hw6_2011_05_16_01_solutions

hw6_2011_05_16_01_solutions - EE263 S. Lall 2011.05.16.01...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: EE263 S. Lall 2011.05.16.01 Homework 6 Due Thursday 5/19 1. Image reconstruction from line integrals. 46040 In this problem we explore a simple version of a tomography problem. We consider a square region, which we divide into an n n array of square pixels, as shown below. x 1 x 2 x n x n +1 x 2 n x n 2 The pixels are indexed column first, by a single index i ranging from 1 to n 2 , as shown above. We are interested in some physical property such as density (say) which varies over the region. To simplify things, well assume that the density is constant inside each pixel, and we denote by x i the density in pixel i , i = 1 , . . . , n 2 . Thus, x R n 2 is a vector that describes the density across the rectangular array of pixels. The problem is to estimate the vector of densities x , from a set of sensor measurements that we now describe. Each sensor measurement is a line integral of the density over a line L . In addition, each measurement is corrupted by a (small) noise term. In other words, the sensor measurement for line L is given by n 2 summationdisplay i =1 l i x i + v, where l i is the length of the intersection of line L with pixel i (or zero if they dont intersect), and v is a (small) measurement noise. This is illustrated below for a problem with n = 3. In this example, we have l 1 = l 6 = l 8 = l 9 = 0. x 1 x 2 x 3 x 4 x 5 x 6 x 8 x 9 l 2 l 3 l 4 l 5 l 7 line L Now suppose we have N line integral measurements, associated with lines L 1 , . . . , L N . From these measurements, we want to estimate the vector of densities x . The lines are characterized by the intersection lengths l ij , i = 1 , . . . , n 2 , j = 1 , . . . , N, 1 EE263 S. Lall 2011.05.16.01 where l ij gives the length of the intersection of line L j with pixel i . Then, the whole set of measurements forms a vector y R N whose elements are given by y j = n 2 summationdisplay i =1 l ij x i + v j , j = 1 , . . . , N. And now the problem: you will reconstruct the pixel densities x from the line integral measurements y . The class webpage contains the M-file tomodata.m , which you should download and run in Matlab. It creates the following variables: N , the number of measurements ( N ), n_pixels , the side length in pixels of the square region ( n ), y , a vector with the line integrals y j , j = 1 , . . . , N , lines_d , a vector containing the displacement d j , j = 1 , . . . , N , (distance from the center of the region in pixels lengths) of each line, and lines_theta , a vector containing the angles j , j = 1 , . . . , N , of each line. The file tmeasure.m , on the same webpage, shows how the measurements were computed, in case youre curious. You should take a look, but you dont need to understand it to solve the problem. We also provide the function line_pixel_length.m on the webpage, which you do need to use in order to solve the problem. This function computes the pixel intersection lengths for a given line. That is, given d...
View Full Document

Page1 / 17

hw6_2011_05_16_01_solutions - EE263 S. Lall 2011.05.16.01...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online