Studio 7: Surface Data Capture

Presented by

Rashid Javed (704076)

Supervisor:Prof. Dr. Ing. Eberhard Gülch

AGENDA:

1) Introduction

2) Workflow

3) Data and Software Used

4) Methodology

5) Experiment Results

6) Conclusion

Introduction

In the development of digital photogrammetric system, automatic image matching process play an important role.

The automatic image matching is used in finding the conjugate points of an aerial photograph stereo pair automatically.

The first solution of problems to Image matching was given by Hobrough in late 1950.

Main task is to Develop a procedure for automatic point transfer of regular raster points in an epipolar image pair

Introduction

Epipolar Images:

Epipolar images are stereo pairs in which the left and right images are oriented in such a way that ground feature points have the same y-coordinates on both images.

Introduction

Conjugate Points:

• Common points in the overlap areas of the stereo pair images.

• Also called Tie Points

• The search for correspondence points can be done in 2-D e.g. in a rectangle oriented along an approximate epipolar line.

Introduction

Relationship Between Matching Methods and Matching Entities

Introduction

Area Based Matching• Oldest and simplest matching technique• Is performed on the basis of grey values• Compare the grey level of small sub image with its counterpart in the

other image• Template and Search Window

Introduction

Correlation• Technique used for finding the conjugate points• Measure the similarity of template and search window computing the

correlation factor

Parallax• Apparent change in the position of object• Related to the elevation of the terrain

Applications: Image registration, DEM generation, displacement measurements, Target

measurements, Surveillance, Treaty verification

AGENDA:

Introduction

Objectives:

• To develop a procedure of automatic point transfer to determine the conjugate points in an epipolar image pair

• To compare the intensity or gray values of a template

• To asses the quality of the match through using correlation coefficient.

• Develop a parallax field of M*N matrix

AGENDA:

WorkflowSelect odd size template in the source image

Matching window in the destination image with conjugate entities

Computation of co-rrelation coefficients for different threshold

Repeat above steps for new template location until all positions to be matched are visited

Analyse for consistency

AGENDA:

Workflow

Location of Template:• Not to locate in the occluded areas, area with low contrast or repetitive

pattern.

Size of Template• Increase in size will increase the uniqueness of grey value but also increase

distortion.• Decrease in the size will amount to less distortion but the uniqueness of

the grey values will be decreased.

Location and size of search window• Location is important (Epipolar Line)• size should be large enough to include farthest moving template and small

enough to limit the computational cost of matching

Workflow

Acceptance Criteria•Three threshold values are used

Quality Control•Accuracy and reliability of conjugate Points.

AGENDA:

Workflow

Correlation Coefficient

Workflow

Calculation of Parallax

• P = Xleft – Xright

• Where,

• Xleft = position of certain object in left image

• Xright = position of same object in right image

Data and Software Used

Images:• Two epipolar images

• MATLAB

Image Left Image Right

Methodology:

Develop algorithm in the MATLAB

Take the input of starting point in left image, template size as well as window size in the right image.

Select the threshold values for correlation coefficient calculation. Threshold 1 = 0 ≤ r ≤ 0.5, low correlation Threshold 2 = 0.5 ≤ r ≤ 0.75, medium correlation Threshold 3 = 0.75 ≤ r ≤ 1, high correlation

Calculate and locate the correlated points in right image as well as the grid in left image.

Calculate the parallax value

Experiment Results:

Destination Image showing correlating points of different threshold values with template size 7 x 7.

Source Image with the Grid

Experiment Results:

Destination Image showing correlating points of different threshold values with template size of 21 x 21.

Source Image with the Grid

Experiment Results:

Less match points in Shadow and Homogeneous areas, because of constant grey values

Shadow Areas

Homogeneous Areas

Conclusion:

Area based matching depends upon the quality of image as well as the template size

In case of homogenous areas, occluded areas as well as shadow areas, points are not well located.

With small template size, grey value uniqueness is decreased as well as the comuptation time is increased.

With large template size, grey value uniquness is increased but so is the geometric distortion.

Computation of parallax value gives terrain height but we were unable to generate the surface.

Reference:

Schenk, T. (1999). Digital Photogrammetry. Volume I. United States Of America: Terra Science.Hannah, M. J., 1974. "Computer Matching of Areas in Stereo Images," Ph.D. Thesis, Stanford University, Computer Science Department Report STAN-CS-74-438, July, 1974.Dr. Marsha Jo Hannah ,“Digital Stereo Image Matching Techniques”http://www.e-perimetron.org/Vol_4_3/Balletti_Guerra.pdf Misganu Debella-Gilo,Andreas Kääb “Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation“Misganu Debella-Gilo,Andreas Kääb “LOCALLY ADAPTIVE TEMPLATE SIZES FOR MATCHING REPEAT IMAGES OF MASS MOVEMENTS “

Thank you for your Attention