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speed, several methods can be utilized. Here we use the changeable resolution algorithm to accelerate it. The key step of the changeable resolution algorithm is to reduce the resolution at first, then roughly do image registration in the lower resolution images, and then do it accurately near the possible positions in the full resolution images, finally we can get the registration results. Optical image IR image Edge extraction Edge extraction Resolution Reducing Resolution Reducing NCC Roughly registration NCC at certain positions Selecting optimal position Rotation angle estimation Image registration parameters Fig 1 Flow diagram of changeable resolution algorithm based on NCC Suppose Im O and Im IR (image size is M×N) stands for the optical and IR image respectively, f is the image zoom factor, Im Ol and Im IRl (image size is Mh×Nh) stands for the lower resolution one, accordingly, Im Oe , Im IRe , Im Ole , Im IRle are the edged images of the corresponding images, then the generalize cross correlation can be expressed as ρ ( u, v) = l ρ( k, l) = Mh Nh ∑∑ i= 0 j= 0 M N ∑∑ i= 0 j= 0 (Im (Im Oleu, v ( i, j) − ImIRle ( i, j)) σ lu, v k , l σ IR IRl Oek, l ( i, j) − ImI Re ( i, j)) σ σ Where ρ l ( u, v) is the cross correlation coefficient calculated from the lower resolution image pair Im Ole and Im IRle , (u,v) is the coordinate index of the optical image Im Ole , Im Oleu,v is an image located in (u,v)th of the image Im Ole and its size is the same as image Im IRle , σ lu,v and σ IRl are the standard deviation of the corresponding images respectively; ρ ( k, l) is the cross correlation coefficient calculated from the raw resolution image pair Im Oe and Im IRe , (k,l) is the coordinate index of the (1) (2) 28
optical image Im Oe , Im Oek,l is an image located in (k,l)th of the image Im Oe and its size is the same as image Im IRe , σ k,l and σ IR are the standard deviation of the corresponding images respectively. 3. Algorithm implementation of the changeable resolution algorithm (a) Raw optical image (b) Raw IR image Fig 2 Raw images In order to implement the algorithm, the first step is edge extraction. It is important to the results of image registration. There are many methods on edge detection and extraction, such as Prewitt, Sobel, Robert operators and Canny algorithm, and so on. As the above mentioned, the infrared image, different with the reference optical image(Fig 2), has only a lower resolution and less details, we need to reserve its edge information while edge processing. Here the edge operation is based on the Sobel operator, and it can be described as follow, ⎡1 0 −1⎤ ⎡ 1 2 1 ⎤ S = ⎢ ⎥ ⎢ 2 0 − 2 , , (3a) 1 ⎥ S ⎢ ⎥ 2 = ⎢ 0 0 0 ⎥ ⎢⎣ 1 0 −1⎥⎦ ⎢⎣ −1 − 2 −1⎥⎦ ⎡2 1 0 ⎤ ⎡ 0 1 2⎤ S = ⎢ ⎥ ⎢ 1 0 −1 , 3 ⎥ S ⎢ ⎥ (3b) 4 = ⎢ −1 0 1 ⎥ ⎢⎣ 0 −1 − 2⎥⎦ ⎢⎣ − 2 −1 0⎥⎦ Using these four operators to convolute with the raw images, we can get 2 2 ∑∑ E ( i, j) = Im( i + m −1, j + n −1)* S ( m, n) . k=1,2,3,4 k m= 0 n= 0 E( i, j) = max E ( i, j) k k k (5) Where E( i, j) is the edge of the point (i,j) of image Im. After edge detection, we can get the edged image Im Oe , Im IRe , Im Ole , Im IRle , then using NCC algorithm to do the rough registration. On account of possible disturbing, we choose some points with the former higher correlation coefficients, marked as {( u k , vk )}. When we get the candidate points{ ( u k , v , next step is to do the accurate image registration near k )} these specified points of the full resolution images. Here we adopt a weighted coefficient c to ensure that the final point with the maximum correlation coefficient is the optimal one. ρ k = c ρl ( uk , vk ) + (1 − c) ρ( u′ k , v′ k ) (6) ρ max ρ * = k k k (4) (7) 29
Page 2 and 3: The 3 rd VACPS Research Workshop Pr
Page 4 and 5: Organizing Committee Victorian Asso
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