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Computational Photography -Final Project- Miniature Photography Jia-Bin Huang [email protected]

UIUC CS 498 - Computational Photography - Final project presentation

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Page 1: UIUC CS 498 - Computational Photography - Final project presentation

Computational Photography-Final Project-

Miniature PhotographyJia-Bin Huang

[email protected]

Page 2: UIUC CS 498 - Computational Photography - Final project presentation
Page 3: UIUC CS 498 - Computational Photography - Final project presentation
Page 4: UIUC CS 498 - Computational Photography - Final project presentation

Outline

• Introduction of fake blur• Process Overview• Depth-of-Field (DOF) Specification• Results• Discussion

Page 5: UIUC CS 498 - Computational Photography - Final project presentation

Introduction of Fake Miniature

Slide credit: Rebert Held

• Optical blur

Page 6: UIUC CS 498 - Computational Photography - Final project presentation

Normal Lens

Slide credit: Rebert Held

Page 7: UIUC CS 498 - Computational Photography - Final project presentation

Tilt-Shift Lens

Slide credit: Rebert Held

Page 8: UIUC CS 498 - Computational Photography - Final project presentation

Process Overview

• Goal: Simulate the Tilt-Shift Effect (i.e., miniature photography)

• Process– Focus line/region/target selection– DOF specification– Blur image– Color Enhancement (optional)

Page 9: UIUC CS 498 - Computational Photography - Final project presentation

Depth-of-Field (DOF) Specification

• We lost depth information from the image.

• Assumption: objects on the same straight line are at the same depth.

• Methods to specify DOF– Horizontal– Focus line– Object mask– Salient region detection

Page 10: UIUC CS 498 - Computational Photography - Final project presentation

DOF - Horizontal

Page 11: UIUC CS 498 - Computational Photography - Final project presentation

DOF - Focus Line

Page 12: UIUC CS 498 - Computational Photography - Final project presentation

DOF – Object Mask

Page 13: UIUC CS 498 - Computational Photography - Final project presentation

DOF – Salient Region Detection

Page 14: UIUC CS 498 - Computational Photography - Final project presentation

More Results

Page 15: UIUC CS 498 - Computational Photography - Final project presentation
Page 16: UIUC CS 498 - Computational Photography - Final project presentation
Page 17: UIUC CS 498 - Computational Photography - Final project presentation
Page 18: UIUC CS 498 - Computational Photography - Final project presentation
Page 19: UIUC CS 498 - Computational Photography - Final project presentation
Page 20: UIUC CS 498 - Computational Photography - Final project presentation
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Discussion

• Two main open issues– Depth estimation from single image– Salient region selection

• Accurate depth information can help create realistic miniature effect

• Salient region selection can help automate the miniature photography process

Page 24: UIUC CS 498 - Computational Photography - Final project presentation

References

• [1] Held et al. "Using Blur to Affect Perceived Distance and Size". ACM Transactions on Graphics 2010.

• [2] Levin et al. "Image and Depth from a Conventional Camera with a Coded Aperture". SIGGRAPH 2007.

• [3] Felzenszwalb and Huttenlocher. "Efficient Graph-Based Image Segmentation". IJCV 2004.

• [4] Achant et al. " Frequency-tuned Salient Region Detection ". CVPR 2009.

Page 25: UIUC CS 498 - Computational Photography - Final project presentation

Thank You!Questions?