Lecture 08 - Graphs and Cutting Images - CP Fall 2019.pptx...

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Cutting Images: Graphs and Boundary Finding Computational Photography Derek Hoiem, University of Illinois 09/21/19 “The Double Secret”, Magritte
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Last class: texture synthesis and transfer
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Last class: in-painting
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This Lecture: Finding Seams and Boundaries Segmentation
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This Lecture: Finding Seams and Boundaries Retargeting
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This Lecture: Finding Seams and Boundaries Stitching
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This Lecture: Finding seams and boundaries Fundamental Concept: The Image as a Graph Intelligent Scissors: Good boundary = short path Graph cuts: Good region has low cutting cost
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Semi-automated segmentation User provides imprecise and incomplete specification of region – your algorithm has to read his/her mind. Key problems 1. What groups of pixels form cohesive regions? 2. What pixels are likely to be on the boundary of regions? 3. Which region is the user trying to select?
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What makes a good region? Contains small range of color/texture Looks different than background Compact
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What makes a good boundary? High gradient along boundary Gradient in right direction Smooth
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The Image as a Graph Node: pixel Edge: cost of path or cut between two pixels
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Intelligent Scissors Mortenson and Barrett (SIGGRAPH 1995)
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Intelligent Scissors A good image boundary has a short path through the graph. Mortenson and Barrett (SIGGRAPH 1995) 1 2 1 4 1 6 9 1 3 1 4 1 1 3 2 3 5 Start End
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Intelligent Scissors Formulation: find good boundary between seed points Challenges Minimize interaction time Define what makes a good boundary Efficiently find it
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