Comparing methods for scaling shape similarity

Ernest Greene; Ernest Greene

doi:10.3934/Neuroscience.2019.2.54

AIMS Neuroscience

2019, Volume 6, Issue 2: 54-59. doi: 10.3934/Neuroscience.2019.2.54

Previous Article Next Article

Commentary

Comparing methods for scaling shape similarity

Ernest Greene ^,

Laboratory for Neurometric Research, Department of Psychology, University of Southern California,Los Angeles, California, USA

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:

[1]	Hannah Nordberg, Michael J Hautus, Ernest Greene . Visual encoding of partial unknown shape boundaries. AIMS Neuroscience, 2018, 5(2): 132-147. doi: 10.3934/Neuroscience.2018.2.132
[2]	Tien-Wen Lee, Gerald Tramontano . Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements. AIMS Neuroscience, 2021, 8(4): 526-542. doi: 10.3934/Neuroscience.2021028
[3]	Ernest Greene, Michael J. Hautus . Demonstrating Invariant Encoding of Shapes Using A Matching Judgment Protocol. AIMS Neuroscience, 2017, 4(3): 120-147. doi: 10.3934/Neuroscience.2017.3.120
[4]	Ernest Greene, Michael J. Hautus . Evaluating persistence of shape information using a matching protocol. AIMS Neuroscience, 2018, 5(1): 81-96. doi: 10.3934/Neuroscience.2018.1.81
[5]	Ernest Greene . New encoding concepts for shape recognition are needed. AIMS Neuroscience, 2018, 5(3): 162-178. doi: 10.3934/Neuroscience.2018.3.162
[6]	Paul G. Nestor, Toshiyuki Ohtani, James J. Levitt, Dominick T. Newell, Martha E. Shenton, Margaret Niznikiewicz, Robert W. McCarley . Prefrontal Lobe Gray Matter, Cognitive Control and Episodic Memory in Healthy Cognition. AIMS Neuroscience, 2016, 3(3): 338-355. doi: 10.3934/Neuroscience.2016.3.338
[7]	Mercedes Fernandez, Juliana Acosta, Kevin Douglass, Nikita Doshi, Jaime L. Tartar . Speaking Two Languages Enhances an Auditory but Not a Visual Neural Marker of Cognitive Inhibition. AIMS Neuroscience, 2014, 1(2): 145-157. doi: 10.3934/Neuroscience.2014.2.145
[8]	Sherry Zhang, Jack Morrison, Wei Wang, Ernest Greene . Recognition of letters displayed as successive contour fragments. AIMS Neuroscience, 2022, 9(4): 491-515. doi: 10.3934/Neuroscience.2022028
[9]	Tiziana M. Florio . Stereotyped, automatized and habitual behaviours: are they similar constructs under the control of the same cerebral areas?. AIMS Neuroscience, 2020, 7(2): 136-152. doi: 10.3934/Neuroscience.2020010
[10]	Yasir Rehman, Cindy Zhang, Haolin Ye, Lionel Fernandes, Mathieu Marek, Andrada Cretu, William Parkinson . The extent of the neurocognitive impairment in elderly survivors of war suffering from PTSD: meta-analysis and literature review. AIMS Neuroscience, 2021, 8(1): 47-73. doi: 10.3934/Neuroscience.2021003

Acknowledgments

This work was supported by the Quest for Truth Foundation.

Conflict of interest

The author declares no conflict of interest.

References

[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

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)