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A fast and accurate proposal method of human detection called personness estimation is proposed, which facilitates real-time human detection on mobile devices and can be effectively integrated into part-based detection, achieving high detection performance at a low computational cost.
The simulation results show that our proposal method is an efficient and robust algorithm for multicast route selection.
The optimal multi-objective free-form of a shell structure with an orthotropic material can be determined using the proposal method, and the influence of the orthotropic angle to the optimal shape and thickness distribution is investigated in detail.
The examination was carried out using the Inst. of Electrical Engineers of Japan one-machine infinite bus system model in order to demonstrate the effectiveness of the proposal method.
Experimental results demonstrate the effectiveness of our proposal method.
The other advantage of the proposal method is simplicity.
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One aim of detection proposal methods is to reduce the computational overhead of object detection.
We also explore other factors such as proposal methods and imbalance of training samples.
The algorithm only takes 0.15 s and can be applied to any existing proposal methods to improve their localization quality.
Our approach may be considered as an accurate generalization of previous proposal methods, to the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity.
Extensive experiments on PASCAL VOC 2007 and ILSVRC 2013 datasets show our approach significantly and consistently improves the recall, localization accuracy, and detection performance of existing proposal methods.
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