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This paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose...
The real-time and accurate forecasting of expressway traffic is an important prerequisite for the development of reasonable traffic management control scheme, the reasonable choice of travel route and the effective prevention of traffic congestion. The current traffic data are mostly collected using a variety of monitoring equipment, but the data can only reflect a certain traffic flow and the cost...
The reliability and life predictions of products using small samples represent a major challenge to reliability engineers. In this paper, we develop a physics-statistics (P–S)-based model and an adaptive Kalman filter approach for reliability and life predictions. The P–S-based model combines the physics-of-failure models with the statistics models to consider the randomness among identical products...
The location information in social media often provides insight on the repercussions of events and is becoming more and more vital to the applications such as localized search, local news recommendation and emergency detection. However, for the security and privacy concerns, most users in social media are unwilling to publish their location, and they may post non-existing places or submit unspecific...
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