Transcript Problem Definition

Triangle-Constraint
for Finding More Good Features
Piero Zamperoni Best Student Paper Award, ICPR 2010
Xiaojie Guo and Xiaochun Cao
Computer Vision Laboratory
Tianjin University, China
Tianjin University
Computer Vision Lab
Motivation
Many tasks in computer vision and pattern
recognition are based on local image features.
Feature Extraction
- Numerous feature extraction schemes have
been proposed, like Harris Corner, SIFT etc.
Similarity Measurement
- However, similarity measurements for
features are limited.
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Motivation
For similarity measurement (SM), two factors, i.e.
&
need to be considered.
# correct matches/#total matches
However, recently proposed SMs only improve
the matching score but neglect the importance
of the num of correct matches
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Motivation
The neglect inspires us to propose an effective
similarity measurement for
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Motivation
There are 216 hits
(matching score
9 3 . 11 % ) u s i n g o u r
method(T-CM).
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There are 39 hits
(matching score
85.97%))using the
original matching
method(OMM).
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Triangle-Constraint Measurement
Seed Point Selection – Bi-matching
Illustration of bi-matching method.
The matches (seed points) are those marked by ellipses.
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Triangle-Constraint Measurement
Organization of Seed Points
Seed point
False positive match
Illustration of the Delaunay algorithm
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Triangle-Constraint Measurement
Triangle-Constraint
PA
PB
Pi
*
=
Illustration of the Triangle-Constraint
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Triangle-Constraint Measurement
Triangle-Constraint
P
Radius of candidate
A
the
set
containing
Euclidean
all
distance
the
temporary
the descriptors
forfrom
the PieCj
Thehandle
To
similarity
the score
problem
between
of falsethe
positive
P
i
and
matches
the
candidate
survived
feature
A
predefined
threshold
area R
matches
A and
B Pi
between for
thePCj
and P
the
and the Cj respectively
Bi-matching,
is
measured by
an additional step is taken after processing all the
features from PA:
S
If the maximum score of all the features in C is greater than a
predefined threshold τ, the corresponding feature pair is
To decide
whethermatch.
the temporary matches are
considered
as temporary
accepted as final matches or not.
C
Illustration of the Triangle-Constraint
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Experiment Evaluation
Dataset – INRIA dataset
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Experiment Evaluation
Evaluation Criterion
-The criterion of our evaluation is based on the number of
correct matches and the matching score.
- A match is defined as correct if the distance between the accurate location and the estimated location is less than
6 pixels, incorrect otherwise.
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Experiment Evaluation
Results – Relative image pair matching
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Experiment Evaluation
Results – Relative image pair matching
Due to the
huge
amount of
features that
increases
the
possibility of
accidentally
considering
incorrect
matches as
correct.
Since the matching score is
undefined (0/0)
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Experiment Evaluation
Results – Irrelative image pair matching
There are 22 hits by the OMM and 0 hit by our method.
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Conclusion
Triangle-Constraint Measurement:
• Effective technique for similarity measurement to
improve both the number of correct matches and
the matching score.
• Invariant to translation, rotation , scale and affine
transformations.
• Robust to partial perspective distortions.
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Question
Questions?
Computer Vision Lab @ TJU
Tianjin University
2009-08-11
Computer Vision Lab
http://cs.tju.edu.cn/orgs/vision
Thank you very much!
Computer Vision Lab @ TJU
Tianjin University
2009-08-11
Computer Vision Lab