Research On Technique Of Image Mosaic Of Multiple Model In Large View Scene

This paper mostly makes a study on image mosaic technique in large view scene based on multiple model combination.The image projection mosaic in large view scene firstly must choose an appropriate model. Multinomial model and projection model are the prevalent models in common. For the multinomial, it is easy to calculate and can get the model coefficients with linearity fitting. However, a mass of feature points with well-proportioned distributing must be extracted from images in order to achieve perfect coefficients. For the projection model, the most excellent quality of projection model is that it accords with the theory of camera imaging. Moreover, we can obtain model coefficients in need of only four accurate feature matching points. Based on such excellence, in this paper, we raise a method of multiple projection models combination to get coefficients. On account of the nonlinearity of projection model, it makes computing become more complicated, which also becomes emphasis issue of this paper. For example, sampling in scale.Although projection model requires only four feature matching points, it has a strict demand on precision of matching points. Consequently, this paper makes a general analysis about the SIFT operator which has the excellent capability, and obtains key SIFT operator parameters which satisfy the need of getting model coefficients, such as frequency of sampling in scale.Local alignment has been commonly applied in more images mosaic of large view scene, whereas it easily brings a lot of accumulating error in the integrated mosaic image. For the sake of solving such problem, based on projection model, this paper presents a new technique of image mosaic of multiple model combination in large view scene. The technique firstly obtains model coefficients of every model all together, and the object is to get the minimum total error of all models. Secondly, all coefficients will be corrected by iterative algorithm, which makes using of the relation between outer parameters of camera and model coefficients. The results of experiment prove that such algorithm is efficient and brings less accumulating error.