In this paper, a new algorithm for moving object detection is proposed by using unsupervised bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the firs...
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In this paper, a new algorithm for moving object detection is proposed by using unsupervised bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the first contains of classify and estimate the motion vectors between successive frames using the Star diamond search algorithm based on unsupervised bayesian classifier with Gaussian Expectation of Maximization algorithm, this step serves also to detect the static and dynamic blocks. In the second step, the dynamic blocks are compensated with the white pixels value and the stationary are compensated by black pixels value. In the third step, the morphological opening and closing filters are used for refining the object detected. The proposed approach is trained and evaluated using available infrared (FLIR_ADAS_v2) dataset. The results demonstrate the effectiveness of the proposed method.
This paper proposes a two-step block matching algorithm for motion estimation. To reduce the algorithmic complexity, the unsupervised bayesian classifier with resampling bootstrap Gaussian expectation of maximization ...
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In a fully automatic cell extraction process, one of the main issues to overcome is the problem related to extracting overlapped nuclei since such nuclei will often affect the quantitative analysis of cell images. In ...
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In a fully automatic cell extraction process, one of the main issues to overcome is the problem related to extracting overlapped nuclei since such nuclei will often affect the quantitative analysis of cell images. In this paper, we present an unsupervisedbayesian classification scheme for separating overlapped nuclei. The proposed approach first involves applying the distance transform to overlapped nuclei. The topographic surface generated by distance transform is viewed as a mixture of Gaussians in the proposed algorithm. In order to learn the distribution of the topographic surface, the parametric expectation-maximization (EM) algorithm is employed. Cluster validation is performed to determine how many nuclei are overlapped. Our segmentation approach incorporates a priori knowledge about the regular shape of clumped nuclei to yield more accurate segmentation results. Experimental results show that the proposed method yields superior segmentation performance, compared to those produced by conventional schemes.
Overlapping speaker localization approaches generally require a binary detector which performs the source/noise classification of the location estimates. This is mainly due to the unknown time-varying number of source...
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ISBN:
(纸本)9781479968084
Overlapping speaker localization approaches generally require a binary detector which performs the source/noise classification of the location estimates. This is mainly due to the unknown time-varying number of sources, and to the presence of noise and reverberation. In this paper, we firstly introduce an online implementation of a previously developed offline multiple speaker detector. This classifier is then extended to include new detection features. More precisely, the proposed approach uses the classified location estimates as labelled data to train new classification models for different potential features. The resulting models are then integrated into the online classifier to improve the classification performance. In particular, this paper investigates three different classification history-based models, namely, the location, the kurtosis and the probabilistic steered response power features. Experiments conducted on the AV16.3 corpus show the effectiveness of the proposed approach.
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