This assignment investigates different methods of edge detection including unsharp masking, Sobel operators, and Laplacian of Gauss.

You will need to install opencv 3.0. Then just run make.

./visual <input_image>

ESC - exits the program

SPACE - reverts back to the original image

- - Gets the image negative

= - Equalizes the image

t - Convert the image into a binary image using the threshold specified by the slider

b - Automatically converts the image into a binary image using the average pixel intensity as the cutoff

r - Performs a region detection on the image, assigning sepearate regions different grayscale colors

S - Saves the current displayed image as 'out.tif'

u - Applies the unsharp mask to enhace the image

s - Applies the Sobel operator, generating a gradient image

l - Laplacian of Gaussian 7x7 with sigma = 1.4, generates an edge image

L - Laplacian of Gaussian 11x11 with sigma = 5, generates an edge image h - Convert color image to HSI d - Perform forward DCT keeping 9 frequency components D - Perform forward DCT then backwards again, keeping 9 frequency components e - Perform forward DCT keeping 1 frequency component E - Perform forward DCT then backwards again, keeping 1 frequency component x - Do an ROI detection for lines X - Do an ROI detection for circles; note: takes ages Tab - If a linear ROI detection was run, show the Hough transform votes m - Dilate image M - Erode image g - Turn RGB image to GS . - Run image segmentation using morphological ops

1 - produces image M1 2 - produces image N1 3 - produces image N2 4 - produces image N3 5 - produces image N4 6 - produces T1 and prints the error between N1 and T1 7 - produces image K1 8 - produces dumb difference between first two frames 9 - produces dumb difference between second two frames [ - produces compensated difference between first two frames ] - produces compensated difference between second two frames