2.  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

4.  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.

5.  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

6.  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.

7. Aligning a raw image with a real world map coordinate system. Raw Image Real World Map

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

10.  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.

12. 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.

13. 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

14.  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.

15.  Process of partitioning the image into non overlapping regions according to gray level, texture etc  Single priority queue

16.  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.

17. INPUT IMAGE DATABASE REFRENCE IMAGE Image Registration Algo

18. AFFINE TRANSFORM PIECEWISE LINEAR LWM TRANSFORM PROJECTIVETRANSFORM INPUT IMAGE

19. TransformationTime required in seconds 1.Linear conformal5.828000 2.Affine4.250000 3. Projective7.641000 4.Polynomial12.04600 5. Piecewise Linear5.828000 6. Lwm5.953000

20.  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.

21.  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.

22.  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.