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

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

Video

Seeing an Image

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

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

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

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

Thresholding Theory Kernel Convolution http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/kernelmaskoperation/ 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.

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

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

Binary Results Binary Average of Original Frame 18 RAW Frame 18

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

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

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

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

Uniformity

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

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

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

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