1. An Improved Approach For Image Matching Using Principle Component Analysis(PCA An Improved Approach For Image Matching Using Principle Component Analysis(PCA )

2. The Definition of Image Matching GENERAL DEFINITION Image matching can be defined as the process of bringing two images geometrically into agreement so that corresponding pixels in the two images correspond to the same physical region of the scene being imaged Image matching problem is accomplished by transforming (e.g., translating, rotating, scaling) one of the images in such a way that the similarity with the other image is maximized in some sense IMAGE MATCHING USED IN OUR PROJECT Extract small sub-image from a template image and match the exact location of the sub-image in the template image

3. Electronic map Face identification Image matching has huge applications.

4. Traditional Image Matching Algorithm (MAD 、 SAD 、 NCC 、 SSD 、 MSD) PROS Simple Understandable CONS Computation burden Sensitive to noise

5. Example : Traditional NCC Algorithm NCC algorithm is the image grayscale matching algorithm based on similarity measures R(u,v) represents normalized cross correlation coefficient of point(u,v). The bigger the value of R(u,v) is, the larger similarity between two picture is.

6. Improved Algorithm is Anti-interference and Reduce the Computation Time Greatly. Use PCA to decide approx. location Classic NCC algorithm Improved image matching algorithm Our Improved Algorithm

7. Compute the eigenvalue and eigenvector of extraced picture Sorting all eigenvalues and picking the largest P eigenvalues. Finding the corresponding eigenvectors of them, that is the reduced orthogonal vectors of original picture. Project the matching picture to the P dimensional space, get the approximate location of points in original picture. Step1: Using PCA Algorithm to Find the Approximate Location.

8. Find the center of matching point x (i, j ) ; Doing the NCC algorithm( traditional algorithm), but the searching area is much smaller than original algorithm. The reliability is much better than classical algorithm. Step 2: Using NCC Algorithm Around the Appro. Location to find the precise location.

9. Experiment 1 The left picture is the template image The right picture is the sub-image we want to match to the template image Matching?

10. Experiment 1 Results Wrong Matching Using Traditional NCC Algorithm Correct Matching Using Improved Algorithm

11. Experiment 2 Results Correct Matching Using Improved Algorithm Wrong Matching Using Traditional NCC Algorithm

12. Computation time reduces by 40%. Anti- interference. Overall ideal experiment results. Experiment Result

13. LIMITATION When the sub-image is small, the improved algorithm works well. When the sub- image become big, the improved algorithm get more wrong matching. The improved algorithm works better for sub-image which has distinct features.

14. THANK YOU!