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    An FFT-Based technique and best-first search for image registration
    (2008-12-01)
    Samritjiarapon, Olan
    ;
    Chitsobhuk, Orachat
    The image registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different time, from different sensors, or from different viewpoints. One of the major challenges related to image registration is the estimation of large motion, when input images contain small overlapped area. Common image registration using the search algorithm can accurately finds large motion but requires high computation cost due to large search space. Fourier-based technique is an alternative approach since it can rapidly achieve the registration results through its FFT algorithm. However, only Fourier-based technique cannot produce the correct results in the case of large translation. Thus, this paper presents a Fourier-based technique cooperated with best-first search algorithm to analyze the correct translation between two input images. The Fourier-based technique is used to estimate the candidate translations to decrease searching space while best-first search algorithm is used to further search for the correct translation. The proposed technique can estimate large translations, scalings, and rotations in images by an extension of well-known phase correlation technique. The experimental results using various image details show the accuracy of the proposed technique to detect large translations compared to the other techniques in frequency domain. © 2008 IEEE.
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    Image registration using Hough transform and phase correlation
    (2006-01-01)
    Chunhavittayatera, Siwaphon
    ;
    Chitsobhuk, Orachat
    ;
    Tongprasert, Kiatnarong
    This paper presents image registration using a combination of Hough transform and phase correlation technique in Fourier domain to estimate rotation and translation parameters. These paramters are used to register input images and create a seamless representation of the registered image. The first step is based on 1D phase correlation which calculates the translation of angle in Hough space, obtained form Hough transform. The rotation parameter obtain from this step is used to de-rotated the input images. The second step provides the translation in x-y axis using 2D phase correlation. Finally, the input images are registered using the estimated translation parameter. The experimental results show the accuracy of the proposed method to detect the translation parameters and find the relevant potential angles of rotation for various image details and sizes. The proposed method is ease to operation, less computation complexity, thus requires less amount of process time due to the high efficiency of fast Fourier transform (FFT).
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    Acceleration of genetic algorithm with parallel processing with application in medical image registration
    (2005-12-01)
    Laksanapanai, B.
    ;
    Withayachumnankul, W.
    ;
    Pintavirooj, C.
    ;
    Tosranon, P.
    Generally, image registration using genetic algorithm is a time-consuming process since the algorithm needs to evaluate the objective function several hundred times depending on the vastness of search space. The situation appears worse if the registration is intensity-based due to the interpolation loops prior to each objective function. However, with the availability of parallel processing method, one can accelerate the application of genetic algorithm for iterative-based image registration process of up 80 % for multi-modality alignment. Copyright UNION Agency - Science Press.
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    Multiresolution image alignment based on discrete wavelet transform
    (2005-01-01)
    Lohakan, M.
    ;
    Nantivatana, P.
    ;
    Narkbuakaew, W.
    ;
    Pintaviroj, C.
    ;
    Sangworasil, M.
    We introduce a multi-resolution image registration based on using discrete wavelet transform. We first extract contour from both images that we want to align. The extracted contours are then fitted with B-spline curve representation to synthesize the new contours with equal number of point. The area parameter is used in the B-spline fitting to make the new generated curve immune to affine transformation. Before representing the B-spline contour with discrete wavelet transform, the problem of starting point of the contour needs to be handle. This can be done by computing the maximum curvature. The maximum curvature is selected as the starting point. Once the starting points on the contour have been established, the discrete wavelet transform is then recursively represented the contours until only a few points are remained. Due to the affine-invariant properties of discrete wavelet transform, these points can be used as landmark points for registering the transformed contour with the original contour. The experiments have shown that the purposed methods are robust and promising even in the presence of noise.