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

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

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*22m=10150 fault testing conditions

nonwoven fabric of interest nominal image for the nonwoven fabric

5. Guide to spatiotemporal image monitoring

6. Conclusions

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

7. Reference