pose parameters and imaging degradations: 0-360/spl deg/ swing and tilt, 0-82/spl deg/ of slant, more than three octaves in scale change, window-limited translation, high noise levels (0 dB), and significantly reduced resolution (1:5).

  • ICPR – Iconic recognition with Affine-invariant spectral signatures
    Proceedings of 13th International Conference on Pattern Recognition, 1996
    Co-Authors: Jezekiel Ben-arie, Zhiqian Wang, Raghunath K. Rao
    Abstract:

    This paper presents a new approach for object recognition using Affine-invariant recognition of image patches that correspond to object surfaces that are roughly planar. A novel set of Affine-invariant spectral signatures (AISSs) are used to recognize each surface separately invariant to its 3D pose. These local spectral signatures are extracted by correlating the image with a novel configuration of Gaussian kernels. The spectral signature of each image patch is then matched against a set of iconic models using multidimensional indexing (MDI) in the frequency domain. AffineInvariance of the signatures is achieved by a new configuration of Gaussian kernels with modulation in two orthogonal axes. The proposed configuration of kernels is Cartesian with varying aspect ratios in two orthogonal directions. The kernels are organized in subsets where each subset has a distinct orientation. Each subset spans the entire frequency domain and provides Invariance to slant, scale and limited translation. The complete set of orientations is utilized to achieve Invariance to rotation and tilt. Hence, the proposed set of kernels achieve complete AffineInvariance.

  • ICIP (3) – SVD and log-log frequency sampling with Gabor kernels for invariant pictorial recognition
    Proceedings of International Conference on Image Processing, 1
    Co-Authors: Zhiqian Wang, Jezekiel Ben-arie
    Abstract:

    This paper presents an efficient scheme for Affine-invariant object recognition. Affine Invariance is obtained by a representation which is based on a new sampling configuration in the frequency domain. We discuss the decomposition of Affine transform into slant, tilt, swing, scale and 2D translation by applying singular value decomposition (SVD). The Affine invariant spectral signatures (AISS) are derived from a set of Cartesian logarithmic-logarithmic (log-log) sampling configuration in the frequency domain. The AISS enables the recognition of image patches that correspond to roughly planar object surfaces-regardless of their poses in space. Unlike previous log-polar representations which are not invariant to slant (i.e. foreshortening only in one direction), the AISS yields a complete Affine Invariance. The proposed log-log configuration can be employed either by a global Fourier transform or by a local Gabor transform. Local representation enables one to recognize separately several objects in the same image. The actual signature recognition is performed by multi-dimensional indexing in a pictorial dataset. 3D pose information is also derived as a by-product.

Timor Kadir – One of the best experts on this subject based on the ideXlab platform.

  • an Affine invariant salient region detector
    European Conference on Computer Vision, 2004
    Co-Authors: Timor Kadir, Andrew Zisserman, Michael Brady
    Abstract:

    In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to Affine Invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

  • ECCV (1) – An Affine Invariant Salient Region Detector
    Lecture Notes in Computer Science, 2004
    Co-Authors: Timor Kadir, Andrew Zisserman, Michael Brady
    Abstract:

    In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to Affine Invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

Jezekiel Ben-arie – One of the best experts on this subject based on the ideXlab platform.

  • Pictorial recognition of objects employing Affine Invariance in the frequency domain
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
    Co-Authors: Jezekiel Ben-arie, Zhiqian Wang
    Abstract:

    Describes an efficient approach to pose invariant pictorial object recognition employing spectral signatures of image patches that correspond to object surfaces which are roughly planar. Based on singular value decomposition (SVD), the Affine transform is decomposed into slant, tilt, swing, scale, and 2D translation. Unlike previous log-polar representations which were not invariant to slant, our log-log sampling configuration in the frequency domain yields complete Affine Invariance. The images are preprocessed by a novel model-based segmentation scheme that detects and segments objects that are Affine-similar to members of a model set of basic geometric shapes. The segmented objects are then recognized by their signatures using multidimensional indexing in a pictorial dataset represented in the frequency domain. Experimental results with a dataset of 26 models show 100 percent recognition rates in a wide range of 3D pose parameters and imaging degradations: 0-360/spl deg/ swing and tilt, 0-82/spl deg/ of slant, more than three octaves in scale change, window-limited translation, high noise levels (0 dB), and significantly reduced resolution (1:5).

  • ICPR – Iconic recognition with Affine-invariant spectral signatures
    Proceedings of 13th International Conference on Pattern Recognition, 1996
    Co-Authors: Jezekiel Ben-arie, Zhiqian Wang, Raghunath K. Rao
    Abstract:

    This paper presents a new approach for object recognition using Affine-invariant recognition of image patches that correspond to object surfaces that are roughly planar. A novel set of Affine-invariant spectral signatures (AISSs) are used to recognize each surface separately invariant to its 3D pose. These local spectral signatures are extracted by correlating the image with a novel configuration of Gaussian kernels. The spectral signature of each image patch is then matched against a set of iconic models using multidimensional indexing (MDI) in the frequency domain. AffineInvariance of the signatures is achieved by a new configuration of Gaussian kernels with modulation in two orthogonal axes. The proposed configuration of kernels is Cartesian with varying aspect ratios in two orthogonal directions. The kernels are organized in subsets where each subset has a distinct orientation. Each subset spans the entire frequency domain and provides Invariance to slant, scale and limited translation. The complete set of orientations is utilized to achieve Invariance to rotation and tilt. Hence, the proposed set of kernels achieve complete AffineInvariance.

  • ICIP (3) – SVD and log-log frequency sampling with Gabor kernels for invariant pictorial recognition
    Proceedings of International Conference on Image Processing, 1
    Co-Authors: Zhiqian Wang, Jezekiel Ben-arie
    Abstract:

    This paper presents an efficient scheme for Affine-invariant object recognition. Affine Invariance is obtained by a representation which is based on a new sampling configuration in the frequency domain. We discuss the decomposition of Affine transform into slant, tilt, swing, scale and 2D translation by applying singular value decomposition (SVD). The Affine invariant spectral signatures (AISS) are derived from a set of Cartesian logarithmic-logarithmic (log-log) sampling configuration in the frequency domain. The AISS enables the recognition of image patches that correspond to roughly planar object surfaces-regardless of their poses in space. Unlike previous log-polar representations which are not invariant to slant (i.e. foreshortening only in one direction), the AISS yields a complete Affine Invariance. The proposed log-log configuration can be employed either by a global Fourier transform or by a local Gabor transform. Local representation enables one to recognize separately several objects in the same image. The actual signature recognition is performed by multi-dimensional indexing in a pictorial dataset. 3D pose information is also derived as a by-product.