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Linked Edges as Stable Region Boundaries

Reporter: Dan Gou Date:2010-07-09

 Author Introduction  MSER Introduction  Abstract  Algorithm  Experiment

Outline

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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. CVWW08

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Author Introduction(2/2)

 Second Author:Hayko Riemenschneider  2008-now, RA, Graz University of Technology 2008, MSc, Graz University of Technology  Horst 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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 Author Introduction  MSER Introduction  Abstract  Algorithm  Experiment

Outline

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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 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.

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 Definition

MSER 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?

  I_t is a thresholded image of I In many images,

I t

 0 ,

I

 255 ,  

I

  

t



t

local binarization is stable over a large range of thresholds in certain regions. ( MSER ) I I_t I_t(t = 0~255)

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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

R l



R l

 1

B

  

R

min ,...,

R l

,

R l

 1 ,...,

R

max 

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MSER Algorithm(3/3)

 Algorithm  Computing the stability score  score

v

 

l



R l

 

R l



R l

  

l R l R l

 1

R l

   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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R

max 

MSER Result

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 Author Introduction  MSER Introduction  Abstract  Algorithm  Experiment

Outline

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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 the

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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 input image  Gray imge Vs.

Gradient magnitude image  Different 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 c I(x)>=0 the whole image is a connected region

C i t

Shape Similarity

C j t

  I(x)>=1 image is divided into several connected regions

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c

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 

C j



C j

 

x



C j

:

DT i where

 is a maximum   boundary distance parameter  For the region Ci, the corresponding stability value is the average   

i

 1

N n N

  1

DT i

 

n

, where

x n



C j

 Select the boundary which has a small stability value

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 Author Introduction  MSER Introduction  Abstract  Algorithm  Experiment

Outline

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Experiment(1/4)

  Data base:  ETHZ object detection data set  Weizmann horses Parameter setting   Minimum region size: 400    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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31 T

HANK YOU

!

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