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Reporter: Dan Gou Date:2010-07-09

Linked Edges as Stable Region Boundaries. Reporter: Dan Gou Date:2010-07-09. Outline. Author Introduction MSER Introduction Abstract Algorithm Experiment. Author Introduction(1/2). First Author:Donoser Michael (post-doc) Graz University of Technology Research Image Acquisition

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Reporter: Dan Gou Date:2010-07-09

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  1. Linked Edges as Stable Region Boundaries Reporter: Dan Gou Date:2010-07-09

  2. Outline • Author Introduction • MSER Introduction • Abstract • Algorithm • Experiment

  3. 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

  4. 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

  5. Outline • Author Introduction • MSER Introduction • Abstract • Algorithm • Experiment

  6. 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.

  7. MSER Definition • Definition

  8. 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

  9. How comes MSER • How to do? • 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) I I_t I_t(t = 0~255)

  10. 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)

  11. 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

  12. 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

  13. 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

  14. MSER Algorithm(3/3) • Algorithm • Computing the stability score • We associate to the branch the stability score • Here the is the region size • Select the maximally stable region in the branch , 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

  15. MSER Result

  16. Outline • Author Introduction • MSER Introduction • Abstract • Algorithm • Experiment

  17. 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 cumbersome post-processing

  18. Outline • Author Introduction • MSER Introduction • Abstract • Algorithm • Difference from MSER • Component tree edge detection • Experiment

  19. 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.

  20. Outline • Author Introduction • MSER Introduction • Abstract • Algorithm • Difference from MSER • Component tree edge detection • Experiment

  21. 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

  22. 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

  23. Component tree • Component tree Shape Similarity I(x)>=0 the whole image is a connected region C P C P I(x)>=1 image is divided into several connected regions c V R c CVPR c

  24. Select stable region boundaries • Shape similarity • Distance Transfrom ( chamfer distance) • Stability value boundary d4-DT d8-DT

  25. Select stable region boundaries • Stability value • Boundaries Ci and C j • Get the distance transformation DTi of Ci • Finding connected boundary fragment fulfilling • For the region Ci, the corresponding stability value is the average chamfer distance of the matched boundary pixels • Select the boundary which has a small stability value

  26. Outline • Author Introduction • MSER Introduction • Abstract • Algorithm • Experiment

  27. 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

  28. 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

  29. Experiment(3/4)

  30. 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

  31. THANK YOU!Q&A

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