SYNTHESIS OF INTEREST POINT DETECTORS THROUGH GENETIC PROGRAMMING

1. SYNTHESIS OF INTEREST POINT DETECTORS THROUGH GENETIC PROGRAMMING Leonardo Trujillo Gustavo Olague EvoVisión Project, Computer Science Department, Applied Physics Division, CICESE Ensenada B.C. México

2. THIS WORK FULLFILS 6 OF 8 CRITERIA FOR HUMAN COMPETITIVENESS • (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. • (C) The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. • (D) The result is publishable in its own right as a new scientific result ¾ independent of the fact that the result was mechanically created. • (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. • (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. • (G) The result solves a problem of indisputable difficulty in its field.

3. The Problem • The CV problem addressed in this work is Interest Point Detection. • IP detection is one of the principal low-level feature extraction techniques used by modern CV systems. • IP detection corresponds with the commonly accepted model for early vision proposed by Marr in 1986 [2], and used widely in CV applications. 2. Marr, D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman: San Francisco.

4. The Problem

5. Stereo correspondence. Wide Range of Applications

6. Wide Range of Applications • Image Indexing. 3. C. Schmid and R. Mohr. Local Greyvalue Invariants for Image Retrieval. IEEETrans. on Pattern. Analysis and Machine Intelligence, 19(5):530--535, May 1997.

7. Wide Range of Applications • Object detection and recognition. 4. D. G. Lowe. Object recognition from local scale-invariant features. In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pages 1150.1157, 1999.

8. Our Approach • IP detection is posed as an optimization problem, and GP is used to synthesis IP detectors. • Well established and desirable properties in IP detection, such as geometric stability and global separability, are promoted through an adequate fitness function. • The geometric stability of learned IP detectors as well as their global separability are considered, through the use of the detectors repeatability rate (Schmid et al. 2000) and the entropy related with the point distribution across the image as part of the fitness function.

9. Repeatability Rate;Performance Metric for IP Detectors • This measure quantifies the geometric stability of detected points. • A high repeatability rate ensures that point detection is invariant to condition changes during image acquisition.

10. Repeatability Rate;Performance Metric for IP Detectors • INRIA Rhone Alpes, • University of Oxford, • Katholieke Universiteit Leuven • Center for Machine Perception at the Czech Technical University. 5. C. Schmid, R. Mohr and C. Bauckhage. Evaluation of interest point detectors. International Journal of Computer Vision, 37(2):151-172, 2000.

11. Results • Our approach produced two main results; two IP detectors that outperformed most, if not all, man made designs on well known image sequences. • These detectors were synthesized, with GP, using Gaussian derivatives and filters, as well as basic arithmetic and non linear operations in the GP process. • The two detectors are IPGP1 and IPGP2.

12. IPGP1

13. IPGP1

14. IPGP2

15. IPGP2

16. Evaluation on Rotation

17. Images - Illumination

18. HUMAN COMPETITIVENESS • The results obtained in this work fulfill six of the eight human competitive criteria. • Our results are directly comparable with other IP detectors because there is a widely accepted performance metric in the computer vision community; a performance metric that is maintained by some of the most prestigious research institutions in the field.

19. Performance Evaluation in Schmid et al. 2000, for Image Rotation Sequence

20. Additional IP Detectors • JOURNAL ARTICLES (B): • Kitchen and Rosenfeld. Gray-Level Corner Detection. Pattern Recognition Letters, 1:95-102, 1982. • H. Wang and M. Brady, Real-time corner detection algorithm for motion estimation. Image and Vision Computing, vol. 13, no. 9, pp. 695--703, November 1995. • Dreschler and Nagel. Volumetric Model and 3D trajectory of a moving car derived from monocular TV frame sequences of a street scene. Computer Graphics and Image Processing. 20:199-228, 1981. • CONFERENCE PAPERS (F): • P. R. Beaudet. Rotational invariant image operators. In Proc. IAPR 1978, pages 579-583, 1978.

