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Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
In this paper, we propose a full reference (FR) video quality assessment (VQA) algorithm for the broadcasting multimedia signal. The relationships between the VQA parameters and the quality score obtained by the subjective human visual system (HVS) model of the distorted videos are investigated. Considering the high correlations and the importance of the VQA parameters, a new FR VQA algorithmis worked...
This paper presents novel algorithms for shot boundary detection and key frames extraction. The algorithm differs from conventional methods mainly in the use of image segmentation and attention model. Matching difference between two consecutive frames is computed with different weights. Shot boundaries are detected with automatic threshold. Key frame is extracted by using reference frame-based approach...
In many computer vision related applications it is necessary to distinguish between the background of an image and the objects that are contained in it. This is a difficult problem because of the double constraint on the available time and the computational cost of robust object extraction algorithms. This paper builds upon former work on combining the strong theoretical foundations of clustering...
Video object segmentation is a key technology in video processing, which is the foundation of the newly object-based video compression standard MPEG-4. A novel spatio-temporal video object segmentation algorithm is proposed in this paper, which is very efficient to acquire the video object moving in a static background. Firstly, video sequence is segmented with change detection algorithm to get segmentation...
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