In this paper, a new video frame interpolation method based on image morphing for frame rate up-conversion is proposed. In this method, image features are extracted by Scale-Invariant Feature Transform in each frame, and their correspondence in two contiguous frames is then computed separately in foreground and background regions. By using the above two functions, the proposed method accurately generates interpolation frames and thus achieves frame rate up-conversion.

When the inter-slice resolution of tomographic image slices is large, it is necessary to estimate the locations and intensities of pixels, which would appear in the non-existed intermediate slices. This paper presents a new method for generating the missing medical slices from two given slices. It uses the contours of organs as the control parameters to the intensity information in the physical gaps of sequential medical slices. The Snake model is used for generating the control points required for the elastic body spline (EBS) morphing algorithm. Contour information derived from this segmentation pre-process is then further processed and used as control parameters to warp the corresponding regions in both input slices into compatible shapes. In this way, the intensity information of the interpolated intermediate slices can be derived more faithfully. In comparison with the existing intensity interpolation methods, including linear interpolation, which only considers corresponding points in a small physical neighborhood, this method warps the data images into similar shapes according to contour information to provide a more meaningful correspondence relationship.

We have developed a new disparity mapping technique for image morphing which prevents synthesized images from blurring and a fast rendering technique which realizes interactive morphing animation. In the image morphing rendering process, all pixels are moved according to their disparity maps and then distorted images are mixed with each other. Calculation costs of this process tend to be high because pixel per pixel moving and mixing are included. And if the accuracy of the disparity maps is low, synthesized images become blurred. This paper describes new two techniques for overcoming these problems. One is a disparity mapping technique by which the edges in each input image are accurately mapped to each other. This technique reduces blurring in synthesized images. The other is a data transformation technique by which the morphing rendering process is replaced with texture mapping, orthographic camera, α-brending and z-buffering. This transformation enables the morphing rendering process to be accelerated by 3D accelerators, thus enabling interactive morphing animations to be achieved on ordinary PCs.

In this paper a new snake model for image morphing with semiautomated delineation which depends on Hermite's interpolation theory, is presented. The snake model will be used to specify the correspondence between features in two given images. It allows a user to extract a contour that defines a facial feature such as the lips, mouth, and profile, by only specifying the endpoints of the contour around the feature which we wish to define. We assume that the user can specify the endpoints of a curve around the features that serve as the extremities of a contour. The proposed method automatically computes the image information around these endpoints which provides the boundary conditions. Then the contour is optimized by taking this information into account near its extremities. During the iterative optimization process, the image forces are turned on progressively from the contour extremities toward the center to define the exact position of the feature. The proposed algorithm helps the user to easily define the exact position of a feature. It may also reduce the time required to establish the features of an image.