Image enlargement based on a step edge model

In compiling a multimedia document we often need to enlarge the size of an image. The traditional pixel repetition method tends to make the edges jagged. On the other hand, the interpolation-based methods tend to make the edges blurry in the enlarging process. In this paper we propose an image magnification method based on a step edge model. Using the model, we are able to define a straight step edge segment with four parameters. In enlarging a digital image, we first derive the parameter values for its step edge segments. This is like extracting the step edges in the corresponding continuous image. Then we re-digitize the step edges in the continuous image with a finer grid to obtain an enlarged image. In this way, the step edges are able to stay well defined after they are enlarged. The experiments show that in both visual comparison and quantitative analysis, the results produced by the suggested step edge model-based approach are consistently and significantly better than that produced by pixel repetition and bilinear interpolation.     

Planar shape matching based on binary tree shape representation

In this paper we suggest a new way of representing planar two-dimensional shapes and a shape matching method which utilizes the new representation. Through merging of the neighboring boundary runs, a shape can be partitioned into a set of triangles. These triangles are inherently connected according to a binary tree structure. Here we use the binary tree with the triangles as its nodes to represent the shape. This representation is found to be insensitive to shape translation, rotation, scaling and skewing changes due to viewer's location changes (or the object's pose changes). Furthermore, the representation is of multiresolution.

In shape matching we compare the two trees representing two given shapes node by node according to the breadth-first tree traversing sequence. The comparison is done from top of the tree and moving downward, which means that we first compare the lower resolution approximations of the two shapes. If the two approximations are different, the comparison stops. Otherwise, it goes on and compares the finer details of the two shapes. Only when the two shapes are very similar, will the two corresponding trees be compared entirely. Thus, the matching algorithm utilizes the multiresolution characteristic of the tree representation and appears to be very efficient.