Scene segmentation & interpretation Shape context, a descriptor for object recognition in computer vision.

1. Shape Context
Rocío Cabrera u1908272
Vanya Valindria u1908259
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2. Introduction
Can you guess what number it is?
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3. Objectives
“Have descriptors that can be computed in
one image and used to find corresponding
points, if visible, in another image.”
“Given a query model image, to develop an
algorithm capable of retrieving similar-
shaped images from an extensive database”
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4. Process Stages
Solve the Use the
Evaluate the
correspondences Compute the
correspondence
.
problem between
the two shapes
.
to estimate an
aligning
transform
distance between
the two shapes ?
distance and
classify the
shape
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5. SHAPE CONTEXT
“A novel approach to measuring similarities between shapes and
exploit it for object classification/recognition”
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6. Shape Context Computation
Step 1.
Obtain from ShapeP and ShapeQ n-samples uniformly spaced taken from
their edge elements
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7. Shape Context Computation
Step 2.
Compute the Euclidean distance (r) and the angle (θ) from each point in
the set to all the other n-1 points.
Normalize r by the median distance (λ) and measure the angle relative to
the positive x-axis.
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8. Shape Context Computation
Step 3.
Compute the log of the r vector.
Discretize the distance and angle measurements
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9. Shape Context Computation
Step 4.
For each origin point, capture number of points that lie a given θ,R bin.
Each shape context is a log-polar histogram of the coordinates of the n-1
points measured from the origin reference point.
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10. Shape Context Computation
 Shape context of the sample points in ShapeP and
ShapeQ.
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11. Matching Shape Contexts
 How can we assign the sample points of ShapeP to
correspond to those of ShapeQ?
 Determining shape correspondences such that:
l Corresponding points have very similar descriptors
l The correspondences are unique
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12. Matching Shape Contexts
 Define matching cost function
 Shape context
 Distance between the two normalized histograms
 Local appearance
 Dissimilarity of the tangent angles
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13. Matching Shape Contexts
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14. Modeling Transformation
 Given a set of correspondences, estimate a
transformation that maps the model into the target
 Euclidean transformation
 Affine model
 Thin Plate Spline (TPS)
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15. Classification/Recognition
 This enables a measure of shape similarity
 The dissimilarity between two shapes can be computed
as the sum of matching errors between corresponding
points, together with a term measuring the magnitude
of the aligning transform
 Given a dissimilarity measure, a k-NN technique can
be used for object classification/recognition
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16. Method Evaluation
Advantages Drawbacks
 Incorporates invariance to:  Sensitive local distortion or
blurred edges
 Translation
 Problems in cluttered
 Scale background
 Rotation
 Occlusions
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17. Applications
 Digit recognition
 Silhouette similarity-
based retrieval
 3 D object recognition
 Trademark retrieval
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18. Database for Digit Recognition
 MNIST datasets of
handwritten digits:
 60,000 training and
10,000 test digits
Links:
http://yann.lecun.com/exdb/mnist/
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19. Database for Silhouette
 MPEG-7 shape silhouette
database (Core Experiment
CE-Shape-1 part B)
 1400 images: 70 shapes
categories and 20 images per
category
 Links:
http://mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm
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20. Database for 3-D object recognition
 COIL-20 database
 20 common household
objects; turned every 5˚ for
a total of 72 views per
object
 Links:
http://www1.cs.columbia.edu/CAVE/software/softlib/coil-20.php
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21. Database for Trademark retrieval
 300 different real-
world trademark
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22. MATLAB DEMO
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23. Conclusions
 The shape context method is simple to implement
yet it is a rich shape descriptor
 The methodology makes it invariant to translation,
scale and rotation
 Useful tool for shape matching and recognition
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