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Mitigation of Effects of Occlusion on Object Recognition with Deep Neural Networks through Low-Level Image Completion
Heavily occluded objects are more difficult for classification algorithms to identify correctly than unoccluded objects. This effect is rare and thus hard to measure with datasets like ImageNet and PASCAL VOC, however, owing to biases in human-generated image pose selection. We introduce a dataset t...
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| Publicado en: | Comput Intell Neurosci |
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| Autores principales: | , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Hindawi Publishing Corporation
2016
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4908250/ https://ncbi.nlm.nih.gov/pubmed/27340396 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1155/2016/6425257 |
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