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In applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets. These methods are slow to compute and not compact to use in a large scale scenario. Learning-based hashing is often used in...
Content-based 3D shape retrieval is an important problem in computer vision. Traditional retrieval interfaces require a 2D sketch or a manually designed 3D model as the query, which is difficult to specify and thus not practical in real applications. With the recent advance in low-cost 3D sensors such as Microsoft Kinect and Intel Realsense, capturing depth images that carry 3D information is fairly...
Detecting abnormal behaviors in crowd scenes is quite important for public security and has been paid more and more attentions. Most previous methods use offline trained model to perform detection which can't handle the constantly changing crowd environment. In this paper, we propose a novel unsupervised algorithm to detect abnormal behavior patterns in crowd scenes with online learning. The crowd...
Based on hydrology, a storm flood computation model for urban rain pipe networks is proposed. In this model, the research region is divided into three areas, that is permeable area, impervious area and pipe area. The main methods include the isochronal method, Horton infiltration formula, time-contour method and Muskingum method. The proposed model in this paper is tested by the rainfall runoff data...
The work of how to measure the difference, or deviation, between two models is an important studied problem. Venkatesh Ganti has developed the FOCUS framework for computing an interpretable, qualifiable deviation measure between two datasets in terms of the models they induce. In this paper, we propose a novel method to measure the deviation for cluster models making use of CF-tree based on the FOCUS...
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