ads lab ncku1 michael maire, pablo arbelaez, charless fowlkes, and jitendra malik university of...
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ADS lab NCKU 1
Michael Maire , Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik
university of California, Berkeley – Berkeleyuniversity 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
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Outline Introduction flowchart Contour Detection
◦ Local contours◦ Global Contours
Detecting Junctions
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Introduction Contour Detection
Application◦ Image segment
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Introduction Junctions Detection
Application
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flowchart
Image
Local contours
Globalcontours
Result
Contour Detection:
Junctions Detection:
Contour DetectionResult
Result
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Contour Detection: Local contours
Boundary Cues
Model
Pb
Brightness
Color
Texture
Goal: learn the posterior probability of a boundary Pb(x,y,) from local information only
Cue Combination
Martin, Fowlkes, Malik PAMI 04
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Contour Detection: Local contours
Line Detection
梯度運算子 (Gradient Operator)
Individual Features Brightness Gradient BG (x,y,r,)
Color Gradient CG (x,y,r,)
Texture Gradient TG (x,y,r,)
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Contour Detection: Local contours
These are combined using logistic regression
r
(x,y)
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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)
How to partition the Graph:
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Contour Detection: Global Contours
Partition graph so that similarity within group is large and similarity between groups is small -- Normalized Cuts [Shi & Malik 97]
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Contour Detection: Global Contours
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Contour Detection: Global Contours
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Contour Detection: Combine
Global ContoursLocal contours
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
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Detecting Junctions
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Detecting Junctions
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Demo
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Conclusions & Comment 上課時呈現 ^__^