Top Banner
Static Image Mosaicing Amin Charaniya ([email protected]) EE 264: Image Processing and Reconstructi
22

Static Image Mosaicing Amin Charaniya ([email protected]) EE 264: Image Processing and Reconstruction.

Dec 21, 2015

Download

Documents

Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Static Image Mosaicing

Amin Charaniya

([email protected])

EE 264: Image Processing and Reconstruction

Page 2: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Presentation Overview

Problem definition Background

Literature Survey Image transformations

Image Registration Coarse Image registration Transformation Optimization

Image Blending Implementation and Results Conclusions (limitations and enhancements)

Page 3: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

The Problem

Q: “Static” ?Ans.: No moving objects in the scene.

+Image 1 Image 2 Mosaiced image

Page 4: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

The Solution

Original images

Image Registration /Alignment / Warping Image Blending

Page 5: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Constraints

Scene Static / Dynamic Planar / Non planar (perspective distortion)

Camera Motion Translation (sideways motion) Panning and Tilting (rotation about the Y and X axes) Scaling (zooming, forward / backward motion) General motion

Other Constraints Automated / User input

Page 6: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Background and Literature survey

Barnea & Silverman, 1972 (L1 Norm) Kuglin & Hines, 1975 (Phase Correlation) Mann & Picard, 1994 (Cylindrical projection) Irani & Anandan, 1995 (Static and Dynamic mosaics) Szeliski, 1996 (Transformation optimization) Badra, 1998 (Rotation and Zooming) Peleg and Rousso, 2000 (Adaptive Manifolds, Mosaicing

using strips)

Page 7: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Image transformations

TransformationInputimage

Output

image

w

y

x

w

y

x

876

543

210

'

'

'

mmm

mmm

mmm

100

543

210

mmm

mmm

affineM

Affine transformation

876

543

210

mmm

mmm

mmm

projectiveM

Projective transformation

100

cossin

sincos

y

x

rigid t

t

M

Rigid transformationOriginal

shape

Page 8: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Presentation Overview

Problem definition Background

Literature Survey Image transformations

Image Registration Coarse Image registration Transformation Optimization

Image Blending Implementation and Results Conclusions (limitations and enhancements)

Page 9: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Image Registration

Coarse ImageRegistration

Initial transformation TransformationOptimization

ErrorImproved ?

{Phase Correlation

L1 Norm

User input

Page 10: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Phase Correlation

Kuglin & Hines, 1975 Translation property of Fourier Transform

)(2..00

00),(),( yx yxjyxTFeFyyxxf

1|| 1..1j

TFeFf

2|| 2..2

jTF

eFf

)( 21 jeInverse

transformd(x,y)

maximum

(x0, y0)

Page 11: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Spatial Correlation, L1 Norm

Barnea and Silverman

E(x0,y0) = |f1(x,y) – f2(x- x0, y- y0)|

f1

f2 f2

Spatial correlation techniques User input

Page 12: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Transformation Optimization

Richard Szeliski, “Video Mosaics for Virtual Environments”, 1996. Optimization of initial transformation matrix M, to minimize error. Levenberg-Marquardt non-linear minimization algorithm.

yx

yxfyxfeerror,

221

)),('),(()(minimize

Compute partial derivatives

}7..0{,

km

e

k

bIAm1

)( mMM )()1( tt

Page 13: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Transformation Optimization

Advantages Faster convergence Statistically optimal solution

Limitations Local minimization (need a good initial guess)

Page 14: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Presentation Overview

Problem definition Background

Literature Survey Image transformations

Image Registration Coarse Image registration Transformation Optimization

Image Blending Implementation and Results Conclusions (limitations and enhancements)

Page 15: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Image Blending

Simple averaging Weighted averaging

2/)),('),((),( 21 yxfyxfyxf

),('),(),(),(),( 2211 yxfyxwyxfyxwyxf

Smooth transition (edges, illumination artifacts)

Sample weight function – “hat filter”

0 xmax

2

|2

|1)(

max

max

x

xx

xw

More weight at the center of the image, less at the edges

Page 16: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Image blending

Simple averaging Weighted averaging

Page 17: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Presentation Overview

Problem definition Background

Literature Survey Image transformations

Image Registration Coarse Image registration Transformation Optimization

Image Blending Implementation and Results Conclusions (limitations and enhancements)

Page 18: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Implementation

Implemented using Matlab Source Images

BE 230 lab images (fixed tripod) College 8 images (free hand motion, perpective distortion) East Field House images (free hand motion)

Equipment: Sony DCR-TRV 900 3CCD digital camcorder

Page 19: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Sample results

Page 20: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Sample results

Page 21: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Conclusions/Enhancements

Better automatic coarse registration techniques needed.

Need to handle more general camera motion.

Page 22: Static Image Mosaicing Amin Charaniya (amin@cse.ucsc.edu) EE 264: Image Processing and Reconstruction.

Thanks for listening !!

Questions ?