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Generalized Arf invariants in algebraic Ltheory
"... Abstract. The difference between the quadratic Lgroups L∗(A) and the symmetric Lgroups L ∗ (A) of a ring with involution A is detected by generalized Arf invariants. The special case A = Z[x] gives a complete set of invariants for the Cappell UNilgroups UNil∗(Z; Z, Z) for the infinite dihedral gr ..."
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Cited by 9 (0 self)
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Abstract. The difference between the quadratic Lgroups L∗(A) and the symmetric Lgroups L ∗ (A) of a ring with involution A is detected by generalized Arf invariants. The special case A = Z[x] gives a complete set of invariants for the Cappell UNilgroups UNil∗(Z; Z, Z) for the infinite dihedral
www.elsevier.com/locate/aim GeneralizedArf invariants in algebraic Ltheory
, 2005
"... The difference between the quadratic Lgroups L∗(A) and the symmetric Lgroups L∗(A) of a ring with involution A is detected by generalized Arf invariants. The special case A = Z[x] gives a complete set of invariants for the Cappell UNilgroups UNil∗(Z;Z,Z) for the infinite dihedral group D ∞ = Z2 ..."
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The difference between the quadratic Lgroups L∗(A) and the symmetric Lgroups L∗(A) of a ring with involution A is detected by generalized Arf invariants. The special case A = Z[x] gives a complete set of invariants for the Cappell UNilgroups UNil∗(Z;Z,Z) for the infinite dihedral group D ∞ = Z2
www.elsevier.com/locate/aim Generalized Arf invariants in algebraic Ltheory
, 2005
"... The difference between the quadratic Lgroups L∗(A) and the symmetric Lgroups L ∗ (A) of a ring with involution A is detected by generalized Arf invariants. The special case A = Z[x] gives a complete set of invariants for the Cappell UNilgroups UNil∗(Z; Z, Z) for the infinite dihedral group D ∞ = ..."
Abstract
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The difference between the quadratic Lgroups L∗(A) and the symmetric Lgroups L ∗ (A) of a ring with involution A is detected by generalized Arf invariants. The special case A = Z[x] gives a complete set of invariants for the Cappell UNilgroups UNil∗(Z; Z, Z) for the infinite dihedral group D
Multiresolution grayscale and rotation invariant texture classification with local binary patterns
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper presents a theoretically very simple, yet efficient, multiresolution approach to grayscale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain ..."
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Cited by 1299 (39 self)
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that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized grayscale and rotation invariant operator presentation that allows for detecting the "
Visual categorization with bags of keypoints
 In Workshop on Statistical Learning in Computer Vision, ECCV
, 2004
"... Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of im ..."
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Cited by 1005 (14 self)
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Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors
Bundle Adjustment  A Modern Synthesis
 VISION ALGORITHMS: THEORY AND PRACTICE, LNCS
, 2000
"... This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics c ..."
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Cited by 562 (13 self)
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covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than
Axiomatic quantum field theory in curved spacetime
, 2008
"... The usual formulations of quantum field theory in Minkowski spacetime make crucial use of features—such as Poincare invariance and the existence of a preferred vacuum state—that are very special to Minkowski spacetime. In order to generalize the formulation of quantum field theory to arbitrary globa ..."
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Cited by 689 (18 self)
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The usual formulations of quantum field theory in Minkowski spacetime make crucial use of features—such as Poincare invariance and the existence of a preferred vacuum state—that are very special to Minkowski spacetime. In order to generalize the formulation of quantum field theory to arbitrary
Realtime human pose recognition in parts from single depth images
 IN CVPR
, 2011
"... We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler p ..."
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Cited by 568 (17 self)
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perpixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidencescored 3D proposals of several body joints by reprojecting the classification result and finding
Parallel Networks that Learn to Pronounce English Text
 COMPLEX SYSTEMS
, 1987
"... This paper describes NETtalk, a class of massivelyparallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed h ..."
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Cited by 549 (5 self)
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human performance. (i) The learning follows a power law. (;i) The more words the network learns, the better it is at generalizing and correctly pronouncing new words, (iii) The performance of the network degrades very slowly as connections in the network are damaged: no single link or processing unit
Results 1  10
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