# Shape Descriptors for Maximally Stable Extremal Regions by 8ubrKB

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```									Shape Descriptors for Maximally Stable
Extremal Regions

Per-Erik Forss´en and David G. Lowe
Department of Computer Science
University of British Columbia

Eleventh IEEE International Conference on
Computer Vision（ICCV – 2007）
Reporter：Shih-Hao Wang
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Outline

   Introduction

   Multi-Resolution

   Maximally Stable Extremal Regions（MSER）

   Experiment Model

   Experiment

   Conclusions
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Introduction

   Affine-invariance Concept：originally features of images
will not be changed after affine transformation

   Problem：When a scene is blurred or viewed from
increasing distances, many details in the image disappear
and different region boundaries are formed

   Use An affine invariant shape descriptor for maximally
stable extremal regions (MSER) to reduce above-
mentioned effect

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Multi-Resolution

   Nested Vector Space：
V       V1  V0  V1          V

V3 V2 V1 V0    V1    V2   V3

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Multi-Resolution

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Maximally Stable Extremal Regions

   Apply a series of thresholds – one for each grayscale level.

   Threshold the image at each level to create a series of
black and white images.

   One extreme will be all white, the other all black. In
between, blobs grow and merge.

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Maximally Stable Extremal Regions

   Example

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Multi-Resolution MSER

   Method：

   Step1：Instead of detecting features (from step4) only in
the input image

   Step2：Construct a scale pyramid with one octave
between scales

   Step3：Detect MSERs separately at each resolution

   Step4：Duplicate MSERs are removed by eliminating fine
scale MSERs with similar locations and sizes as MSERs      8
detected at the next coarser scale
Multi-Resolution MSER

   Scale pyramid

   The scale pyramid is constructed by blurring and resample
with a Gaussian kernel ,  = 1.0 pixels.
x2  y 2
1            
G2 D ( x, y; )              e      2 2
2   2                    9
Experiment (performance Measurement)

   Performance measure in this paper：inlier frequency

   k-th tentative correspondence is an inlier：inlier function
is 1

   Otherwise is 0

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Experiment

   3D scene correspondence evaluation

   The 56 accepted correspondences are shown in blue.
Features from rejected correspondences are shown in
green.
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Experiment

   3D scene results

   Performance of shape and texture descriptors on
the scene
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Experiment
   Planar and parallax-free scenes

   A scene with de-focus blur. The multi-resolution MSER
provides better performance than using only the original   13

resolution
Experiment

   Top image shows the 22 correspondences found using
single resolution MSERs.

   Bottom image shows the 53 correspondences found using
multi-resolution MSERs
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Experiment
   Planar and parallax-free scenes

   A scene with scale change. Again, the multi-resolution
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MSER gives better performance
Experiment

   Top image shows the 22 correspondences found using
single resolution MSERs.

   Bottom image shows the 33 correspondences found using
multi-resolution MSERs
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Conclusions

   New method provide better robustness to large scale
changes and blurred images

   Improve matching performance over large scale changes
and for blurred images

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