Linked Edges as Stable
Region Boundaries
1
Reporter: Dan Gou
Date:2010-07-09
Outline
 Author
Introduction
 MSER Introduction
 Abstract
 Algorithm
 Experiment
2
Author Introduction(1/2)
 First


Author:Donoser Michael (post-doc)
Graz University of Technology
Research
Image Acquisition
 Unsupervised Color Segmentation
 Tracking
 Shape Matching
 Edge Detection


Related work
3D Segmentation by Maximally Stable Volumes (MSVs). ICPR06
 Efficient Maximally Stable Extremal Region (MSER) Tracking.CVPR06
 Color Blob Segmentation by MSER Analysis. ICIP06
 Online Object Recognition by MSER Trajectories. ICPR08
 Robust Online Object Learning and Recognition by MSER Tracking.
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CVWW08

Author Introduction(2/2)
 Second

2008-now, RA, Graz University of Technology
2008, MSc, Graz University of Technology
 Horst

Author:Hayko Riemenschneider
Bischof
Professor, Graz University of Technology
co-chairman of international conferences (ICANN, DAGM),
and local organizer for ICPR'96
 program co-chair of ECCV2006 and Area chair of CVPR 2007,
ECCV2008, CVPR 2009, ACCV 2009.
 Associate Editor for IEEE Trans. on Pattern Analysis and Machine
Intelligence, Pattern Recognition, Computer and Informatics and the
Journal of Universal Computer Science.



1993, Ph.D. The Vienna University of Technology
1990, M.S. the Vienna University of Technology
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Outline
 Author
Introduction
 MSER Introduction
 Abstract
 Algorithm
 Experiment
5
MSER Introduction
 MSER
MSER stands for—Maximally Stable Extremal Regions
 A method of blob detection in images


This method of extracting a comprehensive number of corresponding
image elements contributes to the wide-baseline matching, and it has
led to better stereo matching and object recognition algorithms.
 MSER
Definition
 How comes MSER
 MSER properties
 MSER Algorithm
 MSER Result
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参考文献：J. Matas, O. Chum, M. Urba, and T. Pajdla. "Robust wide baseline stereo from maximally
stable extremal regions." Proc. of British Machine Vision Conference, 2002.
MSER Definition
 Definition
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How comes MSER
 Imagine
that a gray-level image as a topographic
map

The hills and valleys will be corresponding to the local intensity
maximal and minimal regions.

Along with the height increasing from 0 to a large num, the hills and
valleys will be stable for a large range of the height
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How comes MSER
 How


to do?
0, I x   t
It  
255, I x   t
I_t is a thresholded image of I
In many images, local binarization is stable over a large
range of thresholds in certain regions. (MSER)
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I
I_t
I_t(t = 0~255)
MSER Properties
 MSER
properties as a region detector
Invariance to affine transformation of image intensities
 Covariance to adjacency preserving
 Stability
 Muti-scale detection
 Can be enumerated in O(nloglogn) (quasi-linear)

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MSER Algorithm
 Algorithm
Build component tree
 Extract extremal regions
 Arrange the extremal regions in a tree of nested regions
 Computing the stability score
 Refining the selection

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MSER Algorithm(1/3)
 Algorithm

Build component tree(using the union-find sets)
Definition: A representation of a gray-level image that contains
information about each image component and the links that exist
between components at sequential gray-levels in the image.
 All pixels are arranged by their intensity and neighborhood
relationship
 Every child tree is corresponding to a region, and the root of the
child tree is the index of the pixel who has the biggest value in the
region

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MSER Algorithm(2/3)
 Algorithm

Extract extremal regions


In the component tree, nodes whose parent nodes have a bigger
value.
Arrange the extremal regions in a tree of nested regions

Connecting two regions R_l and R_l+1, if and only if Rl  Rl 1
BR  Rmin ,...,Rl , Rl 1 ,...,Rmax 
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MSER Algorithm(3/3)
 Algorithm

Computing the stability score

We associate to the branch BRl   Rl , Rl 1,...,Rl   the stability
R R
score vRl   l  l
Rl



Here the Rl is the region size
Select the maximally stable region in the branch BR  Rmin ,...,Rl , Rl 1,...,
, Rmax 
which has a local minimal stability score
Refining the selection
Remove very small and very big regions
 Remove regions which have too high area variation
 Remove duplicated regions

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MSER Result
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Outline
 Author
Introduction
 MSER Introduction
 Abstract
 Algorithm
 Experiment
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Abstract
 Problem

Find the most stable region boundaries in grayscale
images
 Solution


Use a component tree where every node contains a single
connected region obtained from thresholding the gradient
magnitude image
Region boundaries which are similar in shape across
several levels of the tree are included in the final result
 Superiority


Efficient (quasi-linear)
Label all indentified edges during calculation, avoiding the17
cumbersome post-processing
Outline
 Author
Introduction
 MSER Introduction
 Abstract
 Algorithm


Difference from MSER
Component tree edge detection
 Experiment
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Difference from MSER
 Different

Gray imge Vs. Gradient magnitude image
 Different

input image
stability criterion
Analyzing the stability of the shape of the region contours
Vs. region size stability
 Indentify
parts of the region contours that are similar,
the returned edges need not be closed.
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Outline
 Author
Introduction
 MSER Introduction
 Abstract
 Algorithm


Difference from MSER
Component tree edge detection
 Experiment
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Component tree edge detection
 Preprocessing

Gray imge -> Gradient magnitude image
 Component
tree(similar to MSER)
 elect stable region boundaries

Different stability criterion from MSER

Analyzing the stability of the shape of the region contours
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Proprecessing
 Gray



imge -> Gradient magnitude image
Smooth the image with a low-pass filter to remove noise
A first order 2D Gaussian derivative filter
Normalize the magnitudes and scale them to an integer
range
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Component tree
 Component
tree
C
I(x)>=0 the whole image is a i
t
C
Shape Similarity j
connected region
I(x)>=1 image is divided into
several connected regions
c
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c
c
t 
Select stable region boundaries
 Shape


similarity
Distance Transfrom ( chamfer distance)
Stability value
boundary
d4-DT
d8-DT
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Select stable region boundaries
 Stability

value
Boundaries Ci and C j
Get the distance transformation DTi of Ci
 Finding connected boundary fragment C j  C j fulfilling

C j  C j  x  C j : DTi x   
where is a maximumboundary distanceparameter

For the region Ci, the corresponding stability value is the average
chamfer distance of the matched boundary pixels C j
1 N
Ci    DTi xn , where xn  C j
N n 1

Select the boundary which has a small stability value
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Outline
 Author
Introduction
 MSER Introduction
 Abstract
 Algorithm
 Experiment
26
Experiment(1/4)
 Data base:
 ETHZ object detection data set
 Weizmann horses
 Parameter setting
 Minimum region size: 400
 Stability parameter : 5
 Shape similarity parameter
 : 10
 Compare
 Precision
 Recall
 F-measure
 Weighted harmonic mean of precision and recall
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Experiment(2/4)
 Improvement


Able to match the quality of the detection results of a
supervised method
Able to match the speed of a standard Canny method
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Experiment(3/4)
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Experiment(4/4)
 Advantage

Far less noise


Only stable edges are returned
No post-processing is required

In contrast to the edge responses from Canny or Berkeley, our edges
are connected and uniquely labeled
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THANK YOU!
Q&A
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