Transcript of 1 P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour Detection and Hierarchical image...
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- 1 P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour
Detection and Hierarchical image Segmentation. IEEE Trans. on PAMI,
2011. Student: Hsin-Min Cheng Advisor: Sheng-Jyh Wang
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- Outline Introduction Contour Detection Hierarchical
Segmentation Results Conclusion 2
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- Introduction Original ImageContour Contour 3
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- Introduction Original ImageSegmentation Segmentation 4
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- Introduction From Contour to Segmentation Original
ImageSegmentationContour 5
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- Introduction Goal Contour Detection Hierarchical Segmentation
from Contours Original ImageSegmentationContour 6
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- Outline Introduction Contour Detection Hierarchical
Segmentation Results Conclusion 7
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- Contour Detection 1. Learn local boundary cues 2. Global
framework to capture closure, continuity 3. Local Cues and global
cues combination 8
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- Learn local boundary cues Image Local Boundary Cues Model
Brightness Color Texture Cue Combination Contour Detection 9
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- Learn local boundary cues Brightness L*a*b* colorspace Color
L*a*b* colorspace Texture Convolve with 17 filters Filters for
creating textons 10 Contour Detection
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- 11 Learn local boundary cues Oriented gradient of histograms
Example Gradient magnitude G at location(x, y) Three scales of r 11
Contour Detection ure
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- 12 Learn local boundary cues Local Cues Combination 12 Contour
Detection ure
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- Global framework to capture closure, continuity Contour
Detection 13 V:image pixels E:connections between pairs of nearby
pixels =>Build a weighted graph G=(V,E) from image
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- Global framework to capture closure, continuity Contour
Detection 14
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- Local Cues and global cues combination Contour Detection 15
Local CuesGlobal cues
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- Outline Introduction Contour Detection Hierarchical
Segmentation Results Conclusion 16
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- Hierarchical Segmentation Multiple Segmentations Fixed
resolution Hierarchy of Segmentations Flexible resolution
adjustment 17
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- Hierarchical Segmentation 1. From contours to segmentation 2.
Hierarchical segmentation by iterative merging 18
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- Hierarchical Segmentation From contours to segmentation
Watershed Transform Concept 19
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- Hierarchical Segmentation From contours to segmentation
Watershed Transform Example 20
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- Hierarchical Segmentation From contours to segmentation
Watershed Transform 21 Boundary strength Artifacts Weight each
arc
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- Hierarchical Segmentation From contours to segmentation
Oriented Watershed Transform 22 WT OWT
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- Hierarchical Segmentation Hierarchical segmentation by
iterative merging Hierarchical segmentation Example 23
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- Brief Summary 24 Original Image - Local cues - Global cues
Oriented Gradient of histograms Contour Oriented Watershed
Transform Iterative Merging Hierarchical Segmentation
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- Outline Introduction Contour Detection Hierarchical
Segmentation Results Conclusion 25
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- Result 26
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- Result 27
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- Result 28 Evaluation of segmentation algorithmsEvaluation of
contour detector BSDS300 Dataset
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- Outline Introduction Contour Detection Hierarchical
Segmentation Results Conclusion 29
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- Conclusion A high performance contour detector, combining local
and global image information A method to transform any contour
detector signal into a hierarchy of regions while preserving
contour quality 30
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- Reference P. Arbelaez, M. Maire, C. Fowlkes and J. Malik.
Contour Detection and Hierarchical Image Segmentation. IEEE TPAMI,
Vol. 33, No. 5, pp. 898-916, May 2011 P. Arbelaez, M. Maire, C.
Fowlkes and J. Malik. From Contours to Regions: An Empirical
Evaluation. In CVPR 2009. P. Arbelaez and L. Cohen. Constrained
Image Segmentation from Hierarchical Boundaries. In CVPR 2008.
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- Outline Introduction Contour Detection Hierarchical
Segmentation Evaluation Results 32
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- Boundary Benchmarks ODS : optimal dataset scale OIS : optimal
image scale AP :average precision 33
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- Region benchmarks(1) Segment Covering Probabilistic Rand Index
[Unnikrishnan et. al. 07] [Yang et. al. 08] Variation of
Information [Meila 05] Distance Between two segmentations in terms
of their average conditional entropy given by 34
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- Region benchmarks(2) CoveringRand Index Variation of
Information 35
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- Additional Dataset 36