human action recognition is an important problem in computer vision. one of the methods that are recently used is sparse coding. conventional sparse coding algorithms learn dictionaries and codes in an unsupervised manner and neglect class information that is available in the training set. but in this paper for solving this problem, we use a discriminative sparse code based on multi-manifolds. ...

The spectacular evolution of sensor networks and the proliferation of location-sensing devices in daily life activities are leading to an explosion of disparate spatio-temporal data. The collected data describes the movement of mobile objects used to construct what we call trajectory data. The diversity of these generated data has led to a variety of spatio-temporal models. In fact, the concept...

The concept and applications of spatio-temporal optical computing are proposed. This concept introduces ordinary spatial optical computing techniques using Fraunhofer and Fresnel diffraction into time domain. The concept is classified in three phases. The first phase employs spatiotemporal duality of light. In this thesis a new algorithm to analyze low coherence interferograms is proposed in th...

Bacillary dysentery (BD) remains a big public health problem in China. Effective spatio-temporal monitoring of BD incidence is important for successful implementation of control and prevention measures. This study aimed to examine the spatio-temporal pattern of BD and analyze socio-economic factors that may affect BD incidence in Sichuan province, China. Firstly, we used space-time scan statist...

There exist numerous indexing methods which handle either spatio-temporal or high-dimensional data well. However, those indexing methods which handle spatio-temporal data well have certain drawbacks when confronted with high-dimensional data. As the most efficient spatio-temporal indexing methods are based on the R-tree and its variants, they face the well known problems in high-dimensional spa...

We consider spatio-temporal interpolation of geographic data using both the reduction method, which treats time as an independent dimension, and the extension method, which treats time as equivalent to a spatial dimension. We adopt both 2-D and 3-D shape functions from finite element methods for the spatio-temporal interpolation of 2-D spatial and 1-D temporal data sets. We also develop new 4-D...

The requirements for efficient access and management of massive multi-dimensional spatio-temporal data in geographical information system and its applications are well recognized and researched. The most popular spatio-temporal access method is the R-Tree and its variants. However, it is difficult to use them for parallel access to multi-dimensional spatio-temporal data because R-Trees, and var...

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effective...