21. Performance for Image Rotation Sequence

22. Performance for Image Rotation Sequence

23. Results - Rotation

24. HUMAN COMPETITIVENESS • (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. • C. Schmid, R. Mohr and C. Bauckhage. Evaluation of interest point detectors. International Journal of Computer Vision, 37(2):151-172, 2000. • H. Wang and M. Brady, Real-time corner detection algorithm for motion estimation. Image and Vision Computing, vol. 13, no. 9, pp. 695--703, November 1995. • F. Heitger, L. Rosenthaler, R. von der Heydt, E. Peterhans, and O. Kuebler,Simulation of neural contour mechanism: from simple to end-stopped cells, Vision Research, 32(5):963-981, 1992. • Foerstner. A feature based correspondence algorithm for image matching, International Archives of Photogrammetry and Remote Sensing. 26(3) pp. 150-166, 1986. • Kitchen and Rosenfeld. Gray-Level Corner Detection. Pattern Recognition Letters, 1:95-102, 1982. • Dreschler and Nagel. Volumetric Model and 3D trajectory of a moving car derived from monocular TV frame sequences of a street scene. Computer Graphics and Image Processing. 20:199-228, 1981.

25. HUMAN COMPETITIVENESS • (C) The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. • The Improved-Harris [3] detector is kept by the Visual Geometry Group of the Robotics Research Group, with participation by: • INRIA Rhone Alpes • University of Oxford • Katholieke Universiteit Leuven, and • Center for Machine Perception at the Czech Technical University

26. HUMAN COMPETITIVENESS

27. HUMAN COMPETITIVENESS • (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. • C. Schmid, R. Mohr and C. Bauckhage. Evaluation of interest point detectors. International Journal of Computer Vision, 37(2):151-172, 2000.

28. HUMAN COMPETITIVENESS • (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. • P. R. Beaudet. Rotational invariant image operators. In Proc. IAPR 1978, pages 579-583, 1978. • C. Harris and M. Stephens. A combined corner and edge detector. In Proc. Fourth Alvey Vision Conf., volume 15, pages 147-151, 1988. • Foerstner. A feature based correspondence algorithm for image matching, International Archives of Photogrammetry and Remote Sensing. 26(3) pp. 150-166, 1986.

29. HUMAN COMPETITIVENESS • (D) The result is publishable in its own right as a new scientific result ¾ independent of the fact that the result was mechanically created. • The work was accepted in the Computer Vision Track of one of the largest international conferences on pattern recognition, The International Conference on Pattern Recognition (ICPR) 2006. • Trujillo, L., Olague. G. Evolving Interest Point Detectors, to appear in, International Conference on Pattern Recognition (ICPR 2006), Hong Kong, China, August 20-24, 2006..

30. HUMAN COMPETITIVENESS

31. HUMAN COMPETITIVENESS • (G) The result solves a problem of indisputable difficulty in its field. • Research on feature extraction is still a hot topic on computer vision, and IP detection is still one of the main feature extraction techniques in the field. • Most conferences and computer vision journals devote a special section to feature extraction. In particular researchers are trying to propose new interest point detectors to solve all kind of machine vision applications. Our methodology opens a new avenue for research on feature extraction problems.

32. HUMAN COMPETITIVENESS

33. THIS WORK FULLFILS 6 OF 8 CRITERIA FOR HUMAN COMPETITIVENESS • (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. • (C) The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. • (D) The result is publishable in its own right as a new scientific result ¾ independent of the fact that the result was mechanically created. • (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. • (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. • (G) The result solves a problem of indisputable difficulty in its field.

34. WHY SHOULD THIS WORK WIN? • This work strengthens the link between two major research areas in computer science: Computer Vision & Evolutionary Computation. • This work establishes a new research avenue in the emerging field of Evolutionary Computer Vision, by developing a general procedure to synthesize feature extraction techniques.

35. SYNTHESIS OF INTEREST POINT DETECTORS THROUGH GENETIC PROGRAMMING Leonardo Trujillo Gustavo Olague EvoVisión Project, Computer Science Department, Applied Physics Division, CICESE Ensenada B.C. México