ALIGNING A RAW IMAGE TO A REAL TIME COORDINATE

SYSTEM ON THE WEB

Aniket PhatakUNI: avp2110

TOPICS COVERED

Image Search Engine Results now Focus on GIS image registration The Technique and its advantages Internal working Sample Results Applicable to other areas like face

recognition etc. Future scope

IMAGE SEARCH ENGINE RESULTS NOW

CURRENT IMAGE SEARCH TECHNIQUE Image search is a complex and costly task Hence, present web search engines query

the title or the metadata of the image to get results faster.

Adversarial attack is the huge problem associated with above technique

Hence, we need to devise some algorithm that can some significant pixels in image for image comparison

Focus now is on Web-based georeferencing.

GEOGRAPHIC IMAGES ON WEB

Google Earth and corresponding maps.google.com has set high standards for all web applications and websites dealing with high resolution/high accuracy geographical feature content. It can be used using APIs.

The programming environment of Flex SDK and corresponding scripting language Actionscript v3.0 embedded in Adobe Flash CS3 has enabled the use of Google Maps library in Flash Applications.

Required for this:- High Internet speeds Geo-referencing

HOLISTIC VIEW OF PROCESS

Using Principal Component Analysis(PCA) technique, the most similar image from the database is selected.

Now some specific significant pixels named Control Point Pairs(CPPs) are selected for image registration automatically.

Next time, for image registration and georeferencing on any other server, we just need to pass these CPPs instead of whole image.

INTRODUCTION

Aligning a raw image with a real world map coordinate system.

Raw Image Real World Map

LAYERS ON IMAGES

GEO-REFERENCING PROCESS

Spatial datasets from different sources need to be accurately aligned geographically in order to be viewed or analyzed together

GEOREFERENCING PROCESS

Georeferencing is one of the vital research areas of GIS data integration literature. Geospatial information needs to be extracted from multiple sources in a very consistent and precise way. The typical Georeferencing process includes:

Identifying a set of control point pairs that link locations on a raster image with corresponding locations on a correctly positioned vector dataset.

Calculating a transformation function from a raster image to the vector map based on the Control Point Pairs (CPPs).

Transforming and re-sampling the image.

SYSTEM DIAGRAM

ISSUES

Manually Finding CPPs is

Time consuming

Tedious

Sometimes impossible

Must know a priori approximate location

Distorted and Transformed images makes it even

harder to identify the location.

SOLUTION

AUTOMATED GEO-REFERENCING

Requires no pre knowledge of the image’s placement

in the road network.

Necessitates only a few points from the image.

Tolerates point location distortion , missing points

and spurious points

Provides high performance and scalability

IMAGE ENHANCEMENT

Process by which an image is manipulated to increase the amount of information perceivable by the human eye.

Inputs: neighborhood pixels, intensity, gray level values .

Outputs: enhanced (smoothened) image . Algorithms : delta-connected components,

symmetric neighborhood filters .

IMAGE SEGMENTATION

Process of partitioning the image into non

overlapping regions according to gray level,

texture etc

Single priority queue

IMAGE REGISTRATION

Process of overlaying two or more images of the same scene taken at different times, from different from different view points.

Geometric alignment of images. Correlation function used for feature matching. Comprises of:1. Feature detection.2. Feature matching.3. Transformations.

IMAGE REGISTRATION

INPUT IMAGEDATABASE REFRENCE IMAGE

Image Registration Algo

IMAGE REGISTRATION AND TRANSFORMATIONS

AFFINE TRANSFORM PIECEWISE LINEAR

LWM TRANSFORM PROJECTIVETRANSFORM

INPUT IMAGE

Transformation Time required in seconds

1. Linear conformal 5.828000

2. Affine 4.250000

3. Projective 7.641000

4. Polynomial 12.04600

5. Piecewise Linear 5.828000

6. Lwm 5.953000

SAMPLE RESULTS

LIMITATIONS

Only spatial datasets from different sources are

considered.

A minimum of 4 control points in image is

required for matching.

Pattern matching is currently only being done on

point data.

FUTURE SCOPE

Easily extended to other image matching

applications like face recognition etc.

Natural Disaster management.

Implementing GIS Applications and Pattern

Matching for paleontological classification of

ammonitic suture.

Housing Stock surveys.

CONCLUSION

An image search engine can use this algorithm to avoid storing

various copies of same image location.

It can register images from different sources and align them

without actually comparing them pixel by pixel each time which

is time consuming and costly process.

Easily scalable architecture and more suitable for distributed

environment where network bandwidth is precious.

Removes manual human intervention and thereby any

possibility of human error in image matching.

THANK YOU