1. Detecting dangerous items in an X-Ray is an application of computer vision O True O False
Added by Eric S.
Close
Step 1
Step 1: Computer vision is a field of artificial intelligence that enables computers to "see" and interpret images and videos. Show more…
Show all steps
Your feedback will help us improve your experience
Sri K and 77 other AP CS educators are ready to help you.
Ask a new question
Labs
Want to see this concept in action?
Explore this concept interactively to see how it behaves as you change inputs.
Key Concepts
Recommended Videos
Question: Depth perception within a microscopic sample is possible. True or False
Sri K.
Medical imaging reconstruction:In this question, you will consider an example resembles medical imaging reconstruction in MRI. We begin with a true image image of dimension 50 × 50 (i.e., there are 2500 pixels in total). Data is cs.mat; you can plot it first. This image is truly sparse, in the sense that 2084 of its pixels have a value of 0, while 416 pixels have a value of 1. You can think of this image as a toy version of an MRI image that we are interested in collecting. Because of the nature of the machine that collects the MRI image, it takes a long time to measure each pixel value individually, but it's faster to measure a linear combination of pixel values. We measure n = 1300 linear combinations, with the weights in the linear combination being random, in fact, independently distributed as N (0, 1). Because the machine is not perfect, we don't get to observe this directly, but we observe a noisy version. These measurements are given by the entries of the vector y = Ax + n, where y ∈ R 1300 , A ∈ R 1300×2500, and n ∼ N (0, 25 × I1300) where In denotes the identity matrix of size n × n. In this homework, you can generate the data y using this model. Now the question is: can we model y as a linear combination of the columns of x to recover some coefficient vector that is close to the image? Roughly speaking, the answer is yes. Key points here: although the number of measurements n = 1300 is smaller than the dimension p = 2500, the true image is sparse. Thus we can recover the sparse image using few measurements exploiting its structure. This is the idea behind the field of compressed sensing. The image recovery can be done using lasso min x ky − Axk 2 2 + λkxk1. (a) Now use lasso to recover the image and select λ using 10-fold cross-validation. Plot the cross-validation error curves, and show the recovered image. (b) To compare, also use ridge regression to recover the image: min x ky − Axk 2 2 + λkxk 2 2 . Select λ using 10-fold cross-validation. Plot the cross-validation error curves, and show the recovered image. Which approaches give a better recovered image?
Supreeta N.
Adi S.
Recommended Textbooks
Computer Science and Information Technology
Introduction to Programming Using Python
Computer Science - An Overview
Transcript
Watch the video solution with this free unlock.
EMAIL
PASSWORD