Skip to Main Content
Learning OpenCV
book

Learning OpenCV

by Gary Bradski, Adrian Kaehler
September 2008
Beginner to intermediate content levelBeginner to intermediate
580 pages
20h 7m
English
O'Reilly Media, Inc.
Content preview from Learning OpenCV

Exercises

There are sample code routines in the …/opencv/samples/c/ directory that demonstrate many of the algorithms discussed in this chapter:

  • lkdemo.c (optical flow)

  • camshiftdemo.c (mean-shift tracking of colored regions)

  • motempl.c (motion template)

  • kalman.c (Kalman filter)

  1. The covariance Hessian matrix used in cvGoodFeaturesToTrack() is computed over some square region in the image set by block_size in that function.

    1. Conceptually, what happens when block size increases? Do we get more or fewer "good features"? Why?

    2. Dig into the lkdemo.c code, search for cvGoodFeaturesToTrack(), and try playing with the block_size to see the difference.

  2. Refer to Figure 10-2 and consider the function that implements subpixel corner finding, cvFindCornerSubPix().

    1. What would happen if, in Figure 10-2, the checkerboard were twisted so that the straight dark-light lines formed curves that met in a point? Would subpixel corner finding still work? Explain.

    2. If you expand the window size around the twisted checkerboard's corner point (after expanding the win and zero_zone parameters), does subpixel corner finding become more accurate or does it rather begin to diverge? Explain your answer.

  3. Optical flow

    1. Describe an object that would be better tracked by block matching than by Lucas-Kanade optical flow.

    2. Describe an object that would be better tracked by Lucas-Kanade optical flow than by block matching.

  4. Compile lkdemo.c. Attach a web camera (or use a previously captured sequence of a textured moving object). In running ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Learning OpenCV 3

Learning OpenCV 3

Adrian Kaehler, Gary Bradski
Learning OpenCV, 2nd Edition

Learning OpenCV, 2nd Edition

Adrian Kaehler, Gary Bradski
Practical OpenCV

Practical OpenCV

Samarth Brahmbhatt
Machine Learning for OpenCV

Machine Learning for OpenCV

Michael Beyeler, Michael Beyeler (USD)

Publisher Resources

ISBN: 9780596516130Supplemental ContentErrata Page