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Computer-Vision

Instructions to run driver (cv_driver):

  1. Open all the .m files and images into your Matlab workspace
  2. Open up cv_driver.m
  3. Run the file
  4. Outputs will be present

Includes many different computer visison techniques.

Quantization Effect: Part A: Create a function that quantizes any real number (x) with L levels. The function should input x, L, the minimum value, and the maximum value given below. Part B: Apply the quantization function to the image pic1.tif and reconstruct an image with 6 bits per pixel, and 3 bits per pixel.

Color Transformation: Reads an RGB image and: β€’ Displays each of the 3 RGB channels β€’ Transforms the RGB image into the YCrCb color space. β€’ Creates and Displays the brightness image β€’ Creates the chrominance images.

Sampling Effect: Part A: Resamples an image with dimension NxM to (N/n)x(M/m), known as decimation of dimension by nxm, where each nxm pixels are mapped to 1 pixel, by picking the first sample of nxm blocks. Part B: Resample an image with dimention NxM to (nN)x(mM), known as interpolation of dimensions by nxm, where each pixel is duplicated to nxm pixels.

Image Enhancement: Enhances an image using the power-law transformation, where new pixel value is: 𝑠=π΄π‘Ÿπ›Ύ. Where r is the old pixel value, Ξ³ is the image enhancement, and A is set to 1. Then create a single plot showing the mapping function between the old and new pixels.

Histogram Computation: Computes the histogram of racecar.tif and the 2 enhanced images from Image Enhancement.

Spatial Filtering: Convolution method to perform spatial filtering on an image. Apply an averaging filter with a kernel size of 9x9 to image racecar.tif.

Frequency Domain Filtering Frequency domain method to smooth racecar.tif with an ideal filter.

Gradient Base Edge Detection: Extract the gradient base edge magnitude and direction and smooth the image with a Gaussian Low Pass Filter. Computes the edge detection of the smooth image in the X-direction and Y-direction. Computes the magnitude, and the direction of the edge information and quantizes the direction information to 8 directions.

Sobel Edge Detection: Find sobel edge detection.

Edge Detection with Laplacian of Gaussian: Constructs two Laplacian of Gaussian filters

Template Matching: Finds the location of the letter β€œn” in the image β€œfamous.jpg”. Uses the image n_famous.jpg as a template and applies the template matching technique and the correlation operator as a measure. For Template matching my algorithm first gets sizes of the target image and template image. After taking the mean of each image a correlation is performed to get a correlation matrix. The algorithm then finds the row and column which corresponds to the correlation matrix max value. This process is repeated for the known number of β€œn”’s. Each loop the max value found is set to zero to prepare for a new max to be found in the next loop. After finding each letter β€œn” a box the size of the template image is drawn around the letter β€œn” found.

Harris Corner Detection: Implements a Harris Corner Detection algorithm. Finds the corners in the image β€œnoisytriangle.jpg”. Use π‘˜π‘˜ = 0.05 for the corner operation det(𝑀) βˆ’ π‘˜ (𝑀)^2. The algorithm starts with applying a Gaussian filter using a Prewitt gradient operator. An edge detection is then performed. After the edge detection the corner operation det(M) βˆ’ k (M)2 is performed where 𝑀= [π‘₯_𝑒𝑑𝑔𝑒() π‘šπ‘Žπ‘”(); π‘šπ‘Žπ‘”() 𝑦_𝑒𝑑𝑔𝑒()]; . The positions of the corners are found by finding which values in the corner operation matrix are above a certain threshold. The threshold was found manually to be 0.1βˆ—π‘π‘œπ‘Ÿπ‘›π‘’π‘Ÿ_π‘šπ‘Žπ‘‘π‘Ÿπ‘–π‘₯ . After getting all the positions of the corners I removed the corners that were detected on the outside edge of the image. The corners were then placed onto the original image. To calculate the coordinates of the 3 new corners of the inner triangle when the image is rotated by 10 degrees I put the non-rotated inner-corners into a rotation operation.

Hough Transform: Finds the 6 lines in the image β€œnoisytriangle.jpg” using the Hough transform. The algorithm starts by converting the image as a black and white canny edge image. A Hough Transform is the performed with rho steps set as 1. Using the size of the image rho was found to range from -704 : 704. I set theta as -90 and 90 degrees. This was the default values I found in Matlab’s Hough() function. A Standard Hough Transform was performed on the black & white image with rho and theta. After getting the image in Hough space the algorithm finds the peaks. To find the peaks I needed to set the number of peaks, the threshold (minimum value to be considered a peak) and the neighborhood size (2-element vector of positive odd integers). I had to set the peaks to 8 because I was having a problem with lines being drawn twice in the next step. Next I displayed the lines on the image. This was done by obtaining the theta and rho from the corresponding peak. Once the theta and rho where found I transformed the lines in x-y space. The lines were then drawn on the image. To finish the algorithm finds the position of the corners by finding where the lines intercept at the corner points.