A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects...A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression was provided. The background samples were chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation was used to estimate the probability density function of background intensity. Pixel's neighbor information was considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information was used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation was demonstrated in the traffic surveillance application.展开更多
In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions ...In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.展开更多
ICM （Idealized Cognitive Model） theory put forward by Lakoff has a guiding function in the analysis of discourse coherence without the coherent devices, based on which WANG Yin mentioned cognitive world. The cogniti...ICM （Idealized Cognitive Model） theory put forward by Lakoff has a guiding function in the analysis of discourse coherence without the coherent devices, based on which WANG Yin mentioned cognitive world. The cognitive world is of two kinds： ICM and background knowledge. The process of understanding discourse is the process of activating the human＇s ICM and background knowledge so that coherence is realized.展开更多
The pyramidal multiphase level set framework （PMLSF） based on the technique of painting background （TPBG） and the Chan-Vese model can detect multiple objects on a given image. However, the boundaries of the sub-ob...The pyramidal multiphase level set framework （PMLSF） based on the technique of painting background （TPBG） and the Chan-Vese model can detect multiple objects on a given image. However, the boundaries of the sub-object obtained by PMLSF-TPBG are not variable since a specialcolor parameter is used in TPBG. To solve the problem, a new technique utilizing a varying parameter is proposed to ensure that PMLSF is effective for the detection of the desired boundaries of the sub-object. The interval of the variable color parameter is proved and the effects of the parameter are also discussed. Experimental results for the brain tumor detection show that different boundaries of the brain tumors can be detected with different color parameters. It is especially useful for clinical diagnoses.展开更多
Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher ...Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher order Markov chain model and how to automatically select the proper order are given in this paper. The chi square test is first run on synthetic data sets to show that it can efficiently find the proper order of Markov chain. Using chi square test, distinct higher order context dependences inherent in ten sets of sequences of yeast S.cerevisiae from other literature have been found. So the Markov chain with higher order would be more suitable for modeling the non coding background sequences than an independent model.展开更多
There exists a Ghost region in the detection result of the traditional visual background extraction(ViBe)algorithm,and the foreground extraction is prone to false detection or missed detection due to environmental cha...There exists a Ghost region in the detection result of the traditional visual background extraction(ViBe)algorithm,and the foreground extraction is prone to false detection or missed detection due to environmental changes.Therefore,an improved ViBe algorithm based on adaptive detection of moving targets was proposed.Firstly,in the background model initialization process,the real background could be obtained by setting adjusting parameters in mean background modeling,and the ViBe background model was initialized by using the background.Secondly,in the foreground detection process,an adaptive radius threshold was introduced according to the scene change to adaptively detect the foreground.Finally,mathematical morphological close operation was used to fill the holes in the detection results.The experimental results show that the improved method can effectively suppress the Ghost region and detect the foreground target more completely under the condition of environmental changes.Compared with the traditional ViBe algorithm,the detection accuracy is improved by more than 10%,the false detection rate and the missed detection rate are reduced by 20% and 7% respectively.In addition,the improved method satisfies the real-time requirements.展开更多
文摘A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression was provided. The background samples were chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation was used to estimate the probability density function of background intensity. Pixel's neighbor information was considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information was used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation was demonstrated in the traffic surveillance application.
基金The National Natural Science Foundation of China (No.61172135,61101198)the Aeronautical Foundation of China (No.20115152026)
文摘In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.
文摘ICM （Idealized Cognitive Model） theory put forward by Lakoff has a guiding function in the analysis of discourse coherence without the coherent devices, based on which WANG Yin mentioned cognitive world. The cognitive world is of two kinds： ICM and background knowledge. The process of understanding discourse is the process of activating the human＇s ICM and background knowledge so that coherence is realized.
文摘The pyramidal multiphase level set framework （PMLSF） based on the technique of painting background （TPBG） and the Chan-Vese model can detect multiple objects on a given image. However, the boundaries of the sub-object obtained by PMLSF-TPBG are not variable since a specialcolor parameter is used in TPBG. To solve the problem, a new technique utilizing a varying parameter is proposed to ensure that PMLSF is effective for the detection of the desired boundaries of the sub-object. The interval of the variable color parameter is proved and the effects of the parameter are also discussed. Experimental results for the brain tumor detection show that different boundaries of the brain tumors can be detected with different color parameters. It is especially useful for clinical diagnoses.
文摘Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher order Markov chain model and how to automatically select the proper order are given in this paper. The chi square test is first run on synthetic data sets to show that it can efficiently find the proper order of Markov chain. Using chi square test, distinct higher order context dependences inherent in ten sets of sequences of yeast S.cerevisiae from other literature have been found. So the Markov chain with higher order would be more suitable for modeling the non coding background sequences than an independent model.
基金National Natural Science Foundation of China(No.61761027)Postgraduate Education Reform Project of Lanzhou Jiaotong University(No.1600120101)。
文摘There exists a Ghost region in the detection result of the traditional visual background extraction(ViBe)algorithm,and the foreground extraction is prone to false detection or missed detection due to environmental changes.Therefore,an improved ViBe algorithm based on adaptive detection of moving targets was proposed.Firstly,in the background model initialization process,the real background could be obtained by setting adjusting parameters in mean background modeling,and the ViBe background model was initialized by using the background.Secondly,in the foreground detection process,an adaptive radius threshold was introduced according to the scene change to adaptively detect the foreground.Finally,mathematical morphological close operation was used to fill the holes in the detection results.The experimental results show that the improved method can effectively suppress the Ghost region and detect the foreground target more completely under the condition of environmental changes.Compared with the traditional ViBe algorithm,the detection accuracy is improved by more than 10%,the false detection rate and the missed detection rate are reduced by 20% and 7% respectively.In addition,the improved method satisfies the real-time requirements.
