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- W204537088 abstract "Object detection is widely employed in a large number of areas, such as human detection, medical image processing, etc. However, it is insufficient to use only a learning algorithm to detect objects and more techniques or models, such as a probability based approach, a part model, a segmentation model, are combined with the learning algorithm to accomplish the detection task. To this end, a fusion approach is required to balance the decisions making by multiple models. This paper proposes an optimization methodology that fuses a set of confidence outputs estimated by multiple models. Various experiments are executed and demonstrate that the proposed fusion method has a relative better performance than that of the system constituted by a single model." @default.
- W204537088 created "2016-06-24" @default.
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- W204537088 date "2014-01-01" @default.
- W204537088 modified "2023-09-27" @default.
- W204537088 title "An Optimization Method of Fusing Multiple Decisions in Object Detection" @default.
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- W204537088 doi "https://doi.org/10.1007/978-3-319-13186-3_4" @default.
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