Bookmark and Share

Computer Vision Metrics: Appendix A (Part B)

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

For Part A of Appendix A, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.

Test 2: Synthetic Corner Point Alphabet Detection

Table A-4 provides the total detected synthetic corner points at all pyramid levels; some detectors do not use pyramids. Note: for detectors that report features separately over image pyramid levels, individual pyramid-level detections are shown in Table A-5.

Table A-4. Summary Count of Detected Features Found in the Synthetic Interest Point Alphabet, 0 degree Rotation

Table A-5. Octave Count of Detected Features Found in the Synthetic Corner Point Alphabet, 0 degree Rotation

Each feature exists within a 14x14 pixel region, and the total number of features detected in each cell is provided in summary tables with the annotated images. Note that several features may be detected within each 14 x 14 cell, and the detectors often provide non-repeatable results, which are discussed at the end of this appendix.

Annotated Synthetic Corner Point Detector Results

Test 2 is exactly like the interest point detector results in Test 1. As such, for ORB and SURF detectors, the annotated renderings using the drawkeypoints( ) function are too dense to be useful, but are included in the online test results.

The diameter of the circle drawn at each detected keypoint corresponds to the “diameter of the meaningful keypoint neighborhood,” according to the OpenCV KeyPoint class definition, which varies in size according to the image pyramid level where the feature was detected. Some detectors do not use a pyramid, so the diameter is always the same. The position of the detected features is normalized to the full resolution image, and all detected keypoints are drawn.

Entire Images Available Online

To better understand the detector results for each test, the entire image should be viewed to see the anomalies, such as where detectors fail to recognize identical patterns. Test results shown in Figures A-16 through A-25 only show a portion of the images.