Researches On Image Mosaic Based On Features

Image mosaic is an important research area of digital image processing recently. It is a technology that carries on the spatial matching to a series of image which are overlapped partly with each other, and finally generates a seamless, large field of view and high-resolution image by fusing these images. It has wide applications in the fields of virtual reality, computer version, remote sensing image processing, military and so on.SIFT(Scale Invariant Feature Transform) initially is as a key feature to be proposed, which is not variable in the scale ,translation and rotation in the image changes, but also has a strong robustness in illumination change, affine transformation and noise. Therefore, SIFT image registration is a field in the of research. SIFT ,which is in the field of image registration ,is of a forefront research. Extraction and matching SIFT features of images in the image mosaic is an important step.The thesis first introduces the research significance of image mosaic, the present research status at home and abroad .Then the thesis describes the theoretical basis for image mosaic: coordinate system, imaging model, the basic steps of image mosaic, image acquisition, image preprocessing, image registration,image fusion. In the image pre-processing, the paper narrates the method that gray image is converted to color image.With regard to the core process steps to image registration, the paper also introduces the four key elements, for the three classes of the common registration methods carrying on comparison and analysis and the image transformation model after registration. Compared the two kinds of familiar interpolation methods:Nearest neighbor interpolation and bilinear interpolation, and the interpolation method is used to solve the problem that image pixel coordinates after transformation is non-integer. Finally, the thesis introduces the concept of image fusion and its importance.Extract SIFT features mainly through image samples using different distance and makes a hierarchical pyramid structure. Then the pyramid is convolved with Gaussian function which has different parameter. DOG(Difference of Gaussian)is formed thorough subtracting between adjacency Gaussian pyramid. The next stage which identifies key locations is to find the extremum (maxima or minima) of difference of Gaussian function as a candidate. then we discard the feature points of low contrast and eliminate edge responses in order to obtain stable feature points. At last we define feature's orientation and describe the SIFT eigenvector. After extracting SIFT features, we use NN(Nearest Neighbor)to match features. NN uses the ratio of between the nearest neighbor point and the second nearest neighbor as the matching criterion. When we search the nearest neighbor ,we use a priority k-d tree search method, which uses a priority queue to search the nearest neighbor in the order of increasing distance from the query point and which has a high efficiency of searching.The priority k-d tree search method can avoid prolonging search times and also provides a dramatic improvement in the NN search for moderate dimensionality(e.g. 8-15).In the stage of image registration, the paper use the epipolar geometry constraint and the homography constraint to delete the wrongly matched points. The paper introduces the concept of epipolar geometry, fundamental matrix F and its basic properties, takes an example to explain random sample consensus algorithm.At first, we select sets of 7 feature correspondences, using RANSAC algorithm based on Sampson distance to discard outliers and estimate the fundamental matrix F. Secondly, we select sets of 4 feature correspondences which have been purified in the previous step. Based on comparing the registration error and distance threshold, we use RANSAC algorithm again to discard the wrongly matched points, purify the inliers, robustly estimate the transformation matrix H between two images,and with the inliers, an initial transformation parameters between any two images can be obtained .The paper also establishes a probabilistic model for image match verification, automatic identification of repeated image and noise in image sequence, retains the right match images. Finally a bundle-adjustment algorithm is applied to prevent image distortion when there are more than two source images registrating, disregarding the accumulated errors caused by concatenation of pairwise homographies. The homography of two adjacent images are updated using Levenberg-Marquardt algorithm.After image registration, we have found images that match to each other, and get the transformation between them, by connecting them we will realize image mosaic. The thesis use backward mapping and bilinear interpolation function to execute interpolation calculation in image connection. Due to the different sampling time and sampling angle, there will be differences in the degree of deformation and light intensity in the overlap region and some image edges are still visible after we stitch the images. Hence the need for image fusion is very necessary for us to build a seamless integrated image eliminating the discontinuity of light intensity or gray in images. The paper introduces three kinds of common methods of image fusion, average method,Muli-Band Blending method and overlapped area linear transition method, compares the advantages and disadvantages of each algorithm. The proposed algorithm combined with the image regristration algorithm based on SIFT feature is applied in the image mosaic by the experiment. The thesis makes an improvement on overlapped area linear transition method, which combines with light intensity compensation method and uses cosine form to adjust transition factor, solving the effect on obvious changes in light intensity, and have made satisfactory effect.In the end ,we summarize the thesis and recall the main content. In this thesis ,our method has realistic value, at the same time, we also get the perfect experiment result. The will be helpful to future research about this area. We have to research in some area in the future.