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Computer Vision Metrics: Appendix A

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Synthetic Feature Analysis

This appendix provides analysis of several common detectors against the synthetic feature alphabets described in Chapter 7. The complete source code, shell scripts, and the alphabet image sets are available from Springer Apress at:

Figure A-1. Example analysis results from Test #4 below, (left) annotated image showing detector locations, (center) count of each alphabet feature detected, shown as a 2D shaded histogram, (right) set of 2D shaded histograms for rotated image sets showing all 10 detectors

This appendix contains:

  • Background on the analysis, methodology, goals, and expectations.
  • Synthetic alphabet ground truth image summary.
  • List of detector parameters used for standard OpenCV methods: SIFT, SURF, BRISK, FAST, HARRIS, GFFT, MSER, ORB, STAR, SIMPLEBLOB. Note: No feature descriptors are computed or used, only the detector portions of BRISK, SURF, SIFT, ORB, and STAR are used in the analysis.
  • Test 1: Interest point alphabets.
  • Test 2: Corner point alphabets.
  • Test 3: Synthetic alphabet overlays onto real images.
  • Test 4: Rotational invariance of detectors against synthetic alphabets.

Background Goals and Expectations

The main goals for the analysis are:

  • To develop some simple intuition about human vs. machine detection of interest point and corner detectors, to observe detector behavior on the synthetic alphabets, and to develop some understanding of the problems involved in designing and tuning feature detectors.
  • To measure detector anomalies among white, black, and gray versions of the alphabets. A human would recognize the same pattern easily whether or not the background and foreground are changed; however, detector design and parameter settings influence detector invariance to background and foreground polarity.
  • To measure detector sensitivity to slight pixel interpolation artifacts under rotation.

Note: Experienced practitioners with well-developed intuition regarding capabilities of interest point and corner detector methods may not find any surprises in this analysis.

The analysis uses several well-known detector methods as implemented in the OpenCV library; see Table A-1. The analysis provides...