(Sample) Final Exam with brief answers

Transcription

1 Name: Perm #: (Sample) Final Exam with brief answers CS/ECE 181B Intro to Computer Vision March 24, 2017 noon 3:00 pm This is a closed-book test. There are also a few pages of equations, etc. included at the beginning for your reference. Be sure to read each question carefully and provide all the information requested. If the question asks you to explain, do so! Show your work. Write your answers in the spaces provided and, if necessary, on the back of the page. If you use the back, draw an arrow or write SEE BACK to make sure the graders don t miss it. If you need more space, attach extra sheets of paper (available at the front). Watch the clock don t spend too much time on one problem. Don t leave questions blank! Exams must be turned in by 3:00pm sharp. Good luck!

2 CS/ECE 181B Final Exam 2 (Sample) Final Exam Questions Note: The length of this sample exam is not necessarily the length of the real exam! 1. [2 points] What is a fundamental difference between the field of computer vision and the field of image processing? Image processing typically produces an image (or information about an image) as its output, whereas computer vision typically produces more semantic (higher-level) information about the content of the image. 2. [4 points] A remote sensing device may produce images with several bands (or planes or layers ) e.g., the Landsat satellite produces a seven-band image. (a) Briefly, what is the fundamental difference between the bands? (b) In general, how are they different from the three bands produced by an RGB color camera? (a) The range of wavelengths each band senses is different. (b) The range and shape of the wavelength sensitivity functions can be quite different 3. [2 points] The RGB values of two different image points in the same image (perhaps far apart in the image) are both (192, 216, 108). What, if anything, can you say for certain about (a) the colors of the two surfaces imaged at these locations, and (b) the lightness (or albedo) of the two surfaces? (a) Not much, since the values (a function of the irradiance) are essentially a product of two things: the illumination and the surface reflection function. Knowing the product doesn t tell you much about the component factors, especially at a single point. (b) Same answer.

3 CS/ECE 181B Final Exam 3 4. [3 points] Describe non-maximum suppression in computer vision problems. Give an example of when it might be used. In a region of a (typically processed) image, keep the highest/maximum values of that region and suppress (to zero) the other values. This is often used after edge detection to thin the output to a more usable output. 5. [4 points] [Show your calculations.] (a) What is the value of the correlation between patch H and patch F? (This is a single scalar value (1x1), the result when H and F completely overlap.) H: F: (b) What is the value (again, a single scalar) of the normalized correlation between patch H and patch F? 27/(sqrt(13)*sqrt(81)) = [3 points] List two reasons why the correspondence problem in stereo vision is very challenging. A given point looks different from different camera directions (due to the surface reflectance function). Occlusion.

4 CS/ECE 181B Final Exam 4 7. [4 points] List two advantages and two disadvantages of multiple camera stereo (with respect to twocamera stereo). + Multiple depth estimates can improve overall depth outputs. + Covers a wider range of the scene than a single pair. - Many more correspondence matches to make, which become harder for cameras that are farther apart. - More computation. 8. [4 points] Segmentation attempts to assign the same label to pixels that belong together. List four criteria of sameness that might be used for segmentation i.e., pixels may be grouped together if they have similar what? Color, depth, texture, brightness, motion 9. [8 points] Your grayscale camera views light objects on a dark conveyor belt, and you need to write a program to segment the objects, producing a binary image. Unfortunately, the light level is changing constantly, because of other things going on in the factory. Describe an algorithm to reliably segment the objects from the background in every video frame. This could be approached several ways more interested in coming up with a well-thought-out answer than one specific correct answer. One possible approach: For each image, create a grayscale histogram, find a good threshold point between the (presumably) lower-valued maximum and the higher-valued maximum, and threshold the image for the higher values. This should give you a noisy binary image showing (mostly) the light objects for each image, despite the changing light levels. In the second step (to produce segmented objects), you can use a connected components method (defined by 8-connected neighborhoods) or perhaps an active contour for each blob in the image.

5 CS/ECE 181B Final Exam [5 points] In the figure below showing a stereo camera setup, with cameras O 1 and O 2, draw and identify the baseline, the epipoles, the epipolar plane, and the epipolar line corresponding to the image point p. P p O1 O2 See the diagrams from the 3/13 lecture notes. 11. [4 points] The optical flow at a particular image point is a vector pointing at 120 degrees from the vertical, as shown below. Draw the range of possible 2D object motions that generated this optical flow value. v v u u This was only quickly mentioned in class, but since the optical flow only measures motion perpendicular to the image gradient (there s a little ambiguity in the language here, but this can be viewed as perpendicular to the constant image gradient contour, or in the same direction as a maximum image gradient see the diagram on slide 14 of the 3/15 lecture notes). The actual 2D object motion can be represented as any vector from the origin to the dotted line shown in the figure to the right. Slide 11 from the 3/15 lecture notes shows an example of this. The pattern underneath the hole could be moving in any of those directions and an observer could not tell the different between them.

6 CS/ECE 181B Final Exam [10 points] Your job is to build a computer vision system to track a news anchor s face based on skin color. Given a video scene with one person in it who is facing the camera where the person s face takes up about 20% of the image, but you don t initially know exactly where you need to output the (row, col) location of the center of the face. There may be hands in the images sometimes, but at the bottom of the screen, never overlapping the face. Describe a complete approach to accomplish this task, including any preparation, training, or testing that would be necessary before running the program. Clearly state the program s inputs and outputs. What might cause this algorithm to fail? [Continue on the back of this page if necessary] Like problem 8, this could be approached in different ways. E.g., train manually on images of the news anchor s face (perhaps click on various locations to collect skin pixel values, then represent the range of values in one of the ways shown on slide 9 of 3/8 lecture notes preferably the covariance matrix for the Mahalanobis distance). Then, for every image in the live video, threshold based on Mahalanobis distance to get face pixels. Find the centroid of all such pixels and call that the face center for the frame. This is way too simple, but it s a start.