## ECE 5554 / ECE 4554: Computer Vision Fall 2016## InstructionsDue date: 11:55pm on Monday, October 17th, 2016 Handin: through https:canvas.vt.edu Starter code and data: hw3.zip Deliverables: Please prepare your answer sheet using the filename of FirstName_LastName_HW3.pdf. Include all the code you implement in each problem. Remember to set relative paths so that we can run your code out of the box. Compress the code and answer sheet into FirstName_LastName_HW3.zip before uploading it to canvas.
## Part 1: Single-View Metrology (40 points)## A. Vanishing point detectionFor the New Classroom Building image, shown above, estimate the positions (in the image plane) of the three major orthogonal vanishing points (VPs), corresponding to the building orientations. Use at least three manually selected lines to solve for each vanishing point. The included code getVanishingPoint_shell provides an interface for selecting and drawing the lines, but the code for computing the vanishing point needs to be inserted. ## Write-upPlot the VPs and the lines used to estimate them on the image plane. Specify the VPs (u,v). Plot the ground horizon line and specify its parameters in the form au + bv + c = 0. Normalize the parameters so that: a^2 + b^2 = 1.
## B. Intrinsic parameter calibrationUse the orthogonality constraints of vanishing points to estimate the camera focal length (f) and optical center (u0, v0). ## Write-upShow the process of finding camera focal length and optical center Report the estimated camera focal length (f) and optical center (u0, v0).
## C. Extrinsic parameter calibration## Write-upDescribe how to compute the camera’s rotation matrix when provided with vanishing points in the X, Y, and Z directions.
compute the rotation matrix for this image, setting the vertical vanishing point as the Y-direction, the right-most vanishing point as the X-direction, and the left-most vanishing point as the Z-direction.
## D. Single-view metrologyEstimate the horizon and draw/plot it on the image. Assume that the sign is 1.65 meter. Estimate the heights of the tractor, the building, and the camera (in meters). This can be done with powerpoint, paper and a ruler, or Matlab. ## Write-upTurn in an illustration that shows the horizon line, and the lines and measurements used to estimate the heights of the building, tractor, and camera. Report the estimated heights of the building, tractor, and camera in meters.
## Rubric**10 points**for vanishing point detection.**10 points**for intrinsic parameter calibration.**10 points**for extrinsic parameter calibration.**10 points**for single-view metrology.
## Part 2: Epipolar Geometry (30 points)## OverviewIn this problem, you will implement an algorithm for automatically estimating the fundamental matrix F using RANSAC and the 8-point algorithm. In the file matches.mat, we provide the detected Harris corners row-column positions in variables r1 c1 for the first image; variables r2 c2 for the second image; and the corresponding matched pairs in the variable matches. The outline of the normalized 8-point algorithm: In this problem, you don't need to normalize and denomalize the image coordinates (i.e. ignore step 1 and step 3). You can earn extra points by implmenting the normalized version of the 8-point algorithm. ## Write-upDescribe what test you used for deciding inlier vs. outlier. Display the estimated fundamental matrix F after normalizing to unit length Plot the outlier keypoints with green dots on top of the first image plot(x, y, '.g'); Randomly select 7 sets of matching points. Plot the corresponding epipolar lines ('g’) and the points (with 'r+’) on each image. Show the two images (with plotted points and lines) next to each other.
## HintThe provided function plotmatches.m (e.g. plotmatches(im1,im2,[c1 r1]’,[c2 r2]’,matches’)) can be used to plot matches for debugging. We suggest that you manually select a set of good matches and getting it working with those matches first. Then, you can get it working using RANSAC. If you get it working with manual selection but not with RANSAC, you will get half credit. Your epipolar lines should pass very near (e.g., within 1 pixel) your plotted points. Do not use MATLAB computer vision toolbox for RANSAC or for estimating the fundamental matrix. Useful functions randsample(): for randomly picking integers from a range. Useful for RANSAC.
