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Spatiotemporal method for monitoring image data. 导师:聂斌 杜梦莹( 11 级硕). CONTENT 1. Introduction 2. GLR spatiotemporal framework 3.Metrics used for evaluating the proposed GLR s patiotemporal method 4 . Simulation results 5 . Guide to spatiotemporal image monitoring 6 . Conclusions
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Spatiotemporal method for monitoring image data 导师:聂斌 杜梦莹(11级硕)
CONTENT • 1. Introduction • 2. GLR spatiotemporal framework • 3.Metrics used for evaluating the proposed GLR spatiotemporal method • 4. Simulation results • 5. Guide to spatiotemporal image monitoring • 6. Conclusions • 7. Reference
1. Introduction • Machine Vision Systems(MVS) • Uniformity or a specific pattern • Grayscale images • Include both the spatial and the temporal aspects • The constant total amount of data between images • The identification of a fault’s location within the image and estimated time assists practitioners in process recovery Image data: f(x,y), where x and y represent the spatial coordinates Values: intensity [0, 255]
Components of metircs to evaluate : • Estimate the time of the shift • Determining the location of the fault • Identify the size of the fault
absence of a process shift: affected ROI intensities: Implications on the ROIs: some ROIs not capture the fault; defects can be partially captured by one or more ROIs only one fault not consider changes in 2. GLR spatiotemporal framework
nk is the the number of pixels in ROI k modification when taking over a window the past m images
3. Metrics used for evaluating the GLR spatiotemporal method • two steps: • detect the occurrence of a process shift • provide good estimates of all three spatiotemporal metrics steady-state median run length(SSMRL) dice similarity coefficient(DSC)
4. Simulation results the smallest ROI size: a square of are 22*22 m=10 150 fault testing conditions nonwoven fabric of interest nominal image for the nonwoven fabric
6. Conclusions • the effect choice of window m • three-dimensional(3D) image-based systems • multiple faults detection and diagnosis