The following is a table that shows the storage requirements of each data type and format:

Transcription

1 Name: Sayed Mehdi Sajjadi Mohammadabadi CS5320 A1 1. I worked with imshow in MATLAB. It can be used with many parameters. It can handle many file types automatically. So, I don t need to be worried about the file types. Maybe the most important parameter of this command is the range parameter. Because different image matrixes may have different data ranges and the image would not be displayed properly if an inappropriate range parameter set for this command. There is a trick that ensures that this command considers the entire range of the image. Instead of setting a range like [min max] we can simply pass [] as the range parameter. Imshow then automatically finds upper and lower range of the matrix to be displayed. I personally found it the most frequently parameter of this command. I also opened different image formats like jpg, gif, png and tiff using imread command. 2. The following is the output gray-level image: This image is obtained by sum of two images. In the first image, the brightness increases in row direction and in the second image the brightness increases in column direction. These images can be seen below: The following is a table that shows the storage requirements of each data type and format:

2 Storage board boardu boardi tiff print ASCII binary requiremnt uncompressed 432 bytes 434 bytes 753 bytes 87.9 KB 56.3 KB 3,600 bytes gzip 419 bytes 419 bytes 754 bytes 3,160 bytes 941 bytes 269 bytes compress 439 bytes 440 bytes 833 bytes 13.8 KB 6,282 bytes 1,065 bytes It can be seen that every data type and compression method has different storage requirements. It must be noted that the size of the color map in the indexed image is 64 (MATLAB default) and the saved file contains both of indexed image and map array. The other thing that must be noted is about tiff image. The print command saves extra borders of the figure with the image. This generally increases the size of the saved file. It can be observed that the binary image with gzip compression has the lowest size. Based on this table, we can see that the gzip is generally more effective than compress command. By looking at the first column of this table we can see that the initial double image has the lowest size compared to other data types. 3. I selected the brick image as input: The followings are the output images for k = 3, k = 5, k=7: k = 3 k = 5 k = 7

3 It can be obsereved that as the k increases the image gets more softened and more blurred. 4. The table below shows the gaussian coefficients for k = 2, sigma = 1.5: The following images show the gussian kernels for k=100 and sigma = 20 and 50 respectively: k = 100, sigma = 20 k = 100, sigma = Again, I selected the brick image as input. The followings are the output images for different values of k and sigma: k = 2, sigma = 1.5 k = 10, sigma = 1.5

4 k = 10, sigma = 3 k = 10, sigma = 5 It can be seen that as the sigma increases, the image will get more softened and blurred. In other words, if the sigma is a small value, the k value doesn t have very large effect on output image. If we compare these output images with the outputs of averaging filter in question 3, we observe that blurring using gaussian kernels won t produce the ringing effects found in outputs of averaging filter. 6. Again, I selected the bricks image as the input image. The following images are the spatial derivatives in x and y directions: derivative in x direction derivative in y direction 7. The following is the magnitude of the spatial derivatives of each pixel for two input images:

5 Input image 1 magnitude of spatial derivatives Input image 2 magnitude of spatial derivatives It can be seen that where there is an abrupt change in intensity of the image, the magnitude of spatial derivatives has a large value and can be seen in white color.