## Rubric**5 points**for describing the test used for deciding inlier vs. outlier**10 points**for reporting the estimated fundamental matrix**5 points**for plotting the inliers and outliers on top of the first image**10 points**for displaying the epipolar lines and matched points.
## Part 3: Affine Structure from Motion (30 points)This problem continues the interest point detection and tracking problem from HW2. Now, you will recover a 3D pointcloud from the image sequence hotel.seq0.png … hotel.seq50.png. You are encouraged to use your results from HW2, but in case you were not able to complete it, we have also included pre- computed intermediate results in the supplemental material. Submit your code so that we can reproduce your results. The outline of the affine structure from motion algorithm: ## Write-upInclude pseudocode in your report. Plot the predicted 3D locations of the tracked points for 3 different viewpoints. Choose the viewpoints so that the 3D structure is clearly visible. Plot the predicted 3D path of the cameras. The camera position for each frame is given by the cross product a_k = a_i x a_j. Normalize a_k to be unit length for consistent results. Give 3 plots, one for each dimension of a_k We provide the function plotSfM.m for visualizing the recovered 3D shape and camera positions in each frame.
## HintDo not use existing structure from motion code, such as found in OpenCV. The provided file tracks.mat contains the tracked keypoints over 51 frames. Useful functions svd: for computing singular value decomposition chol: for computing Cholesky factorization plot3: for displaying 3D points
Reference: Tomasi and Kanade. Shape and Motion from Image Streams under Orthography: a Factorization Method. 1992
## Rubric**5 points**for the pseudocode**15 points**for the correct implementation of affine structure from motion**5 points**for displaying the recovered 3D shapes under 3 different viewpoints.**5 points**for displaying the recovered camera motion.
## Extra credit / graduate credit (max possible 40 points extra credit)## Camera calibration**up to 20 points**: Use RANSAC to automatically detect three orthogonal vanishing points.
[VP, lineLabel] = vpDetectionFromLines(lines); This function takes a set of line segments and uses RANSAC to detect three vanishing points that correspond to three orthogonal directions in the scene. The main steps are (1) randomly select a set of edge pairs, compute their intersections as vanishing points hypothesis; (2) check the consistency of these vanishing points hypothesis; (3) clustering the line segments into different groups corresponding to vanishing points.
For (3), you may use the clustering code we included. lineLabel = clusterLineSeg(PrefMat); This function takes a binary matrix PrefMat of size [NumLines x NumVP] where PrefMat(i, j) = 1 indicates line segment i is compatible with vanishing point j (i.e., i-th line is an inlier of j-th vp). Input: lines: a matrix of size [NumLines x 5] where each row represents a line segment with (x1, y1, x2, y2, lineLength)
Output: VP: [2 x 3] each column corresponds to a vanishing point in the order of X, Y, Z lineLabel: [NumLine x 3] each column is a logical vector indicating which line segments correspond to the vanishing point.
Your algorithm should be able to get results similar to the following. First row, original image. Second row, the line segments grouped into three orthogonal vanishing points. Third row, the vanishing points and the position of the ground plane. ## Write-upShow three results where you can automatically detect three vanishing points. ## Epipolar Geometry**up to 20 points**: improve your results using the**normalized**8-point algorithm with RANSAC (i.e. adding the step 1 and step 3). Apply the transformation so that the points have zero mean and their averaged squared distance to the origin is 2. For the inlier matches, report the averaged value of (x’^T F x)^2 (i.e. evaluating how well the recovered fundamental matrix F explains the inlier matches). Compare this value with the un-normalized 8-point algorithm.
## Affine structure from motion**up to 20 points**: Missing track completion. Some keypoints will fall out of frame, or come into frame throughout the sequence. Use the matrix factorization to fill in the missing data and visualize the predicted positions of points that aren't visible in a particular frame.
In your answer sheet, describe the extra points under a separate heading. ## AcknowledgementsThis homework is adapted from the projects developed by Derek Hoiem (UIUC) |