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To compare and evaluate the performance of iterative spectrally smooth temperature and emissivity separation method (ISSTES) and linear emissivity constraint temperature and emissivity separation method (LECTES) on land surface temperature (LST) and land surface emissivity (LSE) estimation, the simulation data for hyperspectral infrared spectroradiometer are used. The results reveal that the LST can...
Depth-image-based rendering (DIBR) produces multiple views efficiently. However, its process lacks some viewpoint information. There will be holes, which influences the 3D video quality. Previous DIBR techniques were mainly applied to 3D images, so it only relied on the single view and the depth map to fill the holes, but insufficient repair information resulted in incorrect repair. In this paper,...
Sensing technologies have made the tracking of users' daily trajectories a common service, such as location based service, children/elders tracking service, etc. This information can also be analyzed to discover some interesting and meaningful information about users. In this paper, we study the Routine Based Classification (RBC) approach for classifying users into different groups. For comparing...
This paper investigates the impacts of different discrete granularities of continuous attribute on the algorithms which employ association rules to complete missing value. The Chi2 algorithm is used to discrete the continuous attributes. By using three different discrete granularities, the impacts of the algorithm on completing missing value are explored. Experimental results show that different discrete...
Gender recognition is a hot research topic in recent years. Human-machine interfaces or video surveillance can be greatly improved if human gender can be recognized automatically. In this study, an embedded hidden Markov model is used for gender recognition. Video, which is recorded in different angles of view, is utilized to sample properties of each gender. Ten consecutive gait frames are segmented...
This paper presents a new approach to credit scoring by synthesizing simple nai??ve Bayesian classifier (SNBC) and the rough set theory. We adopted the combination of SNBC and rough set theory to build credit scoring model. The experiment was done on German Credit Database and showed that the model has a good prediction performance and has real world value upon application.
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