1. Image Processing Methods Using MATLAB
Beth Bogart and Andrew Sevigny
Tuesday October 20, 2015

2. Overview Test Video: what we’ve been processing Processing Methods
Image Averaging
Binary Images Using Thresholding
Next Steps B

3. Video

4. Seeing an Image

5. Averaging a Set of Images
Create an Empty Array for the New Image
Define the Image Range: Which Frames to Average
Iterate Though Each Pixel
Determine the avg. pixel value across frames
Store the average value in the new image array
Display the new image
Noise reduction, smoothing, helps remove pixels that may affect further processing

6. Averaging a Set of Images: Before
Frame 18
Frame 22
BEFORE
Noise reduction, smoothing, helps remove pixels that may affect further processing
Frame 26
Frame 29

7. Averaging a Set of Images: After
Noise reduction, smoothing, helps remove pixels that may affect further processing

8. Benefits of Averaging BEFORE AFTER
Noise reduction, smoothing, helps remove pixels that may affect further processing

9. Thresholding Theory Kernel Convolution
Theory behind kernel convolution:
Uses a box convolution kernel, of an odd number of pixels (3x3, 5x5, etc), that assigns weighting to each of the outer pixels around the target pixel and depending on what you’re trying to do (sharpen or smooth) will take the average of those surrounding pixels and re-assigns the target pixel the average value, which will allow the hue of the image to be reassigned. Goes along for every single pixel per frame, and output is the completely averaged/summed pixels.

10. Our Thresholding Technique
Similar theory as video
Compares target pixel to averaged pixels
Old Image
New Image
Comparing average to threshold value and assigning the target pixel to a one or zero corresponding to the threshold, and will turn the “defects”, in our case, yellow and all other values within the threshold blue
anyting cooling differently to the pixels around it will be brought out by the code
We can change the kernel size as well as the threshold values in order to mix and match to find out what the best outcome is (least amount of noise, most amount of actual data text: clearly read the text)
Value
TValue
AVG
Find Average of Kernel

11. Our Thresholding Technique
Set a Threshold for Error
Choose an image to process
Create an empty array for the new image
Iterate through every pixel (starts w/in boundaries)
Define a kernel of surrounding pixels
Take average of pixels within a set kernel
Is the pixel within the threshold value?
Yes: Set binary image pixel to 1
No: Set binary image pixel to 0
Display the new image
Noise reduction, smoothing, helps remove pixels that may affect further processing

12. Binary Results
Binary Average of Original Frame 18
RAW Frame 18

13. Binary Results: Using Averages
Binary Average of Original Frame 18
Binary Average of Average Frame (Frames 18-29)

14. Binary Results: Changing the Kernel
9x9
7x7
5x5

15. Binary Results: Changing the Threshold
X = 0.04
X = 0.05
X = 0.06
X = the amount of error over the average value from the original pixel value

16. System Integration Recorded videos need to be short
Video timed with the flash
Faster data processing
Removing unnecessary frames
Uniformity: lighting and camera angle
Talk about the time of each video
talk about the duration of flash/cooling
image needs uniformity in lighting and camera angle

17. Uniformity

18. Looking ahead: applying derivatives
Applying derivative techniques
Graphing pixel values over time (focused areas)
Taking 2nd derivative of each pixel’s function
Creating binary image
Graph 1
X = time on natural log scale
Y = change in temperature on natural log scale
Graph 2
Y = change in the rate of temperature with respect to time, nat log scale
Graph 3
X = time on a log scale
Y = change in the change in rate of temperature with respect to time
spiedigitallibrary.org/data/Journals/OPTICE/21928/051008_1.pdf

19. Looking ahead: applying derivatives
spiedigitallibrary.org/data/Journals/OPTICE/21928/051008_1.pdf

20. Looking ahead (cont.) Averaging out image noise Depends on output
Automation
Integrating MATLAB into our system

21. What Have You Seen? Issues with our current system Processing Methods
Image Averaging
Binary Images Using Thresholding
Issues with our current system
Thermographic Derivatives