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Aniket Phatak UNI: avp2110. ALIGNING A RAW IMAGE TO A REAL TIME COORDINATE SYSTEM On THE WEB. 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.

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Aligning a raw image to a real time coordinate system on the web

Aniket Phatak

UNI: avp2110

ALIGNING A RAW IMAGE TO A REAL TIME COORDINATE SYSTEM On THE WEB


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

TOPICS COVERED



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.

Current image search technique


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

Geographic Images on web


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.

Holistic view of process


Introduction
INTRODUCTION similar image from the database is selected.

Raw Image

Real World Map

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


Layers on images
Layers On Images similar image from the database is selected.


Geo referencing process
GEO-REFERENCING Process similar image from the database is selected.

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.

Georeferencing process


System diagram
System diagram data integration literature.


Issues

Manually Finding CPPs is data integration literature.

  • Time consuming

  • Tedious

  • Sometimes impossible

  • Must know a priori approximate location

  • Distorted and Transformed images makes it even harder to identify the location.

ISSUES


Solution

AUTOMATED GEO-REFERENCING data integration literature.

  • 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

SOLUTION


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 ENHANCEMENT


Image segmentation

IMAGE SEGMENTATION


Image registration

IMAGE REGISTRATION


Image registration1
IMAGE REGISTRATION taken at different times, from different from different view points.

Image Registration Algo

DATABASE REFRENCE IMAGE

INPUT IMAGE


Image registration and transformations
IMAGE REGISTRATION AND TRANSFORMATIONS taken at different times, from different from different view points.

PIECEWISE LINEAR

AFFINE TRANSFORM

INPUT IMAGE

PROJECTIVETRANSFORM

LWM TRANSFORM


Sample results
SAMPLE RESULTS taken at different times, from different from different view points.


Limitations

LIMITATIONS


Future scope

FUTURE SCOPE


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.

CONCLUSION


Thank you
THANK YOU storing various copies of same image location.


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