Spatiotemporal method for monitoring image data

1. Spatiotemporal method for monitoring image data 导师：聂斌 杜梦莹（11级硕）

2. 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

3. 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]

4. Components of metircs to evaluate : • Estimate the time of the shift • Determining the location of the fault • Identify the size of the fault

5. 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

6. nk is the the number of pixels in ROI k modification when taking over a window the past m images

7. 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)

8. 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

12. 5. Guide to spatiotemporal image monitoring

13. 6. Conclusions • the effect choice of window m • three-dimensional(3D) image-based systems • multiple faults detection and diagnosis

14. 7. Reference

15. Thanks for your attention!