Presentation is loading. Please wait.

Presentation is loading. Please wait.

ADS lab NCKU1 Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik university of California, Berkeley – Berkeley university of California,

Published byLora Mathews Modified over 9 years ago

Similar presentations

Presentation on theme: "ADS lab NCKU1 Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik university of California, Berkeley – Berkeley university of California,"— Presentation transcript:

1 ADS lab NCKU1 Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik university of California, Berkeley – Berkeley university of California, Irvine - Irvine Using Contours to Detect and Localize Junctions in Natural Images 指導教授 : 李強 教授 Reporter: P76961138 王炳竣 P76964063 方俞淵 P76964495 陳柏嘉

2 ADS lab NCKU2 Outline Introduction flowchart Contour Detection ◦ Local contours ◦ Global Contours Detecting Junctions

3 ADS lab NCKU3 Introduction Contour Detection Application ◦ Image segment

4 ADS lab NCKU4 Introduction Junctions Detection Application

5 ADS lab NCKU5 flowchart Image Local contours Global contours Global contours Result Contour Detection: Junctions Detection: Contour Detection Result Contour Detection Result

6 ADS lab NCKU6 Contour Detection: Local contours Boundary Cues Model PbPb Brightness Color Texture Goal: learn the posterior probability of a boundary P b (x,y,  ) from local information only Cue Combination Martin, Fowlkes, Malik PAMI 04

7 ADS lab NCKU7 Contour Detection: Local contours Line Detection 梯度運算子 (Gradient Operator)

8 Individual Features Brightness Gradient BG (x,y,r,  ) Color Gradient CG (x,y,r,  ) Texture Gradient TG (x,y,r,  ) ADS lab NCKU8 Contour Detection: Local contours These are combined using logistic regression  r (x,y)

9 ADS lab NCKU9 Contour Detection: Global Contours Node : the points in the feature space Graph: G(V, E) Edge : between every pair of points Weight(Wij) Image segmentation (high-level cues)

10 How to partition the Graph: ADS lab NCKU10 Contour Detection: Global Contours Partition graph so that similarity within group is large and similarity between groups is small -- Normalized Cuts [Shi & Malik 97]

11 ADS lab NCKU11 Contour Detection: Global Contours

12 ADS lab NCKU12 Contour Detection: Global Contours

13 ADS lab NCKU13 Contour Detection: Combine Global ContoursLocal contours

14 1.Estimate the optimal junction location by minimizing its weighted distance from the contours 2.Update the weight of each contour in order to select only those contours passing close to the junction ADS lab NCKU14 Detecting Junctions

15 ADS lab NCKU15 Detecting Junctions

16 ADS lab NCKU16 Demo

17 ADS lab NCKU17 Conclusions & Comment 上課時呈現 ^__^

Download ppt "ADS lab NCKU1 Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik university of California, Berkeley – Berkeley university of California,"

Similar presentations

Pose Estimation and Segmentation of People in 3D Movies Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev Inria, Ecole Normale Superieure ICCV.

Pose Estimation and Segmentation of People in 3D Movies Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev Inria, Ecole Normale Superieure ICCV.
Spectral graph reduction for image and streaming video segmentation Fabio Galasso 1 Margret Keuper 2 Thomas Brox 2 Bernt Schiele 1 1 Max Planck Institute.

Spectral graph reduction for image and streaming video segmentation Fabio Galasso 1 Margret Keuper 2 Thomas Brox 2 Bernt Schiele 1 1 Max Planck Institute.
Carolina Galleguillos, Brian McFee, Serge Belongie, Gert Lanckriet Computer Science and Engineering Department Electrical and Computer Engineering Department.

Carolina Galleguillos, Brian McFee, Serge Belongie, Gert Lanckriet Computer Science and Engineering Department Electrical and Computer Engineering Department.
Qualifying Exam: Contour Grouping Vida Movahedi Supervisor: James Elder Supervisory Committee: Minas Spetsakis, Jeff Edmonds York University Summer 2009.

