Due to background light fluctuation,noise interference,voltage fluctuation,and other factors,there will be noise interference of different intensities in the background of the collected image.In this paper,a PIV image...Due to background light fluctuation,noise interference,voltage fluctuation,and other factors,there will be noise interference of different intensities in the background of the collected image.In this paper,a PIV image background interference removal algorithm based on improved neighborhood Otsu processing is proposed.The algorithm proposed in this paper separates the particle image from the background interference through the adaptive neighborhood improved Otsu threshold segmentation method and uses the common PIV analysis tools PIVLab and para PIV to analyze the flow pattern after the interference is removed.The experimental results demonstrated that the proposed algorithm can obviously improve the quality of PIV results in terms of both PSNR and SSIM in the case of background light interference,and the increase in average performance is nearly 50%compared with traditional preprocessing algorithms,which solves the problem of large flow pattern analysis error caused by poor background light removal effect in the case of irregular grating and other background light interference only using traditional preprocessing.展开更多
Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estima...Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.展开更多
Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the c...Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.展开更多
To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is ex...To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.展开更多
文摘Due to background light fluctuation,noise interference,voltage fluctuation,and other factors,there will be noise interference of different intensities in the background of the collected image.In this paper,a PIV image background interference removal algorithm based on improved neighborhood Otsu processing is proposed.The algorithm proposed in this paper separates the particle image from the background interference through the adaptive neighborhood improved Otsu threshold segmentation method and uses the common PIV analysis tools PIVLab and para PIV to analyze the flow pattern after the interference is removed.The experimental results demonstrated that the proposed algorithm can obviously improve the quality of PIV results in terms of both PSNR and SSIM in the case of background light interference,and the increase in average performance is nearly 50%compared with traditional preprocessing algorithms,which solves the problem of large flow pattern analysis error caused by poor background light removal effect in the case of irregular grating and other background light interference only using traditional preprocessing.
基金supported by the National Natural Science Foundation of China (61302188)the Nanjing University of Science and Technology Research Foundation (2010ZDJH05)
文摘Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.
文摘Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.
基金The National Natural Science Foundation of China(No.50805023)the Science and Technology Support Program of Jiangsu Province(No.BE2008081)+1 种基金the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2010093)the Program for Special Talent in Six Fields of Jiangsu Province(No.2008144)
文摘To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.
