Tensorial properties of multiple view constraints

被引:0
|
作者
Heyden, A [1 ]
机构
[1] Lund Univ, Dept Math, S-22100 Lund, Sweden
关键词
D O I
10.1002/(SICI)1099-1476(20000125)23:2<169::AID-MMA110>3.0.CO;2-Y
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We define and derive some properties of the different multiple view tensors. The multiple view geometry is described using a four-dimensional linear manifold in R-3m, where m denotes the number of images. The Grassman co-ordinates of this manifold build up the components of the different multiple view tensors. All relations between these Grassman co-ordinates can be expressed using the quadratic p-relations. From this formalism it is evident that the multiple view geometry is described by four different kinds of projective invariants; the epipoles, the fundamental matrices, the trifocal tensors and the quadrifocal tensors. We derive all constraint equations on these tensors that can be used to estimate them from corresponding points and/or lines in the images as well as all transfer equations that can be used to transfer features seen in some images to another image. As an application of this formalism we show how a representation of the multiple view geometry can be calculated from different combinations of multiple view tensors and how some tensors can be extracted from others. We also give necessary and sufficient conditions for the tensor components, i.e, the constraints they have to obey in order to build up a correct tensor, as well as for arbitrary combinations of tensors. Finally, the tensorial rank of the different multiple view tensors are considered and calculated. Copyright (C) 2000 John Wiley & Sons, Ltd.
引用
收藏
页码:169 / 202
页数:34
相关论文
共 50 条
  • [41] Tensorial phases in multiple beam atomic interference -: art. no. 020101
    Mei, M
    Hänsch, TW
    Weitz, M
    PHYSICAL REVIEW A, 2000, 61 (02): : 1 - 4
  • [42] Logarithmic Schatten-p Norm Minimization for Tensorial Multi-View Subspace Clustering
    Guo, Jipeng
    Sun, Yanfeng
    Gao, Junbin
    Hu, Yongli
    Yin, Baocai
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 45 (03) : 3396 - 3410
  • [43] PARAMETERS FOR MULTIPLE CONSTRAINTS
    CHONG, DP
    BENSTON, ML
    THEORETICA CHIMICA ACTA, 1968, 12 (02): : 175 - &
  • [44] Identification of emergent constraints and hidden order in frustrated magnets using tensorial kernel methods of machine learning
    Greitemann, Jonas
    Liu, Ke
    Jaubert, Ludovic D. C.
    Yan, Han
    Shannon, Nic
    Pollet, Lode
    PHYSICAL REVIEW B, 2019, 100 (17)
  • [45] PIECEWISE SINGLE VIEW PHOTOMETRIC STEREO WITH MULTI-VIEW CONSTRAINTS
    Sabzevari, Reza
    Del Bue, Alessio
    Murino, Vittorio
    2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), 2012, : 21 - 24
  • [46] Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
    Grisafi, Andrea
    Wilkins, David M.
    Csanyi, Gabor
    Ceriotti, Michele
    PHYSICAL REVIEW LETTERS, 2018, 120 (03)
  • [47] Configuration determination by residual dipolar couplings: accessing the full conformational space by molecular dynamics with tensorial constraints
    Tzvetkova, Pavleta
    Sternberg, Ulrich
    Gloge, Thomas
    Navarro-Vazquez, Armando
    Luy, Burkhard
    CHEMICAL SCIENCE, 2019, 10 (38) : 8774 - 8791
  • [48] A view on multiple recurrence
    Eisner, Tanja
    INDAGATIONES MATHEMATICAE-NEW SERIES, 2023, 34 (02): : 231 - 247
  • [49] Multiple view vision
    Åstrom, K
    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1, PROCEEDINGS: COMPUTER VISION AND IMAGE ANALYSIS, 2000, : 59 - 66
  • [50] Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm
    Liu, Wenzhe
    Jiang, Li
    Liu, Da
    Zhang, Yong
    IEEE ACCESS, 2023, 11 : 11132 - 11142