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Libros sobre el tema "Object invariants"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 19 de febrero de 2022

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1

Object recognition through invariant indexing. Oxford: Oxford University Press, 1995.

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2

Lamdan, Yehezkel. Object recognition by affine invariant matching. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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3

Grace, Alan Edward. Adaptive segmentation for aspect invariant object recognition. Birmingham: Universityof Birmingham, 1993.

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4

Kao, Chang-Lung. Affine invariant matching of noisy objects. Monterey, Calif: Naval Postgraduate School, 1989.

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5

Hsu, Tao-i. Affine invariant object recognition by voting match techniques. Monterey, Calif: Naval Postgraduate School, 1988.

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6

Reiss, Thomas H. Recognizing planar objects using invariant image features. Berlin: Springer-Verlag, 1993.

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7

Reiss, Thomas H., ed. Recognizing Planar Objects Using Invariant Image Features. Berlin/Heidelberg: Springer-Verlag, 1993. http://dx.doi.org/10.1007/bfb0017553.

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8

Kyrki, Ville. Local and global feature extraction for invariant object recognition. Lappeenranta, Finland: Lappeenranta University of Technology, 2002.

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9

Group, IRIS, ed. Fast learning and invariant object recognition: The sixth-generation breakthrough. New York: Wiley, 1992.

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10

Soucek, Branko. Fast learning and invariant object recognition: The sixth-generation breakthrough. New York: Wiley, 1992.

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11

Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.

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12

Bart, Evgeniy, ed. Invariant Recognition of Visual Objects. Frontiers Media SA, 2013. http://dx.doi.org/10.3389/978-2-88919-076-8.

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13

Pong, Simon W. L. Rotationally-invariant pecstrum: An object descriptior based on mathematical morphology. 1990, 1990.

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14

Awodey, Steve. Structuralism, Invariance, and Univalence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0004.

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The recent discovery of an interpretation of constructive type theory into abstract homotopy theory suggests a new approach to the foundations of mathematics with intrinsic geometric content and a computational implementation. Voevodsky has proposed such a program, including a new axiom with both geometric and logical significance: the univalence axiom. It captures the familiar aspect of informal mathematical practice according to which one can identify isomorphic objects. This powerful addition to homotopy type theory gives the new system of foundations a distinctly structural character.

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15

Invariant Object Recognition Based on Elastic Graph Matching (Frontiers in Artificial Intelligence and Applications, 86). Ios Pr Inc, 2002.

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16

Bonneh, Yoram. Motion-Induced Blindness. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0103.

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Motion-induced blindness (MIB) is a phenomenon characterized by “visual disappearance” in which relatively small but salient visual objects may disappear from one’s awareness intermittently for several seconds when embedded within a moving pattern. It is a compelling example of multistable perception in which physically invariant stimulation leads to fluctuations in perception. The interest in MIB stems from its potential use in studying visual processing outside the locus of awareness and the neural correlates of consciousness. Current studies of MIB provide evidence against low-level suppression of the visual signal and demonstrate residual processing of the invisible. This chapter explores these and related concepts.

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17

Mann, Peter. Newton’s Three Laws. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0001.

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This chapter introduces Newton’s laws, the Newtonian formulation of mechanics and key concepts such as configuration space and phase space for later development. In 1687, the natural philosopher Sir Isaac Newton published the Principia Mathematica and, with it, sparked the revolutionary ideas key to all branches of classical physics. In this chapter, the system is the object of interest and is considered to be either a single or a collection of generic particles that are not governed by quantum mechanics, for quantum systems do not follow these laws explicitly. Results for systems of particles and conservation laws are presented as the invariance of a given quantity under time evolution. The N-body problem, first integrals, initial value problems and Galilean transformations are all introduced and the Picard iteration and the Verlet algorithm are discussed.

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18

Akemann, Gernot, Jinho Baik y Philippe Di Francesco, eds. The Oxford Handbook of Random Matrix Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.001.0001.

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This handbook showcases the major aspects and modern applications of random matrix theory (RMT). It examines the mathematical properties and applications of random matrices and some of the reasons why RMT has been very successful and continues to enjoy great interest among physicists, mathematicians and other scientists. It also discusses methods of solving RMT, basic properties and fundamental objects in RMT, and different models and symmetry classes in RMT. Topics include the use of classical orthogonal polynomials (OP) and skew-OP to solve exactly RMT ensembles with unitary, and orthogonal or symplectic invariance respectively, all at finite matrix size; the supersymmetric and replica methods; determinantal point processes; Painlevé transcendents; the fundamental property of RMT known as universality; RNA folding; two-dimensional quantum gravity; string theory; and the mathematical concept of free random variables. In addition to applications to mathematics and physics, the book considers broader applications to other sciences, including economics, engineering, biology, and complex networks.

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19

Jeffares, Ben y Kim Sterelny. Evolutionary Psychology. Editado por Eric Margolis, Richard Samuels y Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0020.

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The article presents several models of evolutionary psychology. Nativist evolutionary psychology is built around a most important insight that ordinary human decision-making has a high cognitive load. Evolutionary nativists defend a modular solution to the problem of information load on human decision-making. Human minds comprises of special purpose cognitive devices or modules. One of the modules is a language module, a module for interpreting the thoughts and intentions of others, another is a ‘naive physics’ module for causal reasoning about sticks, stones, and similar inanimate objects, a natural history module for ecological decisions, and a social exchange module for monitoring economic interactions with peers. These modules evolved in response to the distinctive, independent, and recurring problems faced by the ancestors. Domain specific modules handle information about human language, human minds, inanimate causal interactions, the biological world, and other constant adaptive demands faced by human ancestors. Nativist evolutionary psychologists have turned to moral decision making, arguing that cross-cultural moral judgments are invariant in an unexpected way. Natural selection can build and equip a special purpose module only if the information an agent needs to know is stable over evolutionary time. Automatized skills are an alternative means of coping with high-load problems. These skills are phenomenologically rather like modules, but they have very different developmental and evolutionary histories.

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