Shape Classification using Approximately Convex Segment Features
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911357764370432 |
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| author | Ray, Bimal Kumar |
| author_facet | Ray, Bimal Kumar |
| contents | The existing object classification techniques based on descriptive features rely on object alignment to compute the similarity of objects for classification. This paper replaces the necessity of object alignment through sorting of feature. The object boundary is normalized and segmented into approximately convex segments and the segments are then sorted in descending order of their length. The segment length, number of extreme points in segments, area of segments, the base and the width of the segments - a bag of features - is used to measure the similarity between image boundaries. The proposed method is tested on datasets and acceptable results are observed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03625 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Shape Classification using Approximately Convex Segment Features Ray, Bimal Kumar Computer Vision and Pattern Recognition The existing object classification techniques based on descriptive features rely on object alignment to compute the similarity of objects for classification. This paper replaces the necessity of object alignment through sorting of feature. The object boundary is normalized and segmented into approximately convex segments and the segments are then sorted in descending order of their length. The segment length, number of extreme points in segments, area of segments, the base and the width of the segments - a bag of features - is used to measure the similarity between image boundaries. The proposed method is tested on datasets and acceptable results are observed. |
| title | Shape Classification using Approximately Convex Segment Features |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.03625 |