Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation
Current methods for encoding and comparing shapes are computationally demanding and are not suitable for image processing in small portable devices. Here, we describe a simple scan encoding method for transcribing shape information into a 1-D summary. Summaries were derived from an inventory of unkn...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8405528/ |
| _version_ | 1818876879278440448 |
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| author | Ernest Greene Jack Morrison |
| author_facet | Ernest Greene Jack Morrison |
| author_sort | Ernest Greene |
| collection | DOAJ |
| description | Current methods for encoding and comparing shapes are computationally demanding and are not suitable for image processing in small portable devices. Here, we describe a simple scan encoding method for transcribing shape information into a 1-D summary. Summaries were derived from an inventory of unknown shapes, and these values were used to scale the degree of similarity of pair combinations. The scale values provided a significant level of prediction of human judgments in a match recognition task, suggesting substantial correspondence with human perception of shape similarity. Similarity scores derived with the Procrustes method did not predict human judgments. |
| first_indexed | 2024-12-19T13:49:24Z |
| format | Article |
| id | doaj.art-49390376114042d3970db9f92e098a7c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:49:24Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-49390376114042d3970db9f92e098a7c2022-12-21T20:18:47ZengIEEEIEEE Access2169-35362018-01-016382943830210.1109/ACCESS.2018.28536568405528Computational Scaling of Shape Similarity That has Potential for Neuromorphic ImplementationErnest Greene0https://orcid.org/0000-0002-4458-4760Jack Morrison1Department of Psychology, University of Southern California, Los Angeles, CA, USADigital Insight, Somis, CA, USACurrent methods for encoding and comparing shapes are computationally demanding and are not suitable for image processing in small portable devices. Here, we describe a simple scan encoding method for transcribing shape information into a 1-D summary. Summaries were derived from an inventory of unknown shapes, and these values were used to scale the degree of similarity of pair combinations. The scale values provided a significant level of prediction of human judgments in a match recognition task, suggesting substantial correspondence with human perception of shape similarity. Similarity scores derived with the Procrustes method did not predict human judgments.https://ieeexplore.ieee.org/document/8405528/Shape encodingshape similarityretinal encodinghuman judgments |
| spellingShingle | Ernest Greene Jack Morrison Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation IEEE Access Shape encoding shape similarity retinal encoding human judgments |
| title | Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation |
| title_full | Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation |
| title_fullStr | Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation |
| title_full_unstemmed | Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation |
| title_short | Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation |
| title_sort | computational scaling of shape similarity that has potential for neuromorphic implementation |
| topic | Shape encoding shape similarity retinal encoding human judgments |
| url | https://ieeexplore.ieee.org/document/8405528/ |
| work_keys_str_mv | AT ernestgreene computationalscalingofshapesimilaritythathaspotentialforneuromorphicimplementation AT jackmorrison computationalscalingofshapesimilaritythathaspotentialforneuromorphicimplementation |