Mitigation of Effects of Occlusion on Object Recognition with Deep Neural Networks through Low-Level Image Completion
نویسندگان
چکیده
منابع مشابه
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 that emphasizes occlusion and additions to a standard convolutional neural network aimed at increa...
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ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2016
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2016/6425257