Download presentation

Presentation is loading. Please wait.

Published byJordan Butter Modified over 2 years ago

1
Planar Orientation from Blur Gradients in a Single Image Scott McCloskey Honeywell Labs Golden Valley, MN, USA Michael Langer McGill University Montreal, QC, Canada

2
Outline Introduction Relation to Previous Work Modelling the Blur Gradient Planar Orientation Estimation Algorithm ◦ Estimating Tilt ◦ Estimating Slant Test Data and Experimental Results

3
Introduction A focus-based method to recover the orientation of a textured planar surface patch from a single image

4
Relation to Previous Work Depth from Defocus Shape from Texture ◦ Distance effect ◦ Foreshortening effect

5
Modelling the Blur Gradient(1/3) The goal of planar orientation algorithms is to accurately estimate the slant and tilt of a 3D plane

6
Modelling the Blur Gradient(2/3) Visible surface is a plane of depth The slant and tilt are the same at all positions in the image patch Focal length : f The distance from the sensor plane to the lens:

7
Modelling the Blur Gradient(3/3) camera’s aperture :F focal length: f sensor distance: blur radius: image position: (x,y) is a linear function of inverse depth blur radius is a linear function of image position (x, y) the blur gradient

8
Planar Orientation Estimation Algorithm(1/3) Image blur is best observed in the middle to high spatial frequencies ◦ remove low frequencies by low pass filter Comparing the blur along different lines in an image ◦ Sharpness measure

9
Planar Orientation Estimation Algorithm(2/3) Estimating Tilt ◦ Equifocal contour A contour along which the amount of optical blur remains constant ◦ Fnding surface tilt searches for the direction in which the sharpness gradient is maximized

10
Estimating Slant ◦ Slant is estimated as the angle whose back- projection ◦ Produces the smallest gradient in the sharpness measure in the direction of former depth variation ◦ Uniformly blurred image (“doubly blurred image ”) Perspective- induced size change

11
Test Data and Experimental Results(1/4) Test set: 1404 camera images ◦ 9 planar textures ◦ 26 carefully-controlled orientations ◦ 6 different apertures (F = 22, 16, 11, 8, 5.6, 4) 26 planar orientations(Table 1.)

12
Test Data and Experimental Results(2/4) Orientation Estimation Results

13
Test Data and Experimental Results(3/4) Experiments with Image Size

14
Test Data and Experimental Results(4/4) Experiments with Natural Images

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google