Research On Image Registration And Mosaic Based On Feature Point

Image mosaic is an important research area of computer vision, image processing and computer graphics. It can used to construct the high-resolution image with large angle of view, and has been widely used in virtual reality, medical image processing, remote sensing and military affairs. Image mosaic is the technique to stitch a series of overlapped pictures into a bigger one, which is a super-resolution picture with wide eyeshot, nearly no distortion to the originals and no visible seam line. The quality of image mosaic mainly depends on the precision of image registration. Currently, feature point based registration is a kind of image registration technique which mainly conforms to affine transformation model and perspective transformation model. It can be used not only in the less overlapped pictures registration, but also pictures with motion scenes or even with covering parts. Therefore, it has been widely used in practical applications. As a result, it is of great theoretic and practical value to do further researches in feature point based image registration and mosaic. In this dissertation, the author mainly focuses on the research of feature point matching of the Harris feature points and the SIFT feature points. The main research work in the dissertation is as follows::1. The basic geometrical principles of the camera is studied, with emphasis on the existence term of the eight-parameter perspective transform matrix and the term on how to obtain six-parameter affine matrix from the eight-parameter perspective matrix. Thereafter, several commonly used transform model in image mosaic, i.e., similar transform, affine transform and perspective transform, are deduced together with the practical application situation of each model.2. Aiming to solve the problem that traditional feature point matching algorithms are sensitive to image rotation, a new image registration scheme is presented based on rotation normalization and feature point (IPNP). The basic principles of the Harris interest point detector is studied systematically together with several Harris feature based descriptors, including the Sum of Squared Differences (SSD), Cross Correlation (CC) and Normalized Cross Correlation (NCC). The performances of the above descriptors when applied in feature point matching are evaluated. The scope of application and the deficiency for each descriptor are also analyzed. Then an image registration algorithm is proposed based on Harris interest point and image normalization. In the feature matching process, the neighboring area of each feature point is first rotation normalized. As a result, the effect of image rotation on feature matching is solved. Experimental results show that the proposed scheme can solve the rotation sensitive problem of traditional methods and improve the probability of correct matching by 30%, which is advantageous over the traditional methods.3. Aiming to solve the problem that traditional feature point matching schemes are sensitive to added noise, an image registration scheme is proposed based on feature point and invariant moment (IPIM). Image moment is a kind of method to describe the statistical features of an image. It is insensitive to the changes in noise and illumination. Besides, it is invariant to image rotation and scale change. Studies on image moment features show that ? 3 and ? 5 in the Hu moment is instable when the image is rotated. Therefore, the Hu moment is improved, and the feature descriptors are generated using the improved Hu moments, which are then used for feature matching. As a result, the problem that traditional feature matching methods are sensitive to image rotation and added noise is solved. Simulation results show that the proposed scheme performs well on rotation invariance and noise insensitiveness, and the matching effect is advantageous over traditional methods.4. The IPIM method is sensitive to image intensity change. In order to solve the problem, a new image registration method based on interest point and pseudo-Zernike moment is presented (IPPZM). As pseudo-Zernike moment has more number of independent moments and better ability to resist noise, the proposed scheme employs the pseudo-Zernike moments of the neighboring area centering at the feature point as the feature descriptor. The discrete nature of digital image results in the computation error of pseudo-Zernike moments. As a result, different moments are computed with different accuracies. In order to guarantee the accuracy of the descriptor and low computation complexity, an optimization procedure is necessary to select the pseudo-Zernike moments and apply a limit on the number of moments. Simulation results show that the proposed scheme not only solves the illumination sensitiveness problem, but also improves in regard of rotation invariance and noise resistance.5. The Harris feature point is sensitive to image scale change. To overcome this problem, an improved SIFT based image registration scheme is presented. The proposed scheme solve the above problem by using the rotation and scaling invariant property of the SIFT feature points as well as its robustness to added noise, viewpoint change and illumination change, which makes it possible to achieve successful image registration when large scale change occurs. Meantime, we have improved the SIFT feature point extraction method by reducing in advance some unstable feature points, so that the matching speed and precision of correct matching are improved. Experimental results show that the proposed scheme is invariant to rotation, scale change, and it has good robustness to noise and intensity change. Besides, the matching speed is improved by about 2 times.6. The accumulative errors in image mosaic systems have great effect on the quality of the synthesized image. Aiming to solve the problem, an overall optimization adjusting method is proposed based on feature points. If only the local image registration results are employed to align the image sequences, the shadow or fussy effect will occur in the synthesized image due to the error accumulation. The proposed scheme optimizes and adjusts the transform matrix using the correct feature point pairs obtained from the feature point based image registration, aiming to reduce the accumulative error. Experimental results show that the proposed scheme can effectively reduce the accumulative error between the image sequences and improve the quality of the synthesized image.