Comparing methods for scaling shape similarity

  • Received: 08 January 2019 Accepted: 24 April 2019 Published: 05 May 2019
  • Citation: Ernest Greene. Comparing methods for scaling shape similarity[J]. AIMS Neuroscience, 2019, 6(2): 54-59. doi: 10.3934/Neuroscience.2019.2.54

    Related Papers:

  • 加载中


    This work was supported by the Quest for Truth Foundation.

    Conflict of interest

    The author declares no conflict of interest.

    [1] Greene E, Morrison J (2018) Computational scaling of shape similarity that has potential for neuromorphic implementation. IEEE Access 6: 38294–38302. doi: 10.1109/ACCESS.2018.2853656
    [2] Kandell DG (1981) The statistics of shape. In: Barnett V ed, Interpreting Multivariate Data, New York: Wiley & Sons, 75–80.
    [3] Kandell DG (1984) Shape-manifolds, Procrustean metrics and complex projective spaces. Bull Lond Math Soc 16: 81–121. doi: 10.1112/blms/16.2.81
    [4] Kandell DG (1985) Exact distributions for shapes of random triangles in convex sets. Adv App Prob 17: 308–329. doi: 10.2307/1427143
    [5] Goodall C (1991) Procrustes methods in the statistical analysis of shape. J Royal Stat Soc B 53: 285–339.
    [6] Vermeesch P, Garzanti E (2015) Making geological sense of "big data" in sedimentary provenance. Chem Geol 409: 20–27. doi: 10.1016/j.chemgeo.2015.05.004
    [7] Mitteroecker P, Gunz P, Windhager S, et al. (2013) A brief review of shape, form, and allometry in geometric morphometrics, with applications to human facial morphology. Hystrix Ital J Mammal 24: 59–66.
    [8] O'Higgins P (2000) The study of morphological variation in the hominid fossil record: biology, landmarks and geometry. J Anat 197: 203–220.
    [9] Slice DE (2007) Geometric morphometrics. Ann Rev Anthropol 36: 261–281. doi: 10.1146/annurev.anthro.34.081804.120613
    [10] Dryden IL, Mardia KV (2016) Statistical Shape Analysis (2nd Ed), United Kingdom: Wiley & Sons.
    [11] Greene E, Petel Y (2018) Scan transcription of two-dimensional shapes as an alternative neuromorphic concept. Trends Artific Intell 1: 27–33.
    [12] Greene E, Hautus MJ (2017) Demonstrating invariant encoding of shapes using a matching judgment protocol. AIMS Neurosci 4: 120–147. doi: 10.3934/Neuroscience.2017.3.120
    [13] Greene E (2007) Retinal encoding of ultrabrief shape recognition cues. PLoS One 2: e871. doi: 10.1371/journal.pone.0000871
    [14] Gollisch T, Meister M (2008) Rapid neural coding in the retina with relative spike latencies. Science 319: 1108–1111. doi: 10.1126/science.1149639
    [15] Ahissar E, Arieli A (2012) Seeing via miniature eye movements: a dynamic hypothesis for vision. Front Comput Neurosci 6: 1–27.
    [16] Rucci M, Victor JD (2015) The unsteady eye: an information-processing stage, not a bug. Trends Neurosci 38: 195–206. doi: 10.1016/j.tins.2015.01.005
    [17] Greene E (2018) New encoding concepts for shape recognition are needed. AIMS Neurosci 5: 162–178. doi: 10.3934/Neuroscience.2018.3.162
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (
通讯作者: 陈斌,
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索


Article views(2047) PDF downloads(1485) Cited by(0)

Article outline

Figures and Tables


Other Articles By Authors


DownLoad:  Full-Size Img  PowerPoint