The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform
Steven Haker - One of the best experts on this subject based on the ideXlab platform.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of É. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of E. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
Eugenio Calabi - One of the best experts on this subject based on the ideXlab platform.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of É. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of E. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
Jürgen Bereiter-hahn - One of the best experts on this subject based on the ideXlab platform.
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Visualization of Localized Elastic Properties in Human Tooth and Jawbone as Revealed by Scanning Acoustic Microscopy
Ultrasound in Medicine and Biology, 2013Co-Authors: Amit Shelke, Maximilian Blume, Michael Mularczyk, Constantin A. Landes, Robert Sader, Jürgen Bereiter-hahnAbstract:The elastic properties of human canine and supporting alveolar bone are measured by the distribution of localized speed of sound using scanning acoustic microscopy. Methods for the dynamic, non-destructive diagnostics of dental hard tissues can have a key role in the early detection of demineralization processes and carious lesions, and they are supposed to open the possibility of early dental restorations. The localized distribution of the ultrasound velocity in canine tooth and alveolar bone was obtained using scanning acoustic microscopy with a 5- and 30-MHz transducer. An acoustic material Signature Curve signifies the interference of the waves and quantitatively maps the localized speed of sound in alveolar bone and the canine tooth. Seven samples, consisting of alveolar jawbone and tooth sliced along the coronally apical axis, were investigated. The average speed of sound was determined along three independent cross sections at enamel, dentin and cortical bone. The average speed of sound in enamel, bone and dentin was SD 3460 ± 193 m/s, 3232 ± 113 m/s and 2928 ± 106 m/s. The distribution of sound wave propagation reveals a decrease in sound speed from the peripheral parts within the enamel and dentin layers toward the proximal zones. These results prove the possibility of linking the elastic properties to different areas within the osseous and dental hard tissues and visualize them in an extremely high local resolution. The results serve as a basis for further study and substantiate the enormous potential of ultrasound based analysis in the field of dento-alveolar diagnosis.
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Mechanical property quantification of endothelial cells using scanning acoustic microscopy
Health Monitoring of Structural and Biological Systems 2012, 2012Co-Authors: Amit Shelke, Jürgen Bereiter-hahn, S. Brand, Tribikram Kundu, Christopher BlaseAbstract:The mechanical properties of cells reflect dynamic changes of cellular organization which occur during physiologic activities like cell movement, cell volume regulation or cell division. Thus the study of cell mechanical properties can yield important information for understanding these physiologic activities. Endothelial cells form the thin inner lining of blood vessels in the cardiovascular system and are thus exposed to shear stress as well as tensile stress caused by the pulsatile blood flow. Endothelial dysfunction might occur due to reduced resistance to mechanical stress and is an initial step in the development of cardiovascular disease like, e.g., atherosclerosis. Therefore we investigated the mechanical properties of primary human endothelial cells (HUVEC) of different age using scanning acoustic microscopy at 1.2 GHz. The HUVECs are classified as young (t D 90 h) cells depending upon the generation time for the population doubling of the culture (t D). Longitudinal sound velocity and geometrical properties of cells (thickness) were determined using the material Signature Curve V(z) method for variable culture condition along spatial coordinates. The plane wave technique with normal incidence is assumed to solve two-dimensional wave equation. The size of the cells is modeled using multilayered (solid-fluid) system. The propagation of transversal wave and surface acoustic wave are neglected in soft matter analysis. The biomechanical properties of HUVEC cells are quantified in an age dependent manner.
Peter J. Olver - One of the best experts on this subject based on the ideXlab platform.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of É. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of E. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
Allen Tannenbaum - One of the best experts on this subject based on the ideXlab platform.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of É. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
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Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision, 1998Co-Authors: Eugenio Calabi, Peter J. Olver, Chehrzad Shakiban, Allen Tannenbaum, Steven HakerAbstract:We introduce a new paradigm, the differential invariant Signature Curve or manifold, for the invariant recognition of visual objects. A general theorem of E. Cartan implies that two Curves are related by a group transformation if and only if their Signature Curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant Signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.
