Transcript Landmark Based Robot Navigation using Incremental Learning

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
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 peerreviewed 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 humancreated 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.
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.
The Problem
Wide Range of Applications
• Stereo correspondence.
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.
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.
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.
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.
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.
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.
IPGP1
IPGP1  G(  2)  [G(  1) * I  I ]
IPGP1
IPGP2
IPGP2  G(  1)  [ Lxx L yy ]  G(  1)  [ Lxy L xy ]
IPGP2
Evaluation on Rotation
Images - Illumination
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.
Performance Evaluation in Schmid et al.
2000, for Image Rotation Sequence
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.
Performance for Image Rotation Sequence
Performance for Image Rotation Sequence
Results - Rotation
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 peerreviewed 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 endstopped 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.
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
HUMAN COMPETITIVENESS
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.
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. 150166, 1986.
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. .
HUMAN COMPETITIVENESS
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.
HUMAN COMPETITIVENESS
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 peerreviewed 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 humancreated 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.
WHY SHOULD THIS WORK WIN?
1. This work strengthens the link between two
major research areas in computer science:
Computer Vision & Evolutionary Computation.
2. 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.
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