Spatiotemporal method for monitoring image data
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Transcript of Spatiotemporal method for monitoring image data
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
2. GLR spatiotemporal framework
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 ROIsonly one faultnot consider changes in
20,k kN ( , )
21,k kN ( , )
k
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*22m=10150 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