Qualifying Exam: Contour Grouping Vida Movahedi Supervisor: James Elder Supervisory Committee: Minas Spetsakis, Jeff Edmonds York University Summer 2009.
Graph-Based Image Segmentation

Graph-Based Image Segmentation
1 P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour Detection and Hierarchical image Segmentation. IEEE Trans. on PAMI, 2011. Student: Hsin-Min Cheng.

1 P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour Detection and Hierarchical image Segmentation. IEEE Trans. on PAMI, Student: Hsin-Min Cheng.
Boundary Extraction in Natural Images Using Ultrametric Contour Maps Pablo Arbeláez Université Paris Dauphine Presented by Derek Hoiem.

Boundary Extraction in Natural Images Using Ultrametric Contour Maps Pablo Arbeláez Université Paris Dauphine Presented by Derek Hoiem.
1 Contours and Junctions in Natural Images Jitendra Malik University of California at Berkeley (with Jianbo Shi, Thomas Leung, Serge Belongie, Charless.

1 Contours and Junctions in Natural Images Jitendra Malik University of California at Berkeley (with Jianbo Shi, Thomas Leung, Serge Belongie, Charless.
1 Minimum Ratio Contours For Meshes Andrew Clements Hao Zhang gruvi graphics + usability + visualization.

1 Minimum Ratio Contours For Meshes Andrew Clements Hao Zhang gruvi graphics + usability + visualization.
Ghunhui Gu, Joseph J. Lim, Pablo Arbeláez, Jitendra Malik University of California at Berkeley Berkeley, CA 94720.

Ghunhui Gu, Joseph J. Lim, Pablo Arbeláez, Jitendra Malik University of California at Berkeley Berkeley, CA
Biased Normalized Cuts 1 Subhransu Maji and Jithndra Malik University of California, Berkeley IEEE Conference on Computer Vision and Pattern Recognition.

Biased Normalized Cuts 1 Subhransu Maji and Jithndra Malik University of California, Berkeley IEEE Conference on Computer Vision and Pattern Recognition.
10/11/2001Random walks and spectral segmentation1 CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View.

10/11/2001Random walks and spectral segmentation1 CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View.
Lecture 6 Image Segmentation

Lecture 6 Image Segmentation
Recognition using Regions CVPR 2009. Outline Introduction Overview of the Approach Experimental Results Conclusion.

Recognition using Regions CVPR Outline Introduction Overview of the Approach Experimental Results Conclusion.
Image segmentation. The goals of segmentation Group together similar-looking pixels for efficiency of further processing “Bottom-up” process Unsupervised.

Image segmentation. The goals of segmentation Group together similar-looking pixels for efficiency of further processing “Bottom-up” process Unsupervised.
Algorithms & Applications in Computer Vision Lihi Zelnik-Manor Lecture 11: Structure from Motion.

Algorithms & Applications in Computer Vision Lihi Zelnik-Manor Lecture 11: Structure from Motion.
Computer Vision Group University of California Berkeley 1 Learning Scale-Invariant Contour Completion Xiaofeng Ren, Charless Fowlkes and Jitendra Malik.

Computer Vision Group University of California Berkeley 1 Learning Scale-Invariant Contour Completion Xiaofeng Ren, Charless Fowlkes and Jitendra Malik.
CS 376b Introduction to Computer Vision 04 / 08 / 2008 Instructor: Michael Eckmann.

CS 376b Introduction to Computer Vision 04 / 08 / 2008 Instructor: Michael Eckmann.
© 2003 by Davi GeigerComputer Vision October 2003 L1.1 Image Segmentation Based on the work of Shi and Malik, Carnegie Mellon and Berkley and based on.

© 2003 by Davi GeigerComputer Vision October 2003 L1.1 Image Segmentation Based on the work of Shi and Malik, Carnegie Mellon and Berkley and based on.
Region Segmentation. Find sets of pixels, such that All pixels in region i satisfy some constraint of similarity.

Region Segmentation. Find sets of pixels, such that All pixels in region i satisfy some constraint of similarity.

Similar presentations

Ads by Google