1. Program Studi S-1 Teknik Informatika FMIPA Universitas Padjadjaran
VISI KOMPUTER D10K-7C02 Semester Ganjil CV09: Corner Detection (Interest Point Detection) Dr. Setiawan Hadi, M.Sc.CS.
Program Studi S-1 Teknik Informatika
FMIPA Universitas Padjadjaran

2. Perkuliahan dalam 2 Minggu yad
Topik
1 November 2016: Corner Detection dan Chaincode
8 November 2016: Object Recognition
15 November 2016: Stereo Vision

3. Overview Corner Detection
In general terminology : interest point detection
to extract certain kinds of features from an image
frequently used in
motion detection
image matching, tracking
image mosaicking
panorama stitching
3D modelling
object recognition.

4. Corner
the intersection of two or more edges (special case of interest points
In general, interest points could be:
Isolated points of local intensity maximum or minimum.
Line endings.
Points on a curve where the curvature is locally maximized

5. Why are interest points useful?
For establishing corresponding points between images.
panorama stitching
stereo matching
left camera
right camera

6. How could we find corresponding points?
( ) = ?
feature descriptor

7. Interest point detectors should be covariant
Features should be detected in corresponding locations despite geometric or photometric changes.

8. Interest point descriptors should be invariant
Should be similar despite geometric or photometric transformations
( ) = ?
feature descriptor

9. Invariance vs Covariance
Invariance: refers to the property of objects being left unchanged by symmetry operations.
Covariance: preserved by a change of coordinate system.

10. Invarian dan Kovarian

11. Example

12. Harris Corner Detection Basic Idea

13. Constraints

14. Concept Methods Contour based Intensity based
Extract contours and search for maximal curvature or inflexion points (i.e., curvature zero-crossing) along the contour.
Intensity based
Compute a measure that indicates the presence of an interest point (1) directly from gray (or color) values or (2) by first fitting a parametric model to the gray (or color) values.
Methods using parametric models can localize corners to sub-pixel accuracy but are more expensive.

15. Methods
Moravec Operator
Harris Operator
Others
See Wikipedia

16. Moravec Operator First operators for interest point detection
Developed by Hans P. Moravec in 1977 for his research involving the automatic navigation of a robot through a clustered environment.
Moravec defined the concept of “points of interest” in a image and concluded these interest points could be used to find matching regions in different images.

17. Harris Operator
Developed by Chris Harris and Mike Stephens in 1988 as a processing step to build interpretations of a robot’s environment based on image sequences.
Like Moravec, they needed a method to match corresponding points in consecutive image frames, but were interested in tracking both corners and edges between frames.
Improved upon Moravec’s corner detector by considering the differential of the corner score with respect to direction directly.
The Harris corner detector computes the locally averaged moment matrix computed from the image gradients, and then combines the Eigenvalues of the moment matrix to compute a corner measure, from which maximum values indicate corners positions

18. Harris Detector Examples

19. Harris Detector Examples

20. Harris Detector Examples

21. Harris Detector Examples

22. Harris Detector Examples

23. Tugas C# MATLAB File hasil dikirim ke email
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Cobalah program yang dipaparkan dalam artikel tersebut
Program memerlukan Accord.Net framework
MATLAB
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Pelajari dan cobalah kode yang ada. Gunakan objek yang berbeda
File hasil dikirim ke