Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while ...Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify interframe forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
During the sizing process,yarn congestion fault occurs at the reed teeth of a sizing machine.At present,the yarn congestion fault is generally handled by manual detection.The sizing production line operates on a large...During the sizing process,yarn congestion fault occurs at the reed teeth of a sizing machine.At present,the yarn congestion fault is generally handled by manual detection.The sizing production line operates on a large scale and runs continuously.Untimely handling of the yarn congestion fault causes a large amount of yarn waste.In this research,a machine vision-based algorithm for yarn congestion fault detection is developed.Through the analysis of the congestion fault and interference contour characteristics,the basic idea of image phase subtraction to identify the congestion fault is determined.To address the interference information appearing after image phase subtraction,the image pre-processing methods of Canny edge extraction and mean filtering are employed.According to the fault size and location characteristics,the fault contour detection algorithm based on inter-frame difference is designed.To mitigate the camera vibration interference,the anti-vibration interference algorithm based on affine transformation is studied,and the fault detection algorithm for the total yarn congestion fault is determined.The detection of 20 sets of field data is carried out,and the detection rate reaches 90%.This fault detection algorithm realizes the automatic detection of yarn congestion fault of sizing machine with certain real-time performance and accuracy.展开更多
针对合作靶标特征点成像在大跨度动态测量中的特征提取精度下降、实时性不足问题,提出了一种融合帧间运动预测与改进亚像素边缘检测的自适应光斑质心提取方法。基于合作靶标测量运动连贯性特性,构建了动态感兴趣区域(Region of Interest...针对合作靶标特征点成像在大跨度动态测量中的特征提取精度下降、实时性不足问题,提出了一种融合帧间运动预测与改进亚像素边缘检测的自适应光斑质心提取方法。基于合作靶标测量运动连贯性特性,构建了动态感兴趣区域(Region of Interest,ROI)特征参数模型,以帧间运动预测实现ROI的快速定位,结合大律法阈值优化策略实现自适应Canny边缘检测,在提升计算效率的同时有效解决了不同测量距离下的降噪问题。然后,采用多方向Sobel算子与强度斜坡改进的Zernike矩相结合改进了边缘点定位算法,并基于高斯牛顿迭代改进鲁棒最小二乘圆拟合法,实现质心坐标计算。实验结果表明:在仿真测试中,本方法在不同噪声水平下的质心定位误差为0.001~0.025像素;实际测试中,ROI预测算法可满足加速度8.75 m/s^(2)以内的测量场景需求,10~30 m测量距离内的光斑重复性定位误差稳定在0.016~0.040像素,优于传统方法;光斑提取速度提升约75.5%,显著增强了系统的实时处理能力。本研究可为合作靶标的测量应用提供有效技术保障。展开更多
文摘Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify interframe forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金National Key Research and Development Program of China(No.2017YFB1304001)。
文摘During the sizing process,yarn congestion fault occurs at the reed teeth of a sizing machine.At present,the yarn congestion fault is generally handled by manual detection.The sizing production line operates on a large scale and runs continuously.Untimely handling of the yarn congestion fault causes a large amount of yarn waste.In this research,a machine vision-based algorithm for yarn congestion fault detection is developed.Through the analysis of the congestion fault and interference contour characteristics,the basic idea of image phase subtraction to identify the congestion fault is determined.To address the interference information appearing after image phase subtraction,the image pre-processing methods of Canny edge extraction and mean filtering are employed.According to the fault size and location characteristics,the fault contour detection algorithm based on inter-frame difference is designed.To mitigate the camera vibration interference,the anti-vibration interference algorithm based on affine transformation is studied,and the fault detection algorithm for the total yarn congestion fault is determined.The detection of 20 sets of field data is carried out,and the detection rate reaches 90%.This fault detection algorithm realizes the automatic detection of yarn congestion fault of sizing machine with certain real-time performance and accuracy.
文摘针对合作靶标特征点成像在大跨度动态测量中的特征提取精度下降、实时性不足问题,提出了一种融合帧间运动预测与改进亚像素边缘检测的自适应光斑质心提取方法。基于合作靶标测量运动连贯性特性,构建了动态感兴趣区域(Region of Interest,ROI)特征参数模型,以帧间运动预测实现ROI的快速定位,结合大律法阈值优化策略实现自适应Canny边缘检测,在提升计算效率的同时有效解决了不同测量距离下的降噪问题。然后,采用多方向Sobel算子与强度斜坡改进的Zernike矩相结合改进了边缘点定位算法,并基于高斯牛顿迭代改进鲁棒最小二乘圆拟合法,实现质心坐标计算。实验结果表明:在仿真测试中,本方法在不同噪声水平下的质心定位误差为0.001~0.025像素;实际测试中,ROI预测算法可满足加速度8.75 m/s^(2)以内的测量场景需求,10~30 m测量距离内的光斑重复性定位误差稳定在0.016~0.040像素,优于传统方法;光斑提取速度提升约75.5%,显著增强了系统的实时处理能力。本研究可为合作靶标的测量应用提供有效技术保障。
